qualitative research analysis sample

Qualitative Data Analysis Methods 101:

The “big 6” methods + examples.

By: Kerryn Warren (PhD) | Reviewed By: Eunice Rautenbach (D.Tech) | May 2020 (Updated April 2023)

Qualitative data analysis methods. Wow, that’s a mouthful. 

If you’re new to the world of research, qualitative data analysis can look rather intimidating. So much bulky terminology and so many abstract, fluffy concepts. It certainly can be a minefield!

Don’t worry – in this post, we’ll unpack the most popular analysis methods , one at a time, so that you can approach your analysis with confidence and competence – whether that’s for a dissertation, thesis or really any kind of research project.

What (exactly) is qualitative data analysis?

To understand qualitative data analysis, we need to first understand qualitative data – so let’s step back and ask the question, “what exactly is qualitative data?”.

Qualitative data refers to pretty much any data that’s “not numbers” . In other words, it’s not the stuff you measure using a fixed scale or complex equipment, nor do you analyse it using complex statistics or mathematics.

So, if it’s not numbers, what is it?

Words, you guessed? Well… sometimes , yes. Qualitative data can, and often does, take the form of interview transcripts, documents and open-ended survey responses – but it can also involve the interpretation of images and videos. In other words, qualitative isn’t just limited to text-based data.

So, how’s that different from quantitative data, you ask?

Simply put, qualitative research focuses on words, descriptions, concepts or ideas – while quantitative research focuses on numbers and statistics . Qualitative research investigates the “softer side” of things to explore and describe , while quantitative research focuses on the “hard numbers”, to measure differences between variables and the relationships between them. If you’re keen to learn more about the differences between qual and quant, we’ve got a detailed post over here .

So, qualitative analysis is easier than quantitative, right?

Not quite. In many ways, qualitative data can be challenging and time-consuming to analyse and interpret. At the end of your data collection phase (which itself takes a lot of time), you’ll likely have many pages of text-based data or hours upon hours of audio to work through. You might also have subtle nuances of interactions or discussions that have danced around in your mind, or that you scribbled down in messy field notes. All of this needs to work its way into your analysis.

Making sense of all of this is no small task and you shouldn’t underestimate it. Long story short – qualitative analysis can be a lot of work! Of course, quantitative analysis is no piece of cake either, but it’s important to recognise that qualitative analysis still requires a significant investment in terms of time and effort.

Need a helping hand?

In this post, we’ll explore qualitative data analysis by looking at some of the most common analysis methods we encounter. We’re not going to cover every possible qualitative method and we’re not going to go into heavy detail – we’re just going to give you the big picture. That said, we will of course includes links to loads of extra resources so that you can learn more about whichever analysis method interests you.

Without further delay, let’s get into it.

The “Big 6” Qualitative Analysis Methods 

There are many different types of qualitative data analysis, all of which serve different purposes and have unique strengths and weaknesses . We’ll start by outlining the analysis methods and then we’ll dive into the details for each.

The 6 most popular methods (or at least the ones we see at Grad Coach) are:

• Content analysis
• Narrative analysis
• Discourse analysis
• Thematic analysis
• Grounded theory (GT)
• Interpretive phenomenological analysis (IPA)

Let’s take a look at each of them…

QDA Method #1: Qualitative Content Analysis

Content analysis is possibly the most common and straightforward QDA method. At the simplest level, content analysis is used to evaluate patterns within a piece of content (for example, words, phrases or images) or across multiple pieces of content or sources of communication. For example, a collection of newspaper articles or political speeches.

With content analysis, you could, for instance, identify the frequency with which an idea is shared or spoken about – like the number of times a Kardashian is mentioned on Twitter. Or you could identify patterns of deeper underlying interpretations – for instance, by identifying phrases or words in tourist pamphlets that highlight India as an ancient country.

Because content analysis can be used in such a wide variety of ways, it’s important to go into your analysis with a very specific question and goal, or you’ll get lost in the fog. With content analysis, you’ll group large amounts of text into codes , summarise these into categories, and possibly even tabulate the data to calculate the frequency of certain concepts or variables. Because of this, content analysis provides a small splash of quantitative thinking within a qualitative method.

Naturally, while content analysis is widely useful, it’s not without its drawbacks . One of the main issues with content analysis is that it can be very time-consuming , as it requires lots of reading and re-reading of the texts. Also, because of its multidimensional focus on both qualitative and quantitative aspects, it is sometimes accused of losing important nuances in communication.

Content analysis also tends to concentrate on a very specific timeline and doesn’t take into account what happened before or after that timeline. This isn’t necessarily a bad thing though – just something to be aware of. So, keep these factors in mind if you’re considering content analysis. Every analysis method has its limitations , so don’t be put off by these – just be aware of them ! If you’re interested in learning more about content analysis, the video below provides a good starting point.

QDA Method #2: Narrative Analysis 

As the name suggests, narrative analysis is all about listening to people telling stories and analysing what that means . Since stories serve a functional purpose of helping us make sense of the world, we can gain insights into the ways that people deal with and make sense of reality by analysing their stories and the ways they’re told.

You could, for example, use narrative analysis to explore whether how something is being said is important. For instance, the narrative of a prisoner trying to justify their crime could provide insight into their view of the world and the justice system. Similarly, analysing the ways entrepreneurs talk about the struggles in their careers or cancer patients telling stories of hope could provide powerful insights into their mindsets and perspectives . Simply put, narrative analysis is about paying attention to the stories that people tell – and more importantly, the way they tell them.

Of course, the narrative approach has its weaknesses , too. Sample sizes are generally quite small due to the time-consuming process of capturing narratives. Because of this, along with the multitude of social and lifestyle factors which can influence a subject, narrative analysis can be quite difficult to reproduce in subsequent research. This means that it’s difficult to test the findings of some of this research.

Similarly, researcher bias can have a strong influence on the results here, so you need to be particularly careful about the potential biases you can bring into your analysis when using this method. Nevertheless, narrative analysis is still a very useful qualitative analysis method – just keep these limitations in mind and be careful not to draw broad conclusions . If you’re keen to learn more about narrative analysis, the video below provides a great introduction to this qualitative analysis method.

QDA Method #3: Discourse Analysis 

Discourse is simply a fancy word for written or spoken language or debate . So, discourse analysis is all about analysing language within its social context. In other words, analysing language – such as a conversation, a speech, etc – within the culture and society it takes place. For example, you could analyse how a janitor speaks to a CEO, or how politicians speak about terrorism.

To truly understand these conversations or speeches, the culture and history of those involved in the communication are important factors to consider. For example, a janitor might speak more casually with a CEO in a company that emphasises equality among workers. Similarly, a politician might speak more about terrorism if there was a recent terrorist incident in the country.

So, as you can see, by using discourse analysis, you can identify how culture , history or power dynamics (to name a few) have an effect on the way concepts are spoken about. So, if your research aims and objectives involve understanding culture or power dynamics, discourse analysis can be a powerful method.

Because there are many social influences in terms of how we speak to each other, the potential use of discourse analysis is vast . Of course, this also means it’s important to have a very specific research question (or questions) in mind when analysing your data and looking for patterns and themes, or you might land up going down a winding rabbit hole.

Discourse analysis can also be very time-consuming  as you need to sample the data to the point of saturation – in other words, until no new information and insights emerge. But this is, of course, part of what makes discourse analysis such a powerful technique. So, keep these factors in mind when considering this QDA method. Again, if you’re keen to learn more, the video below presents a good starting point.

QDA Method #4: Thematic Analysis

Thematic analysis looks at patterns of meaning in a data set – for example, a set of interviews or focus group transcripts. But what exactly does that… mean? Well, a thematic analysis takes bodies of data (which are often quite large) and groups them according to similarities – in other words, themes . These themes help us make sense of the content and derive meaning from it.

Let’s take a look at an example.

With thematic analysis, you could analyse 100 online reviews of a popular sushi restaurant to find out what patrons think about the place. By reviewing the data, you would then identify the themes that crop up repeatedly within the data – for example, “fresh ingredients” or “friendly wait staff”.

So, as you can see, thematic analysis can be pretty useful for finding out about people’s experiences , views, and opinions . Therefore, if your research aims and objectives involve understanding people’s experience or view of something, thematic analysis can be a great choice.

Since thematic analysis is a bit of an exploratory process, it’s not unusual for your research questions to develop , or even change as you progress through the analysis. While this is somewhat natural in exploratory research, it can also be seen as a disadvantage as it means that data needs to be re-reviewed each time a research question is adjusted. In other words, thematic analysis can be quite time-consuming – but for a good reason. So, keep this in mind if you choose to use thematic analysis for your project and budget extra time for unexpected adjustments.

QDA Method #5: Grounded theory (GT) 

Grounded theory is a powerful qualitative analysis method where the intention is to create a new theory (or theories) using the data at hand, through a series of “ tests ” and “ revisions ”. Strictly speaking, GT is more a research design type than an analysis method, but we’ve included it here as it’s often referred to as a method.

What’s most important with grounded theory is that you go into the analysis with an open mind and let the data speak for itself – rather than dragging existing hypotheses or theories into your analysis. In other words, your analysis must develop from the ground up (hence the name). 

Let’s look at an example of GT in action.

Assume you’re interested in developing a theory about what factors influence students to watch a YouTube video about qualitative analysis. Using Grounded theory , you’d start with this general overarching question about the given population (i.e., graduate students). First, you’d approach a small sample – for example, five graduate students in a department at a university. Ideally, this sample would be reasonably representative of the broader population. You’d interview these students to identify what factors lead them to watch the video.

After analysing the interview data, a general pattern could emerge. For example, you might notice that graduate students are more likely to read a post about qualitative methods if they are just starting on their dissertation journey, or if they have an upcoming test about research methods.

From here, you’ll look for another small sample – for example, five more graduate students in a different department – and see whether this pattern holds true for them. If not, you’ll look for commonalities and adapt your theory accordingly. As this process continues, the theory would develop . As we mentioned earlier, what’s important with grounded theory is that the theory develops from the data – not from some preconceived idea.

So, what are the drawbacks of grounded theory? Well, some argue that there’s a tricky circularity to grounded theory. For it to work, in principle, you should know as little as possible regarding the research question and population, so that you reduce the bias in your interpretation. However, in many circumstances, it’s also thought to be unwise to approach a research question without knowledge of the current literature . In other words, it’s a bit of a “chicken or the egg” situation.

Regardless, grounded theory remains a popular (and powerful) option. Naturally, it’s a very useful method when you’re researching a topic that is completely new or has very little existing research about it, as it allows you to start from scratch and work your way from the ground up .

QDA Method #6:   Interpretive Phenomenological Analysis (IPA)

Interpretive. Phenomenological. Analysis. IPA . Try saying that three times fast…

Let’s just stick with IPA, okay?

IPA is designed to help you understand the personal experiences of a subject (for example, a person or group of people) concerning a major life event, an experience or a situation . This event or experience is the “phenomenon” that makes up the “P” in IPA. Such phenomena may range from relatively common events – such as motherhood, or being involved in a car accident – to those which are extremely rare – for example, someone’s personal experience in a refugee camp. So, IPA is a great choice if your research involves analysing people’s personal experiences of something that happened to them.

It’s important to remember that IPA is subject – centred . In other words, it’s focused on the experiencer . This means that, while you’ll likely use a coding system to identify commonalities, it’s important not to lose the depth of experience or meaning by trying to reduce everything to codes. Also, keep in mind that since your sample size will generally be very small with IPA, you often won’t be able to draw broad conclusions about the generalisability of your findings. But that’s okay as long as it aligns with your research aims and objectives.

Another thing to be aware of with IPA is personal bias . While researcher bias can creep into all forms of research, self-awareness is critically important with IPA, as it can have a major impact on the results. For example, a researcher who was a victim of a crime himself could insert his own feelings of frustration and anger into the way he interprets the experience of someone who was kidnapped. So, if you’re going to undertake IPA, you need to be very self-aware or you could muddy the analysis.

How to choose the right analysis method

In light of all of the qualitative analysis methods we’ve covered so far, you’re probably asking yourself the question, “ How do I choose the right one? ”

Much like all the other methodological decisions you’ll need to make, selecting the right qualitative analysis method largely depends on your research aims, objectives and questions . In other words, the best tool for the job depends on what you’re trying to build. For example:

• Perhaps your research aims to analyse the use of words and what they reveal about the intention of the storyteller and the cultural context of the time.
• Perhaps your research aims to develop an understanding of the unique personal experiences of people that have experienced a certain event, or
• Perhaps your research aims to develop insight regarding the influence of a certain culture on its members.

As you can probably see, each of these research aims are distinctly different , and therefore different analysis methods would be suitable for each one. For example, narrative analysis would likely be a good option for the first aim, while grounded theory wouldn’t be as relevant. 

It’s also important to remember that each method has its own set of strengths, weaknesses and general limitations. No single analysis method is perfect . So, depending on the nature of your research, it may make sense to adopt more than one method (this is called triangulation ). Keep in mind though that this will of course be quite time-consuming.

As we’ve seen, all of the qualitative analysis methods we’ve discussed make use of coding and theme-generating techniques, but the intent and approach of each analysis method differ quite substantially. So, it’s very important to come into your research with a clear intention before you decide which analysis method (or methods) to use.

Start by reviewing your research aims , objectives and research questions to assess what exactly you’re trying to find out – then select a qualitative analysis method that fits. Never pick a method just because you like it or have experience using it – your analysis method (or methods) must align with your broader research aims and objectives.

Let’s recap on QDA methods…

In this post, we looked at six popular qualitative data analysis methods:

• First, we looked at content analysis , a straightforward method that blends a little bit of quant into a primarily qualitative analysis.
• Then we looked at narrative analysis , which is about analysing how stories are told.
• Next up was discourse analysis – which is about analysing conversations and interactions.
• Then we moved on to thematic analysis – which is about identifying themes and patterns.
• From there, we went south with grounded theory – which is about starting from scratch with a specific question and using the data alone to build a theory in response to that question.
• And finally, we looked at IPA – which is about understanding people’s unique experiences of a phenomenon.

Of course, these aren’t the only options when it comes to qualitative data analysis, but they’re a great starting point if you’re dipping your toes into qualitative research for the first time.

If you’re still feeling a bit confused, consider our private coaching service , where we hold your hand through the research process to help you develop your best work.

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

You Might Also Like:

84 Comments

This has been very helpful. Thank you.

Thank you madam,

Thank you so much for this information

I wonder it so clear for understand and good for me. can I ask additional query?

Very insightful and useful

Good work done with clear explanations. Thank you.

Thanks so much for the write-up, it’s really good.

Thanks madam . It is very important .

thank you very good

This has been very well explained in simple language . It is useful even for a new researcher.

Great to hear that. Good luck with your qualitative data analysis, Pramod!

This is very useful information. And it was very a clear language structured presentation. Thanks a lot.

Thank you so much.

very informative sequential presentation

Precise explanation of method.

Hi, may we use 2 data analysis methods in our qualitative research?

Thanks for your comment. Most commonly, one would use one type of analysis method, but it depends on your research aims and objectives.

You explained it in very simple language, everyone can understand it. Thanks so much.

Thank you very much, this is very helpful. It has been explained in a very simple manner that even a layman understands

Thank nicely explained can I ask is Qualitative content analysis the same as thematic analysis?

Thanks for your comment. No, QCA and thematic are two different types of analysis. This article might help clarify – https://onlinelibrary.wiley.com/doi/10.1111/nhs.12048

This is my first time to come across a well explained data analysis. so helpful.

I have thoroughly enjoyed your explanation of the six qualitative analysis methods. This is very helpful. Thank you!

Thank you very much, this is well explained and useful

i need a citation of your book.

Thanks a lot , remarkable indeed, enlighting to the best

Hi Derek, What other theories/methods would you recommend when the data is a whole speech?

Keep writing useful artikel.

It is important concept about QDA and also the way to express is easily understandable, so thanks for all.

Thank you, this is well explained and very useful.

Very helpful .Thanks.

Hi there! Very well explained. Simple but very useful style of writing. Please provide the citation of the text. warm regards

The session was very helpful and insightful. Thank you

This was very helpful and insightful. Easy to read and understand

As a professional academic writer, this has been so informative and educative. Keep up the good work Grad Coach you are unmatched with quality content for sure.

Keep up the good work Grad Coach you are unmatched with quality content for sure.

Its Great and help me the most. A Million Thanks you Dr.

It is a very nice work

Very insightful. Please, which of this approach could be used for a research that one is trying to elicit students’ misconceptions in a particular concept ?

This is Amazing and well explained, thanks

great overview

What do we call a research data analysis method that one use to advise or determining the best accounting tool or techniques that should be adopted in a company.

Informative video, explained in a clear and simple way. Kudos

Waoo! I have chosen method wrong for my data analysis. But I can revise my work according to this guide. Thank you so much for this helpful lecture.

This has been very helpful. It gave me a good view of my research objectives and how to choose the best method. Thematic analysis it is.

Very helpful indeed. Thanku so much for the insight.

This was incredibly helpful.

Very helpful.

very educative

Nicely written especially for novice academic researchers like me! Thank you.

choosing a right method for a paper is always a hard job for a student, this is a useful information, but it would be more useful personally for me, if the author provide me with a little bit more information about the data analysis techniques in type of explanatory research. Can we use qualitative content analysis technique for explanatory research ? or what is the suitable data analysis method for explanatory research in social studies?

that was very helpful for me. because these details are so important to my research. thank you very much

I learnt a lot. Thank you

Relevant and Informative, thanks !

Well-planned and organized, thanks much! 🙂

I have reviewed qualitative data analysis in a simplest way possible. The content will highly be useful for developing my book on qualitative data analysis methods. Cheers!

Clear explanation on qualitative and how about Case study

This was helpful. Thank you

This was really of great assistance, it was just the right information needed. Explanation very clear and follow.

Wow, Thanks for making my life easy

This was helpful thanks .

Very helpful…. clear and written in an easily understandable manner. Thank you.

This was so helpful as it was easy to understand. I’m a new to research thank you so much.

so educative…. but Ijust want to know which method is coding of the qualitative or tallying done?

Thank you for the great content, I have learnt a lot. So helpful

precise and clear presentation with simple language and thank you for that.

very informative content, thank you.

You guys are amazing on YouTube on this platform. Your teachings are great, educative, and informative. kudos!

Brilliant Delivery. You made a complex subject seem so easy. Well done.

Beautifully explained.

Thanks a lot

Is there a video the captures the practical process of coding using automated applications?

Thanks for the comment. We don’t recommend using automated applications for coding, as they are not sufficiently accurate in our experience.

content analysis can be qualitative research?

THANK YOU VERY MUCH.

Thank you very much for such a wonderful content

do you have any material on Data collection

What a powerful explanation of the QDA methods. Thank you.

Great explanation both written and Video. i have been using of it on a day to day working of my thesis project in accounting and finance. Thank you very much for your support.

very helpful, thank you so much

Submit a Comment Cancel reply

Your email address will not be published. Required fields are marked *

Save my name, email, and website in this browser for the next time I comment.

• Print Friendly

• Skip to main content
• Skip to primary sidebar
• Skip to footer
• QuestionPro

• Solutions Industries Gaming Automotive Sports and events Education Government Travel & Hospitality Financial Services Healthcare Cannabis Technology Use Case NPS+ Communities Audience Contactless surveys Mobile LivePolls Member Experience GDPR Positive People Science 360 Feedback Surveys
• Resources Blog eBooks Survey Templates Case Studies Training Help center

Home Market Research

Qualitative Data Analysis: What is it, Methods + Examples

In a world rich with information and narrative, understanding the deeper layers of human experiences requires a unique vision that goes beyond numbers and figures. This is where the power of qualitative data analysis comes to light.

In this blog, we’ll learn about qualitative data analysis, explore its methods, and provide real-life examples showcasing its power in uncovering insights.

What is Qualitative Data Analysis?

Qualitative data analysis is a systematic process of examining non-numerical data to extract meaning, patterns, and insights.

In contrast to quantitative analysis, which focuses on numbers and statistical metrics, the qualitative study focuses on the qualitative aspects of data, such as text, images, audio, and videos. It seeks to understand every aspect of human experiences, perceptions, and behaviors by examining the data’s richness.

Companies frequently conduct this analysis on customer feedback. You can collect qualitative data from reviews, complaints, chat messages, interactions with support centers, customer interviews, case notes, or even social media comments. This kind of data holds the key to understanding customer sentiments and preferences in a way that goes beyond mere numbers.

Importance of Qualitative Data Analysis

Qualitative data analysis plays a crucial role in your research and decision-making process across various disciplines. Let’s explore some key reasons that underline the significance of this analysis:

In-Depth Understanding

It enables you to explore complex and nuanced aspects of a phenomenon, delving into the ‘how’ and ‘why’ questions. This method provides you with a deeper understanding of human behavior, experiences, and contexts that quantitative approaches might not capture fully.

Contextual Insight

You can use this analysis to give context to numerical data. It will help you understand the circumstances and conditions that influence participants’ thoughts, feelings, and actions. This contextual insight becomes essential for generating comprehensive explanations.

Theory Development

You can generate or refine hypotheses via qualitative data analysis. As you analyze the data attentively, you can form hypotheses, concepts, and frameworks that will drive your future research and contribute to theoretical advances.

Participant Perspectives

When performing qualitative research, you can highlight participant voices and opinions. This approach is especially useful for understanding marginalized or underrepresented people, as it allows them to communicate their experiences and points of view.

Exploratory Research

The analysis is frequently used at the exploratory stage of your project. It assists you in identifying important variables, developing research questions, and designing quantitative studies that will follow.

Types of Qualitative Data

When conducting qualitative research, you can use several qualitative data collection methods , and here you will come across many sorts of qualitative data that can provide you with unique insights into your study topic. These data kinds add new views and angles to your understanding and analysis.

Interviews and Focus Groups

Interviews and focus groups will be among your key methods for gathering qualitative data. Interviews are one-on-one talks in which participants can freely share their thoughts, experiences, and opinions.

Focus groups, on the other hand, are discussions in which members interact with one another, resulting in dynamic exchanges of ideas. Both methods provide rich qualitative data and direct access to participant perspectives.

Observations and Field Notes

Observations and field notes are another useful sort of qualitative data. You can immerse yourself in the research environment through direct observation, carefully documenting behaviors, interactions, and contextual factors.

These observations will be recorded in your field notes, providing a complete picture of the environment and the behaviors you’re researching. This data type is especially important for comprehending behavior in their natural setting.

Textual and Visual Data

Textual and visual data include a wide range of resources that can be qualitatively analyzed. Documents, written narratives, and transcripts from various sources, such as interviews or speeches, are examples of textual data.

Photographs, films, and even artwork provide a visual layer to your research. These forms of data allow you to investigate what is spoken and the underlying emotions, details, and symbols expressed by language or pictures.

When to Choose Qualitative Data Analysis over Quantitative Data Analysis

As you begin your research journey, understanding why the analysis of qualitative data is important will guide your approach to understanding complex events. If you analyze qualitative data, it will provide new insights that complement quantitative methodologies, which will give you a broader understanding of your study topic.

It is critical to know when to use qualitative analysis over quantitative procedures. You can prefer qualitative data analysis when:

• Complexity Reigns: When your research questions involve deep human experiences, motivations, or emotions, qualitative research excels at revealing these complexities.
• Exploration is Key: Qualitative analysis is ideal for exploratory research. It will assist you in understanding a new or poorly understood topic before formulating quantitative hypotheses.
• Context Matters: If you want to understand how context affects behaviors or results, qualitative data analysis provides the depth needed to grasp these relationships.
• Unanticipated Findings: When your study provides surprising new viewpoints or ideas, qualitative analysis helps you to delve deeply into these emerging themes.
• Subjective Interpretation is Vital: When it comes to understanding people’s subjective experiences and interpretations, qualitative data analysis is the way to go.

You can make informed decisions regarding the right approach for your research objectives if you understand the importance of qualitative analysis and recognize the situations where it shines.

Qualitative Data Analysis Methods and Examples

Exploring various qualitative data analysis methods will provide you with a wide collection for making sense of your research findings. Once the data has been collected, you can choose from several analysis methods based on your research objectives and the data type you’ve collected.

There are five main methods for analyzing qualitative data. Each method takes a distinct approach to identifying patterns, themes, and insights within your qualitative data. They are:

Method 1: Content Analysis

Content analysis is a methodical technique for analyzing textual or visual data in a structured manner. In this method, you will categorize qualitative data by splitting it into manageable pieces and assigning the manual coding process to these units.

As you go, you’ll notice ongoing codes and designs that will allow you to conclude the content. This method is very beneficial for detecting common ideas, concepts, or themes in your data without losing the context.

Steps to Do Content Analysis

Follow these steps when conducting content analysis:

• Collect and Immerse: Begin by collecting the necessary textual or visual data. Immerse yourself in this data to fully understand its content, context, and complexities.
• Assign Codes and Categories: Assign codes to relevant data sections that systematically represent major ideas or themes. Arrange comparable codes into groups that cover the major themes.
• Analyze and Interpret: Develop a structured framework from the categories and codes. Then, evaluate the data in the context of your research question, investigate relationships between categories, discover patterns, and draw meaning from these connections.

Benefits & Challenges

There are various advantages to using content analysis:

• Structured Approach: It offers a systematic approach to dealing with large data sets and ensures consistency throughout the research.
• Objective Insights: This method promotes objectivity, which helps to reduce potential biases in your study.
• Pattern Discovery: Content analysis can help uncover hidden trends, themes, and patterns that are not always obvious.
• Versatility: You can apply content analysis to various data formats, including text, internet content, images, etc.

However, keep in mind the challenges that arise:

• Subjectivity: Even with the best attempts, a certain bias may remain in coding and interpretation.
• Complexity: Analyzing huge data sets requires time and great attention to detail.
• Contextual Nuances: Content analysis may not capture all of the contextual richness that qualitative data analysis highlights.

Example of Content Analysis

Suppose you’re conducting market research and looking at customer feedback on a product. As you collect relevant data and analyze feedback, you’ll see repeating codes like “price,” “quality,” “customer service,” and “features.” These codes are organized into categories such as “positive reviews,” “negative reviews,” and “suggestions for improvement.”

According to your findings, themes such as “price” and “customer service” stand out and show that pricing and customer service greatly impact customer satisfaction. This example highlights the power of content analysis for obtaining significant insights from large textual data collections.

Method 2: Thematic Analysis

Thematic analysis is a well-structured procedure for identifying and analyzing recurring themes in your data. As you become more engaged in the data, you’ll generate codes or short labels representing key concepts. These codes are then organized into themes, providing a consistent framework for organizing and comprehending the substance of the data.

The analysis allows you to organize complex narratives and perspectives into meaningful categories, which will allow you to identify connections and patterns that may not be visible at first.

Steps to Do Thematic Analysis

Follow these steps when conducting a thematic analysis:

• Code and Group: Start by thoroughly examining the data and giving initial codes that identify the segments. To create initial themes, combine relevant codes.
• Code and Group: Begin by engaging yourself in the data, assigning first codes to notable segments. To construct basic themes, group comparable codes together.
• Analyze and Report: Analyze the data within each theme to derive relevant insights. Organize the topics into a consistent structure and explain your findings, along with data extracts that represent each theme.

Thematic analysis has various benefits:

• Structured Exploration: It is a method for identifying patterns and themes in complex qualitative data.
• Comprehensive knowledge: Thematic analysis promotes an in-depth understanding of the complications and meanings of the data.
• Application Flexibility: This method can be customized to various research situations and data kinds.

However, challenges may arise, such as:

• Interpretive Nature: Interpreting qualitative data in thematic analysis is vital, and it is critical to manage researcher bias.
• Time-consuming: The study can be time-consuming, especially with large data sets.
• Subjectivity: The selection of codes and topics might be subjective.

Example of Thematic Analysis

Assume you’re conducting a thematic analysis on job satisfaction interviews. Following your immersion in the data, you assign initial codes such as “work-life balance,” “career growth,” and “colleague relationships.” As you organize these codes, you’ll notice themes develop, such as “Factors Influencing Job Satisfaction” and “Impact on Work Engagement.”

Further investigation reveals the tales and experiences included within these themes and provides insights into how various elements influence job satisfaction. This example demonstrates how thematic analysis can reveal meaningful patterns and insights in qualitative data.

Method 3: Narrative Analysis

The narrative analysis involves the narratives that people share. You’ll investigate the histories in your data, looking at how stories are created and the meanings they express. This method is excellent for learning how people make sense of their experiences through narrative.

Steps to Do Narrative Analysis

The following steps are involved in narrative analysis:

• Gather and Analyze: Start by collecting narratives, such as first-person tales, interviews, or written accounts. Analyze the stories, focusing on the plot, feelings, and characters.
• Find Themes: Look for recurring themes or patterns in various narratives. Think about the similarities and differences between these topics and personal experiences.
• Interpret and Extract Insights: Contextualize the narratives within their larger context. Accept the subjective nature of each narrative and analyze the narrator’s voice and style. Extract insights from the tales by diving into the emotions, motivations, and implications communicated by the stories.

There are various advantages to narrative analysis:

• Deep Exploration: It lets you look deeply into people’s personal experiences and perspectives.
• Human-Centered: This method prioritizes the human perspective, allowing individuals to express themselves.

However, difficulties may arise, such as:

• Interpretive Complexity: Analyzing narratives requires dealing with the complexities of meaning and interpretation.
• Time-consuming: Because of the richness and complexities of tales, working with them can be time-consuming.

Example of Narrative Analysis

Assume you’re conducting narrative analysis on refugee interviews. As you read the stories, you’ll notice common themes of toughness, loss, and hope. The narratives provide insight into the obstacles that refugees face, their strengths, and the dreams that guide them.

The analysis can provide a deeper insight into the refugees’ experiences and the broader social context they navigate by examining the narratives’ emotional subtleties and underlying meanings. This example highlights how narrative analysis can reveal important insights into human stories.

Method 4: Grounded Theory Analysis

Grounded theory analysis is an iterative and systematic approach that allows you to create theories directly from data without being limited by pre-existing hypotheses. With an open mind, you collect data and generate early codes and labels that capture essential ideas or concepts within the data.

As you progress, you refine these codes and increasingly connect them, eventually developing a theory based on the data. Grounded theory analysis is a dynamic process for developing new insights and hypotheses based on details in your data.

Steps to Do Grounded Theory Analysis

Grounded theory analysis requires the following steps:

• Initial Coding: First, immerse yourself in the data, producing initial codes that represent major concepts or patterns.
• Categorize and Connect: Using axial coding, organize the initial codes, which establish relationships and connections between topics.
• Build the Theory: Focus on creating a core category that connects the codes and themes. Regularly refine the theory by comparing and integrating new data, ensuring that it evolves organically from the data.

Grounded theory analysis has various benefits:

• Theory Generation: It provides a one-of-a-kind opportunity to generate hypotheses straight from data and promotes new insights.
• In-depth Understanding: The analysis allows you to deeply analyze the data and reveal complex relationships and patterns.
• Flexible Process: This method is customizable and ongoing, which allows you to enhance your research as you collect additional data.

However, challenges might arise with:

• Time and Resources: Because grounded theory analysis is a continuous process, it requires a large commitment of time and resources.
• Theoretical Development: Creating a grounded theory involves a thorough understanding of qualitative data analysis software and theoretical concepts.
• Interpretation of Complexity: Interpreting and incorporating a newly developed theory into existing literature can be intellectually hard.

Example of Grounded Theory Analysis

Assume you’re performing a grounded theory analysis on workplace collaboration interviews. As you open code the data, you will discover notions such as “communication barriers,” “team dynamics,” and “leadership roles.” Axial coding demonstrates links between these notions, emphasizing the significance of efficient communication in developing collaboration.

You create the core “Integrated Communication Strategies” category through selective coding, which unifies new topics.

This theory-driven category serves as the framework for understanding how numerous aspects contribute to effective team collaboration. This example shows how grounded theory analysis allows you to generate a theory directly from the inherent nature of the data.

Method 5: Discourse Analysis

Discourse analysis focuses on language and communication. You’ll look at how language produces meaning and how it reflects power relations, identities, and cultural influences. This strategy examines what is said and how it is said; the words, phrasing, and larger context of communication.

The analysis is precious when investigating power dynamics, identities, and cultural influences encoded in language. By evaluating the language used in your data, you can identify underlying assumptions, cultural standards, and how individuals negotiate meaning through communication.

Steps to Do Discourse Analysis

Conducting discourse analysis entails the following steps:

• Select Discourse: For analysis, choose language-based data such as texts, speeches, or media content.
• Analyze Language: Immerse yourself in the conversation, examining language choices, metaphors, and underlying assumptions.
• Discover Patterns: Recognize the dialogue’s reoccurring themes, ideologies, and power dynamics. To fully understand the effects of these patterns, put them in their larger context.

There are various advantages of using discourse analysis:

• Understanding Language: It provides an extensive understanding of how language builds meaning and influences perceptions.
• Uncovering Power Dynamics: The analysis reveals how power dynamics appear via language.
• Cultural Insights: This method identifies cultural norms, beliefs, and ideologies stored in communication.

However, the following challenges may arise:

• Complexity of Interpretation: Language analysis involves navigating multiple levels of nuance and interpretation.
• Subjectivity: Interpretation can be subjective, so controlling researcher bias is important.
• Time-Intensive: Discourse analysis can take a lot of time because careful linguistic study is required in this analysis.

Example of Discourse Analysis

Consider doing discourse analysis on media coverage of a political event. You notice repeating linguistic patterns in news articles that depict the event as a conflict between opposing parties. Through deconstruction, you can expose how this framing supports particular ideologies and power relations.

You can illustrate how language choices influence public perceptions and contribute to building the narrative around the event by analyzing the speech within the broader political and social context. This example shows how discourse analysis can reveal hidden power dynamics and cultural influences on communication.

How to do Qualitative Data Analysis with the QuestionPro Research suite?

QuestionPro is a popular survey and research platform that offers tools for collecting and analyzing qualitative and quantitative data. Follow these general steps for conducting qualitative data analysis using the QuestionPro Research Suite:

• Collect Qualitative Data: Set up your survey to capture qualitative responses. It might involve open-ended questions, text boxes, or comment sections where participants can provide detailed responses.
• Export Qualitative Responses: Export the responses once you’ve collected qualitative data through your survey. QuestionPro typically allows you to export survey data in various formats, such as Excel or CSV.
• Prepare Data for Analysis: Review the exported data and clean it if necessary. Remove irrelevant or duplicate entries to ensure your data is ready for analysis.
• Code and Categorize Responses: Segment and label data, letting new patterns emerge naturally, then develop categories through axial coding to structure the analysis.
• Identify Themes: Analyze the coded responses to identify recurring themes, patterns, and insights. Look for similarities and differences in participants’ responses.
• Generate Reports and Visualizations: Utilize the reporting features of QuestionPro to create visualizations, charts, and graphs that help communicate the themes and findings from your qualitative research.
• Interpret and Draw Conclusions: Interpret the themes and patterns you’ve identified in the qualitative data. Consider how these findings answer your research questions or provide insights into your study topic.
• Integrate with Quantitative Data (if applicable): If you’re also conducting quantitative research using QuestionPro, consider integrating your qualitative findings with quantitative results to provide a more comprehensive understanding.

Qualitative data analysis is vital in uncovering various human experiences, views, and stories. If you’re ready to transform your research journey and apply the power of qualitative analysis, now is the moment to do it. Book a demo with QuestionPro today and begin your journey of exploration.

LEARN MORE         FREE TRIAL

MORE LIKE THIS

Customer Communication Tool: Types, Methods, Uses, & Tools

Apr 23, 2024

Top 12 Sentiment Analysis Tools for Understanding Emotions

QuestionPro BI: From research data to actionable dashboards within minutes

Apr 22, 2024

21 Best Customer Experience Management Software in 2024

Other categories.

• Academic Research
• Artificial Intelligence
• Assessments
• Brand Awareness
• Case Studies
• Communities
• Consumer Insights
• Customer effort score
• Customer Engagement
• Customer Experience
• Customer Loyalty
• Customer Research
• Customer Satisfaction
• Employee Benefits
• Employee Engagement
• Employee Retention
• Friday Five
• General Data Protection Regulation
• Insights Hub
• Life@QuestionPro
• Market Research
• Mobile diaries
• Mobile Surveys
• New Features
• Online Communities
• Question Types
• Questionnaire
• QuestionPro Products
• Release Notes
• Research Tools and Apps
• Revenue at Risk
• Survey Templates
• Training Tips
• Uncategorized
• Video Learning Series
• What’s Coming Up
• Workforce Intelligence

Learn / Guides / Qualitative data analysis guide

Back to guides

6 qualitative data analysis examples to inspire you

Qualitative data analysis is complex, and without seeing examples of successful QDA in action, it can seem like an overwhelming, time-consuming process. 

But the value of QDA—the customer insights and ideas you'll uncover—makes the process worth it, and you might be surprised at how efficient (and even fun!) some QDA methods can be.

Last updated

Reading time.

When you think about data, you probably think quantitative first: facts, figures, and numbers. You can line them up neatly in a spreadsheet, and suddenly they just make sense. 

You know qualitative data is crucial too , but how do you organize and interpret all those words, emotions, and motivations once you collect them? 

This guide looks at six qualitative data analysis examples from companies that got real results. For each one, we look at the type of analysis used and how it played a role in the company’s success—so you can walk away with exciting new techniques to try.

Find clarity on what customers want

Hotjar's product experience insights help teams collect qualitative data so you can deliver a better customer experience.

Get inspired with 6 qualitative data analysis examples 

All companies can benefit from qualitative data analysis to better understand their customers. The question is: which QDA methods are the most effective? 

Qualitative data analysis isn't a one-size-fits-all process —different teams can benefit from different qualitative data analysis types. For example, you might be looking for ways to analyze product reviews, while another team might be trying to make sense of thousands of survey responses.

Sometimes a glimpse into the successful processes of other companies can help you pick up new tricks of your own. Here are six qualitative data analysis examples to inspire you to improve your own process:

1. Art.com 

Art.com is an ecommerce company selling art prints. Their 100% happiness guarantee—they’ll issue a full refund, no questions asked—shows their commitment to putting customers first. But to be proactive—so you can create a delightful customer experience from the start —it helps to collect and analyze data to see what people really want and need.

Their approach to qualitative data analysis

Art.com used Net Promoter Score® (NPS) surveys to ask customers to rate, and then comment in their own words, whether they'd recommend the company to friends or colleagues. 

Collecting the data was one thing, but analyzing it was another. One person was tasked with combing through spreadsheets of insights, using the program’s 'search' function to manually find key words and phrases.

Art.com wanted a Natural Language Processing (NLP) solution to analyze the data for them, so they turned to a tool called Thematic , which allowed them to automatically find and sort survey responses by customized themes. (Note: this qualitative data analysis type is simply called—you guessed it—thematic analysis.)

One Thematic feature essential to Art.com’s analysis was the ability to see how customers' feelings about the company, their products, and the buying experience impacted the bottom line. In other words, the tool allowed them to chart qualitative data alongside quantitative performance data to make actionable changes.

Thematic’s Impact tool

But analysis doesn’t have to be done in a silo. Remember how Art.com had one person poring over data all alone? Thematic enabled the company to create a plan for sharing the responsibility for data analysis. Now Art.com has Team Consumer Leaders: team members who take ownership of the analysis processes each month.

Qualitative data analysis for the win

The results: Art.com spent less time manually combing through data, and shifted the load from one person to a whole team of analysts through data democratization . Plus, they gained a better understanding of customers’ feelings and reactions from NPS surveys, because they could analyze the impact the results had on business performance. 

If this was your company: automatically classifying feedback into categories or themes makes it easier to base decisions on qualitative data versus just a hunch. Follow Art.com's example of using QDA to make customer-centric product decisions and deliver a better user experience.

Pro tip: use Hotjar Net Promoter Score® (NPS) Surveys to create and customize surveys to give your customers. 

In addition to simple rating scales, Hotjar's NPS surveys let you ask short follow-up questions, to gain additional context in the voice of the customer (VoC). You can put these surveys directly on your website, or email them to your customer list. 

With NPS surveys, you can gather valuable insights about what your customers are really thinking —and analyze the responses to find ways to improve their experience.

A household name in the UK, Matalan offers savings on family goods at over 200 retail locations. When they migrated to a new website, their big question was: how can we provide the same smooth experience online we’re known for in-store?

To find the answer to that question, Matalan turned to Hotjar (that’s us! 👋). The user experience team at Matalan started by using our Survey tool to check in with customers, to see what they thought of the new site. Then they dug into a couple of other Hotjar tools for added context—for example, they found that pairing the Feedback widget with Session Recordings was the eye-opening combo they needed:

Hotjar really empowers you to be able to see exactly what your users are doing, how they’re feeling and ultimately their reactions to the changes you make. Without Hotjar we would still be making decisions based on gut instinct instead of qualitative user feedback.

But the Matalan team didn’t stop there. They built a custom dashboard in Google Data Studio as a home base for analyzing their results. When they integrated their Feedback results with Google Data Studio, they could conduct qualitative analysis using the same method we mentioned above: thematic analysis. Organizing the information by theme helped the team spot trends that they could use to inform website changes to A/B test.

The results: after using Hotjar to create hypotheses about customer behavior, Matalan’s success rate in split testing for the website went up by 17%. Then, by adding Google Data Studio into the picture, they could dig even deeper into the analytical process. They also found this was a great way to get more eyes on data within the company—and open the lines of communication across teams.

If this was your company: qualitative data analysis can help create clarity around the real user experience, and can help you make customer-centric design decisions to reduce friction for website visitors.

Pro tip : want to follow Matalan’s lead? 

Hotjar has a step-by-step process for open-ended question analysis . Read our tutorial to learn how to export survey results into Google Sheets—we’ve even included a template to get you started.

Yatter is an agency that helps businesses generate more pay-per-click leads so they can scale and grow. Gavin Bell, Yatter’s founder, helps optimize his clients’ (and his own) social media ads and landing pages to drive traffic and make sales. 

Yatter's approach to qualitative data analysis

Gavin’s style of analysis fits squarely into one of the qualitative data analysis types called diagnostic or root-cause analysis. Essentially, this method investigates why people make decisions by looking for outliers or patterns in data, and can be used for both qualitative and quantitative research.

For their qualitative data analysis, Yatter leans heavily on Hotjar Recordings to understand the user experience on websites—and make improvements accordingly. Gavin’s tip? Always watch five recordings of a customer interacting with a site before making any changes to it . 

On one website he was working on for an ecommerce store for car parts, Gavin knew that users left during the checkout process, and wanted to understand why. He watched user after user get confused during checkout, and click on the menu icon instead. As a result, Gavin decided to remove the menu button from that page.

On his personal site, watching recordings helped Gavin realize that leads spent a long time coming up with a user name to enter in a form. Seeing this behavior led Gavin to auto-fill the form with users’ emails, saving them several seconds in the process and improving their journey.

Hotjar lets you go granular and really understand the individuals using the page. In other words, it turns data into life.

The results: by watching session recordings, Gavin could spot even the smallest bugs and stumbling blocks and find solutions. For example, Yatter increased conversions for one client by 20%just by removing the menu button from the checkout page. For his own page, Gavin was happy to have saved time for visitors, knowing that satisfied leads and customers are the ones that stick around. 

If this was your company: in addition to driving sales, qualitative data analysis provides you with empathetic insights into who customers are, why they do what they do, and what they need to be happy , so you can make the right changes at the right time to create customer delight .

4. WatchShop

An independent retailer based in the UK, WatchShop specializes in selling brand-name and luxury watches directly to the consumer (also known as business-to-consumer, or B2C). The company created its first ecommerce website back in 2007, and continuously makes changes and improvements to the site. WatchShop's goals? To help more leads find the site and optimize their CX.

WatchShop already knew the value of behavioral data—which is why they watched Hotjar Session Recordings. 😉 But they needed help understanding the qualitative insights they were collecting, so explored a QDA method called sentiment analysis. 

Sentiment analysis focuses on emotion in textual data from surveys, reviews, emails, and other sources. Put simply, sentiment analysis helps you understand how customers feel—and why they feel that way. 

WatchShop selected Lumoa , an artificial intelligence-based tool, to help streamline all their text-based data sources. The software then produced an overall customer sentiment score, which functions as a key performance indicator (KPI) that all stakeholders can monitor.

When their customer sentiment score substantially dropped or increased at any point, WatchShop used QDA to understand why . Then, they tasked the appropriate teams to fix the negatives, and take advantage of the positives.

Since Lumoa can integrate with other platforms, WatchShop connected it with TrustPilot, a ratings site, to analyze customer reviews. WatchShop also uses Lumoa to analyze competitors’ reviews, to look at how other brands are perceived—and to figure out what they can learn from their peers.

The results: for one of their clients, WatchShop hoped to improve Product Listing Pages. Using sentiment analysis, the company uncovered issues in the customer journey they hadn’t noticed before, and used their learnings to develop ideas for website changes. In the first round of tests, the company’s conversion rate improved by 4%, and after the second round, conversion rates increased by 10%. 

If this was your company: using a QDA tool like Lumoa helps teams centralize the analytics process, so you can quickly interpret large volumes of qualitative data. Sorting this data also helps you prioritize initiatives based on which issues are most important to your customers.

5. Materials Market

Materials Market does just what their name promises: facilitates trade between construction customers and the suppliers that have the materials they need. The UK-based ecommerce company wants their website to run as smoothly as possible for customers—so they turned to Hotjar for help .

Qualitative data analysis doesn’t have to be fancy to be effective. Andrew Haehn, one of the founders of Materials Market and the Operations Director, takes a simple approach.

Over breakfast every morning, Andrew watches 20 minutes of Hotjar Recordings , carefully observing how users interact with the site. While he eats, he analyzes what’s going well and what needs improvement. 

Why this approach works: consistency . By watching recordings each day, Andrew becomes familiar with users’ standard behaviors—and more attuned to what might be throwing them off track. 

To be even more effective, Andrew sorts recordings by relevance: Hotjar’s algorithm helps him find the most valuable recordings—those marked 'high' or 'very high'—to help him prioritize his time.

One tip from Andrew is to analyze qualitative data alongside quantitative data —from Hotjar’s Heatmaps , for example, which visually depict the most and least popular areas of a web page—to spot areas of confusion and verify user experience issues.

Qualitative data analysis for the win 

The results: Materials Market used Hotjar to collect and analyze qualitative data—and quickly discovered ways to improve the customer experience. Some of the company’s impressive results after watching recordings included: 

A decrease in cart abandonment rate from 25% to 4%

An increase in conversion rate of paying customers from 0.5% to 1.6% (in a single month)

An increase of more than £10,000 in revenue (due to the improved conversion rate)

If this was your company: qualitative data analysis complements quantitative data analysis to help minimize customers' frustrations and maximize profits. Setting time limits and sorting recordings by relevance keeps the analytical process quick and painless.

MURAL , a company offering digital whiteboard solutions, specializes in creative and collaborative problem solving. So, it’s only natural that they used the same techniques in their approach to qualitative data analysis.

MURAL has been refining their qualitative data analysis skills for years, using different methods along the way . Eventually, as the company grew, it sought out a centralized hub for analyzing customer feedback and other insights. 

MURAL, under co-founder and Head of Product Augustin Soler, turned to EnjoyHQ as their platform of choice. EnjoyHQ helped the company collate qualitative data, generate metrics from that data, and conduct thematic analysis. 

As a team that craves data visualization, they export results from EnjoyHQ onto a MURAL whiteboard so they can arrange information to spark discussion and collaboration. Then they use qualitative data analysis as part of their planning process: product teams can home in on a particular feature they plan to update or release down the road, analyze results for that feature, and use it to inform their work.

The results: EnjoyHQ helped MURAL shape their qualitative data analysis process—now they can analyze customer feedback in a more structured way, leading to improved communication and collaboration.

If this was your company: collecting and analyzing qualitative data is vital to optimizing product decisions. Don't be afraid to try new qualitative data analysis methods —or to customize solutions to meet your specific needs.

Pro tip: personalized communication shows customers you care, which can improve brand loyalty and trust. 

For example, when MURAL releases new features, they follow up by sending emails to the people who requested them. Customers then know the company was listening and is taking action to meet their specific needs.

Find ways to make your qualitative data work for you

The qualitative data analysis examples on this page show the clear results that come from focusing on customer insights.  

Qualitative data amplifies the success you're already achieving from crunching numbers in quantitative analysis. By using new types of qualitative data analysis in your team’s processes, you can stop relying on your gut—and instead make data-backed, user-centric product decisions.

FAQs about qualitative data analysis

What are some examples of qualitative data analysis.

Qualitative data analysis examples include taking a closer look at results from surveys, online reviews, website recordings, emails, interviews, and other text sources by using tools and methods like:

Thematic analysis with tools like Thematic.com and EnjoyHQ

Sentiment analysis with tools like Lumoa

Root-cause analysis with tools like Hotjar

What are the types of qualitative data analysis?

There are many qualitative data analysis types to explore. Some types include: 

Root-cause analysis: exploring patterns in data to find answers

Thematic analysis: looking for common themes that emerge

Sentiment analysis: exploring what people feel and why

Narrative analysis: examining the stories people tell 

How can you get started with qualitative data analysis?

You don’t have to make big investments with time or money to start qualitative data analysis. All you need to get started are a free Hotjar account to collect product experience insights, and a few minutes a day to watch session recordings and review survey and feedback responses. Look for trends or patterns that stand out, and consider why users behave the way they do. What might they be thinking or feeling?

What are the benefits of qualitative data analysis?

Qualitative data analysis can have many benefits for a company, by helping stakeholders think about their product, website, and customers in new ways. Some specific advantages include:

Building customer empathy

Improving customer acquisition and retention

Boosting engagement 

Recognizing confusion about messaging

Improving website experiences

How do qualitative and quantitative data analysis work together?

Qualitative and quantitative data analysis go hand in hand. Quantitative is a good starting point to find out what is happening in your business, but qualitative helps you figure out why . Using the two together can help you understand how customers’ thoughts, feelings, and behaviors are driving key financial metrics in your business.

QDA challenges

Previous chapter

Next chapter

• Privacy Policy

Home » Qualitative Research – Methods, Analysis Types and Guide

Qualitative Research – Methods, Analysis Types and Guide

Table of Contents

Qualitative Research

Qualitative research is a type of research methodology that focuses on exploring and understanding people’s beliefs, attitudes, behaviors, and experiences through the collection and analysis of non-numerical data. It seeks to answer research questions through the examination of subjective data, such as interviews, focus groups, observations, and textual analysis.

Qualitative research aims to uncover the meaning and significance of social phenomena, and it typically involves a more flexible and iterative approach to data collection and analysis compared to quantitative research. Qualitative research is often used in fields such as sociology, anthropology, psychology, and education.

Qualitative Research Methods

Qualitative Research Methods are as follows:

One-to-One Interview

This method involves conducting an interview with a single participant to gain a detailed understanding of their experiences, attitudes, and beliefs. One-to-one interviews can be conducted in-person, over the phone, or through video conferencing. The interviewer typically uses open-ended questions to encourage the participant to share their thoughts and feelings. One-to-one interviews are useful for gaining detailed insights into individual experiences.

Focus Groups

This method involves bringing together a group of people to discuss a specific topic in a structured setting. The focus group is led by a moderator who guides the discussion and encourages participants to share their thoughts and opinions. Focus groups are useful for generating ideas and insights, exploring social norms and attitudes, and understanding group dynamics.

Ethnographic Studies

This method involves immersing oneself in a culture or community to gain a deep understanding of its norms, beliefs, and practices. Ethnographic studies typically involve long-term fieldwork and observation, as well as interviews and document analysis. Ethnographic studies are useful for understanding the cultural context of social phenomena and for gaining a holistic understanding of complex social processes.

Text Analysis

This method involves analyzing written or spoken language to identify patterns and themes. Text analysis can be quantitative or qualitative. Qualitative text analysis involves close reading and interpretation of texts to identify recurring themes, concepts, and patterns. Text analysis is useful for understanding media messages, public discourse, and cultural trends.

This method involves an in-depth examination of a single person, group, or event to gain an understanding of complex phenomena. Case studies typically involve a combination of data collection methods, such as interviews, observations, and document analysis, to provide a comprehensive understanding of the case. Case studies are useful for exploring unique or rare cases, and for generating hypotheses for further research.

Process of Observation

This method involves systematically observing and recording behaviors and interactions in natural settings. The observer may take notes, use audio or video recordings, or use other methods to document what they see. Process of observation is useful for understanding social interactions, cultural practices, and the context in which behaviors occur.

Record Keeping

This method involves keeping detailed records of observations, interviews, and other data collected during the research process. Record keeping is essential for ensuring the accuracy and reliability of the data, and for providing a basis for analysis and interpretation.

This method involves collecting data from a large sample of participants through a structured questionnaire. Surveys can be conducted in person, over the phone, through mail, or online. Surveys are useful for collecting data on attitudes, beliefs, and behaviors, and for identifying patterns and trends in a population.

Qualitative data analysis is a process of turning unstructured data into meaningful insights. It involves extracting and organizing information from sources like interviews, focus groups, and surveys. The goal is to understand people’s attitudes, behaviors, and motivations

Qualitative Research Analysis Methods

Qualitative Research analysis methods involve a systematic approach to interpreting and making sense of the data collected in qualitative research. Here are some common qualitative data analysis methods:

Thematic Analysis

This method involves identifying patterns or themes in the data that are relevant to the research question. The researcher reviews the data, identifies keywords or phrases, and groups them into categories or themes. Thematic analysis is useful for identifying patterns across multiple data sources and for generating new insights into the research topic.

Content Analysis

This method involves analyzing the content of written or spoken language to identify key themes or concepts. Content analysis can be quantitative or qualitative. Qualitative content analysis involves close reading and interpretation of texts to identify recurring themes, concepts, and patterns. Content analysis is useful for identifying patterns in media messages, public discourse, and cultural trends.

Discourse Analysis

This method involves analyzing language to understand how it constructs meaning and shapes social interactions. Discourse analysis can involve a variety of methods, such as conversation analysis, critical discourse analysis, and narrative analysis. Discourse analysis is useful for understanding how language shapes social interactions, cultural norms, and power relationships.

Grounded Theory Analysis

This method involves developing a theory or explanation based on the data collected. Grounded theory analysis starts with the data and uses an iterative process of coding and analysis to identify patterns and themes in the data. The theory or explanation that emerges is grounded in the data, rather than preconceived hypotheses. Grounded theory analysis is useful for understanding complex social phenomena and for generating new theoretical insights.

Narrative Analysis

This method involves analyzing the stories or narratives that participants share to gain insights into their experiences, attitudes, and beliefs. Narrative analysis can involve a variety of methods, such as structural analysis, thematic analysis, and discourse analysis. Narrative analysis is useful for understanding how individuals construct their identities, make sense of their experiences, and communicate their values and beliefs.

Phenomenological Analysis

This method involves analyzing how individuals make sense of their experiences and the meanings they attach to them. Phenomenological analysis typically involves in-depth interviews with participants to explore their experiences in detail. Phenomenological analysis is useful for understanding subjective experiences and for developing a rich understanding of human consciousness.

Comparative Analysis

This method involves comparing and contrasting data across different cases or groups to identify similarities and differences. Comparative analysis can be used to identify patterns or themes that are common across multiple cases, as well as to identify unique or distinctive features of individual cases. Comparative analysis is useful for understanding how social phenomena vary across different contexts and groups.

Applications of Qualitative Research

Qualitative research has many applications across different fields and industries. Here are some examples of how qualitative research is used:

• Market Research: Qualitative research is often used in market research to understand consumer attitudes, behaviors, and preferences. Researchers conduct focus groups and one-on-one interviews with consumers to gather insights into their experiences and perceptions of products and services.
• Health Care: Qualitative research is used in health care to explore patient experiences and perspectives on health and illness. Researchers conduct in-depth interviews with patients and their families to gather information on their experiences with different health care providers and treatments.
• Education: Qualitative research is used in education to understand student experiences and to develop effective teaching strategies. Researchers conduct classroom observations and interviews with students and teachers to gather insights into classroom dynamics and instructional practices.
• Social Work : Qualitative research is used in social work to explore social problems and to develop interventions to address them. Researchers conduct in-depth interviews with individuals and families to understand their experiences with poverty, discrimination, and other social problems.
• Anthropology : Qualitative research is used in anthropology to understand different cultures and societies. Researchers conduct ethnographic studies and observe and interview members of different cultural groups to gain insights into their beliefs, practices, and social structures.
• Psychology : Qualitative research is used in psychology to understand human behavior and mental processes. Researchers conduct in-depth interviews with individuals to explore their thoughts, feelings, and experiences.
• Public Policy : Qualitative research is used in public policy to explore public attitudes and to inform policy decisions. Researchers conduct focus groups and one-on-one interviews with members of the public to gather insights into their perspectives on different policy issues.

How to Conduct Qualitative Research

Here are some general steps for conducting qualitative research:

• Identify your research question: Qualitative research starts with a research question or set of questions that you want to explore. This question should be focused and specific, but also broad enough to allow for exploration and discovery.
• Select your research design: There are different types of qualitative research designs, including ethnography, case study, grounded theory, and phenomenology. You should select a design that aligns with your research question and that will allow you to gather the data you need to answer your research question.
• Recruit participants: Once you have your research question and design, you need to recruit participants. The number of participants you need will depend on your research design and the scope of your research. You can recruit participants through advertisements, social media, or through personal networks.
• Collect data: There are different methods for collecting qualitative data, including interviews, focus groups, observation, and document analysis. You should select the method or methods that align with your research design and that will allow you to gather the data you need to answer your research question.
• Analyze data: Once you have collected your data, you need to analyze it. This involves reviewing your data, identifying patterns and themes, and developing codes to organize your data. You can use different software programs to help you analyze your data, or you can do it manually.
• Interpret data: Once you have analyzed your data, you need to interpret it. This involves making sense of the patterns and themes you have identified, and developing insights and conclusions that answer your research question. You should be guided by your research question and use your data to support your conclusions.
• Communicate results: Once you have interpreted your data, you need to communicate your results. This can be done through academic papers, presentations, or reports. You should be clear and concise in your communication, and use examples and quotes from your data to support your findings.

Examples of Qualitative Research

Here are some real-time examples of qualitative research:

• Customer Feedback: A company may conduct qualitative research to understand the feedback and experiences of its customers. This may involve conducting focus groups or one-on-one interviews with customers to gather insights into their attitudes, behaviors, and preferences.
• Healthcare : A healthcare provider may conduct qualitative research to explore patient experiences and perspectives on health and illness. This may involve conducting in-depth interviews with patients and their families to gather information on their experiences with different health care providers and treatments.
• Education : An educational institution may conduct qualitative research to understand student experiences and to develop effective teaching strategies. This may involve conducting classroom observations and interviews with students and teachers to gather insights into classroom dynamics and instructional practices.
• Social Work: A social worker may conduct qualitative research to explore social problems and to develop interventions to address them. This may involve conducting in-depth interviews with individuals and families to understand their experiences with poverty, discrimination, and other social problems.
• Anthropology : An anthropologist may conduct qualitative research to understand different cultures and societies. This may involve conducting ethnographic studies and observing and interviewing members of different cultural groups to gain insights into their beliefs, practices, and social structures.
• Psychology : A psychologist may conduct qualitative research to understand human behavior and mental processes. This may involve conducting in-depth interviews with individuals to explore their thoughts, feelings, and experiences.
• Public Policy: A government agency or non-profit organization may conduct qualitative research to explore public attitudes and to inform policy decisions. This may involve conducting focus groups and one-on-one interviews with members of the public to gather insights into their perspectives on different policy issues.

Purpose of Qualitative Research

The purpose of qualitative research is to explore and understand the subjective experiences, behaviors, and perspectives of individuals or groups in a particular context. Unlike quantitative research, which focuses on numerical data and statistical analysis, qualitative research aims to provide in-depth, descriptive information that can help researchers develop insights and theories about complex social phenomena.

Qualitative research can serve multiple purposes, including:

• Exploring new or emerging phenomena : Qualitative research can be useful for exploring new or emerging phenomena, such as new technologies or social trends. This type of research can help researchers develop a deeper understanding of these phenomena and identify potential areas for further study.
• Understanding complex social phenomena : Qualitative research can be useful for exploring complex social phenomena, such as cultural beliefs, social norms, or political processes. This type of research can help researchers develop a more nuanced understanding of these phenomena and identify factors that may influence them.
• Generating new theories or hypotheses: Qualitative research can be useful for generating new theories or hypotheses about social phenomena. By gathering rich, detailed data about individuals’ experiences and perspectives, researchers can develop insights that may challenge existing theories or lead to new lines of inquiry.
• Providing context for quantitative data: Qualitative research can be useful for providing context for quantitative data. By gathering qualitative data alongside quantitative data, researchers can develop a more complete understanding of complex social phenomena and identify potential explanations for quantitative findings.

When to use Qualitative Research

Here are some situations where qualitative research may be appropriate:

• Exploring a new area: If little is known about a particular topic, qualitative research can help to identify key issues, generate hypotheses, and develop new theories.
• Understanding complex phenomena: Qualitative research can be used to investigate complex social, cultural, or organizational phenomena that are difficult to measure quantitatively.
• Investigating subjective experiences: Qualitative research is particularly useful for investigating the subjective experiences of individuals or groups, such as their attitudes, beliefs, values, or emotions.
• Conducting formative research: Qualitative research can be used in the early stages of a research project to develop research questions, identify potential research participants, and refine research methods.
• Evaluating interventions or programs: Qualitative research can be used to evaluate the effectiveness of interventions or programs by collecting data on participants’ experiences, attitudes, and behaviors.

Characteristics of Qualitative Research

Qualitative research is characterized by several key features, including:

• Focus on subjective experience: Qualitative research is concerned with understanding the subjective experiences, beliefs, and perspectives of individuals or groups in a particular context. Researchers aim to explore the meanings that people attach to their experiences and to understand the social and cultural factors that shape these meanings.
• Use of open-ended questions: Qualitative research relies on open-ended questions that allow participants to provide detailed, in-depth responses. Researchers seek to elicit rich, descriptive data that can provide insights into participants’ experiences and perspectives.
• Sampling-based on purpose and diversity: Qualitative research often involves purposive sampling, in which participants are selected based on specific criteria related to the research question. Researchers may also seek to include participants with diverse experiences and perspectives to capture a range of viewpoints.
• Data collection through multiple methods: Qualitative research typically involves the use of multiple data collection methods, such as in-depth interviews, focus groups, and observation. This allows researchers to gather rich, detailed data from multiple sources, which can provide a more complete picture of participants’ experiences and perspectives.
• Inductive data analysis: Qualitative research relies on inductive data analysis, in which researchers develop theories and insights based on the data rather than testing pre-existing hypotheses. Researchers use coding and thematic analysis to identify patterns and themes in the data and to develop theories and explanations based on these patterns.
• Emphasis on researcher reflexivity: Qualitative research recognizes the importance of the researcher’s role in shaping the research process and outcomes. Researchers are encouraged to reflect on their own biases and assumptions and to be transparent about their role in the research process.

Advantages of Qualitative Research

Qualitative research offers several advantages over other research methods, including:

• Depth and detail: Qualitative research allows researchers to gather rich, detailed data that provides a deeper understanding of complex social phenomena. Through in-depth interviews, focus groups, and observation, researchers can gather detailed information about participants’ experiences and perspectives that may be missed by other research methods.
• Flexibility : Qualitative research is a flexible approach that allows researchers to adapt their methods to the research question and context. Researchers can adjust their research methods in real-time to gather more information or explore unexpected findings.
• Contextual understanding: Qualitative research is well-suited to exploring the social and cultural context in which individuals or groups are situated. Researchers can gather information about cultural norms, social structures, and historical events that may influence participants’ experiences and perspectives.
• Participant perspective : Qualitative research prioritizes the perspective of participants, allowing researchers to explore subjective experiences and understand the meanings that participants attach to their experiences.
• Theory development: Qualitative research can contribute to the development of new theories and insights about complex social phenomena. By gathering rich, detailed data and using inductive data analysis, researchers can develop new theories and explanations that may challenge existing understandings.
• Validity : Qualitative research can offer high validity by using multiple data collection methods, purposive and diverse sampling, and researcher reflexivity. This can help ensure that findings are credible and trustworthy.

Limitations of Qualitative Research

Qualitative research also has some limitations, including:

• Subjectivity : Qualitative research relies on the subjective interpretation of researchers, which can introduce bias into the research process. The researcher’s perspective, beliefs, and experiences can influence the way data is collected, analyzed, and interpreted.
• Limited generalizability: Qualitative research typically involves small, purposive samples that may not be representative of larger populations. This limits the generalizability of findings to other contexts or populations.
• Time-consuming: Qualitative research can be a time-consuming process, requiring significant resources for data collection, analysis, and interpretation.
• Resource-intensive: Qualitative research may require more resources than other research methods, including specialized training for researchers, specialized software for data analysis, and transcription services.
• Limited reliability: Qualitative research may be less reliable than quantitative research, as it relies on the subjective interpretation of researchers. This can make it difficult to replicate findings or compare results across different studies.
• Ethics and confidentiality: Qualitative research involves collecting sensitive information from participants, which raises ethical concerns about confidentiality and informed consent. Researchers must take care to protect the privacy and confidentiality of participants and obtain informed consent.

Also see Research Methods

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

You may also like

Questionnaire – Definition, Types, and Examples

Case Study – Methods, Examples and Guide

Observational Research – Methods and Guide

Quantitative Research – Methods, Types and...

Qualitative Research Methods

Explanatory Research – Types, Methods, Guide

• AI & NLP
• Churn & Loyalty
• Customer Experience
• Customer Journeys
• Customer Metrics
• Feedback Analysis
• Product Experience
• Product Updates
• Sentiment Analysis
• Surveys & Feedback Collection
• Try Thematic

Welcome to the community

Qualitative Data Analysis: Step-by-Step Guide (Manual vs. Automatic)

When we conduct qualitative methods of research, need to explain changes in metrics or understand people's opinions, we always turn to qualitative data. Qualitative data is typically generated through:

• Interview transcripts
• Surveys with open-ended questions
• Contact center transcripts
• Texts and documents
• Audio and video recordings
• Observational notes

Compared to quantitative data, which captures structured information, qualitative data is unstructured and has more depth. It can answer our questions, can help formulate hypotheses and build understanding.

It's important to understand the differences between quantitative data & qualitative data . But unfortunately, analyzing qualitative data is difficult. While tools like Excel, Tableau and PowerBI crunch and visualize quantitative data with ease, there are a limited number of mainstream tools for analyzing qualitative data . The majority of qualitative data analysis still happens manually.

That said, there are two new trends that are changing this. First, there are advances in natural language processing (NLP) which is focused on understanding human language. Second, there is an explosion of user-friendly software designed for both researchers and businesses. Both help automate the qualitative data analysis process.

In this post we want to teach you how to conduct a successful qualitative data analysis. There are two primary qualitative data analysis methods; manual & automatic. We will teach you how to conduct the analysis manually, and also, automatically using software solutions powered by NLP. We’ll guide you through the steps to conduct a manual analysis, and look at what is involved and the role technology can play in automating this process.

More businesses are switching to fully-automated analysis of qualitative customer data because it is cheaper, faster, and just as accurate. Primarily, businesses purchase subscriptions to feedback analytics platforms so that they can understand customer pain points and sentiment.

We’ll take you through 5 steps to conduct a successful qualitative data analysis. Within each step we will highlight the key difference between the manual, and automated approach of qualitative researchers. Here's an overview of the steps:

The 5 steps to doing qualitative data analysis

• Gathering and collecting your qualitative data
• Organizing and connecting into your qualitative data
• Coding your qualitative data
• Analyzing the qualitative data for insights
• Reporting on the insights derived from your analysis

What is Qualitative Data Analysis?

Qualitative data analysis is a process of gathering, structuring and interpreting qualitative data to understand what it represents.

Qualitative data is non-numerical and unstructured. Qualitative data generally refers to text, such as open-ended responses to survey questions or user interviews, but also includes audio, photos and video.

Businesses often perform qualitative data analysis on customer feedback. And within this context, qualitative data generally refers to verbatim text data collected from sources such as reviews, complaints, chat messages, support centre interactions, customer interviews, case notes or social media comments.

How is qualitative data analysis different from quantitative data analysis?

Understanding the differences between quantitative & qualitative data is important. When it comes to analyzing data, Qualitative Data Analysis serves a very different role to Quantitative Data Analysis. But what sets them apart?

Qualitative Data Analysis dives into the stories hidden in non-numerical data such as interviews, open-ended survey answers, or notes from observations. It uncovers the ‘whys’ and ‘hows’ giving a deep understanding of people’s experiences and emotions.

Quantitative Data Analysis on the other hand deals with numerical data, using statistics to measure differences, identify preferred options, and pinpoint root causes of issues.  It steps back to address questions like "how many" or "what percentage" to offer broad insights we can apply to larger groups.

In short, Qualitative Data Analysis is like a microscope,  helping us understand specific detail. Quantitative Data Analysis is like the telescope, giving us a broader perspective. Both are important, working together to decode data for different objectives.

Qualitative Data Analysis methods

Once all the data has been captured, there are a variety of analysis techniques available and the choice is determined by your specific research objectives and the kind of data you’ve gathered.  Common qualitative data analysis methods include:

Content Analysis

This is a popular approach to qualitative data analysis. Other qualitative analysis techniques may fit within the broad scope of content analysis. Thematic analysis is a part of the content analysis.  Content analysis is used to identify the patterns that emerge from text, by grouping content into words, concepts, and themes. Content analysis is useful to quantify the relationship between all of the grouped content. The Columbia School of Public Health has a detailed breakdown of content analysis .

Narrative Analysis

Narrative analysis focuses on the stories people tell and the language they use to make sense of them.  It is particularly useful in qualitative research methods where customer stories are used to get a deep understanding of customers’ perspectives on a specific issue. A narrative analysis might enable us to summarize the outcomes of a focused case study.

Discourse Analysis

Discourse analysis is used to get a thorough understanding of the political, cultural and power dynamics that exist in specific situations.  The focus of discourse analysis here is on the way people express themselves in different social contexts. Discourse analysis is commonly used by brand strategists who hope to understand why a group of people feel the way they do about a brand or product.

Thematic Analysis

Thematic analysis is used to deduce the meaning behind the words people use. This is accomplished by discovering repeating themes in text. These meaningful themes reveal key insights into data and can be quantified, particularly when paired with sentiment analysis . Often, the outcome of thematic analysis is a code frame that captures themes in terms of codes, also called categories. So the process of thematic analysis is also referred to as “coding”. A common use-case for thematic analysis in companies is analysis of customer feedback.

Grounded Theory

Grounded theory is a useful approach when little is known about a subject. Grounded theory starts by formulating a theory around a single data case. This means that the theory is “grounded”. Grounded theory analysis is based on actual data, and not entirely speculative. Then additional cases can be examined to see if they are relevant and can add to the original grounded theory.

Challenges of Qualitative Data Analysis

While Qualitative Data Analysis offers rich insights, it comes with its challenges. Each unique QDA method has its unique hurdles. Let’s take a look at the challenges researchers and analysts might face, depending on the chosen method.

• Time and Effort (Narrative Analysis): Narrative analysis, which focuses on personal stories, demands patience. Sifting through lengthy narratives to find meaningful insights can be time-consuming, requires dedicated effort.
• Being Objective (Grounded Theory): Grounded theory, building theories from data, faces the challenges of personal biases. Staying objective while interpreting data is crucial, ensuring conclusions are rooted in the data itself.
• Complexity (Thematic Analysis): Thematic analysis involves identifying themes within data, a process that can be intricate. Categorizing and understanding themes can be complex, especially when each piece of data varies in context and structure. Thematic Analysis software can simplify this process.
• Generalizing Findings (Narrative Analysis): Narrative analysis, dealing with individual stories, makes drawing broad challenging. Extending findings from a single narrative to a broader context requires careful consideration.
• Managing Data (Thematic Analysis): Thematic analysis involves organizing and managing vast amounts of unstructured data, like interview transcripts. Managing this can be a hefty task, requiring effective data management strategies.
• Skill Level (Grounded Theory): Grounded theory demands specific skills to build theories from the ground up. Finding or training analysts with these skills poses a challenge, requiring investment in building expertise.

Benefits of qualitative data analysis

Qualitative Data Analysis (QDA) is like a versatile toolkit, offering a tailored approach to understanding your data. The benefits it offers are as diverse as the methods. Let’s explore why choosing the right method matters.

• Tailored Methods for Specific Needs: QDA isn't one-size-fits-all. Depending on your research objectives and the type of data at hand, different methods offer unique benefits. If you want emotive customer stories, narrative analysis paints a strong picture. When you want to explain a score, thematic analysis reveals insightful patterns
• Flexibility with Thematic Analysis: thematic analysis is like a chameleon in the toolkit of QDA. It adapts well to different types of data and research objectives, making it a top choice for any qualitative analysis.
• Deeper Understanding, Better Products: QDA helps you dive into people's thoughts and feelings. This deep understanding helps you build products and services that truly matches what people want, ensuring satisfied customers
• Finding the Unexpected: Qualitative data often reveals surprises that we miss in quantitative data. QDA offers us new ideas and perspectives, for insights we might otherwise miss.
• Building Effective Strategies: Insights from QDA are like strategic guides. They help businesses in crafting plans that match people’s desires.
• Creating Genuine Connections: Understanding people’s experiences lets businesses connect on a real level. This genuine connection helps build trust and loyalty, priceless for any business.

How to do Qualitative Data Analysis: 5 steps

Now we are going to show how you can do your own qualitative data analysis. We will guide you through this process step by step. As mentioned earlier, you will learn how to do qualitative data analysis manually , and also automatically using modern qualitative data and thematic analysis software.

To get best value from the analysis process and research process, it’s important to be super clear about the nature and scope of the question that’s being researched. This will help you select the research collection channels that are most likely to help you answer your question.

Depending on if you are a business looking to understand customer sentiment, or an academic surveying a school, your approach to qualitative data analysis will be unique.

Once you’re clear, there’s a sequence to follow. And, though there are differences in the manual and automatic approaches, the process steps are mostly the same.

The use case for our step-by-step guide is a company looking to collect data (customer feedback data), and analyze the customer feedback - in order to improve customer experience. By analyzing the customer feedback the company derives insights about their business and their customers. You can follow these same steps regardless of the nature of your research. Let’s get started.

Step 1: Gather your qualitative data and conduct research (Conduct qualitative research)

The first step of qualitative research is to do data collection. Put simply, data collection is gathering all of your data for analysis. A common situation is when qualitative data is spread across various sources.

Classic methods of gathering qualitative data

Most companies use traditional methods for gathering qualitative data: conducting interviews with research participants, running surveys, and running focus groups. This data is typically stored in documents, CRMs, databases and knowledge bases. It’s important to examine which data is available and needs to be included in your research project, based on its scope.

Using your existing qualitative feedback

As it becomes easier for customers to engage across a range of different channels, companies are gathering increasingly large amounts of both solicited and unsolicited qualitative feedback.

Most organizations have now invested in Voice of Customer programs , support ticketing systems, chatbot and support conversations, emails and even customer Slack chats.

These new channels provide companies with new ways of getting feedback, and also allow the collection of unstructured feedback data at scale.

The great thing about this data is that it contains a wealth of valubale insights and that it’s already there! When you have a new question about user behavior or your customers, you don’t need to create a new research study or set up a focus group. You can find most answers in the data you already have.

Typically, this data is stored in third-party solutions or a central database, but there are ways to export it or connect to a feedback analysis solution through integrations or an API.

Utilize untapped qualitative data channels

There are many online qualitative data sources you may not have considered. For example, you can find useful qualitative data in social media channels like Twitter or Facebook. Online forums, review sites, and online communities such as Discourse or Reddit also contain valuable data about your customers, or research questions.

If you are considering performing a qualitative benchmark analysis against competitors - the internet is your best friend. Gathering feedback in competitor reviews on sites like Trustpilot, G2, Capterra, Better Business Bureau or on app stores is a great way to perform a competitor benchmark analysis.

Customer feedback analysis software often has integrations into social media and review sites, or you could use a solution like DataMiner to scrape the reviews.

Step 2: Connect & organize all your qualitative data

Now you all have this qualitative data but there’s a problem, the data is unstructured. Before feedback can be analyzed and assigned any value, it needs to be organized in a single place. Why is this important? Consistency!

If all data is easily accessible in one place and analyzed in a consistent manner, you will have an easier time summarizing and making decisions based on this data.

The manual approach to organizing your data

The classic method of structuring qualitative data is to plot all the raw data you’ve gathered into a spreadsheet.

Typically, research and support teams would share large Excel sheets and different business units would make sense of the qualitative feedback data on their own. Each team collects and organizes the data in a way that best suits them, which means the feedback tends to be kept in separate silos.

An alternative and a more robust solution is to store feedback in a central database, like Snowflake or Amazon Redshift .

Keep in mind that when you organize your data in this way, you are often preparing it to be imported into another software. If you go the route of a database, you would need to use an API to push the feedback into a third-party software.

Computer-assisted qualitative data analysis software (CAQDAS)

Traditionally within the manual analysis approach (but not always), qualitative data is imported into CAQDAS software for coding.

In the early 2000s, CAQDAS software was popularised by developers such as ATLAS.ti, NVivo and MAXQDA and eagerly adopted by researchers to assist with the organizing and coding of data.  

The benefits of using computer-assisted qualitative data analysis software:

• Assists in the organizing of your data
• Opens you up to exploring different interpretations of your data analysis
• Allows you to share your dataset easier and allows group collaboration (allows for secondary analysis)

However you still need to code the data, uncover the themes and do the analysis yourself. Therefore it is still a manual approach.

Organizing your qualitative data in a feedback repository

Another solution to organizing your qualitative data is to upload it into a feedback repository where it can be unified with your other data , and easily searchable and taggable. There are a number of software solutions that act as a central repository for your qualitative research data. Here are a couple solutions that you could investigate:  

• Dovetail: Dovetail is a research repository with a focus on video and audio transcriptions. You can tag your transcriptions within the platform for theme analysis. You can also upload your other qualitative data such as research reports, survey responses, support conversations, and customer interviews. Dovetail acts as a single, searchable repository. And makes it easier to collaborate with other people around your qualitative research.
• EnjoyHQ: EnjoyHQ is another research repository with similar functionality to Dovetail. It boasts a more sophisticated search engine, but it has a higher starting subscription cost.

Organizing your qualitative data in a feedback analytics platform

If you have a lot of qualitative customer or employee feedback, from the likes of customer surveys or employee surveys, you will benefit from a feedback analytics platform. A feedback analytics platform is a software that automates the process of both sentiment analysis and thematic analysis . Companies use the integrations offered by these platforms to directly tap into their qualitative data sources (review sites, social media, survey responses, etc.). The data collected is then organized and analyzed consistently within the platform.

If you have data prepared in a spreadsheet, it can also be imported into feedback analytics platforms.

Once all this rich data has been organized within the feedback analytics platform, it is ready to be coded and themed, within the same platform. Thematic is a feedback analytics platform that offers one of the largest libraries of integrations with qualitative data sources.

Step 3: Coding your qualitative data

Your feedback data is now organized in one place. Either within your spreadsheet, CAQDAS, feedback repository or within your feedback analytics platform. The next step is to code your feedback data so we can extract meaningful insights in the next step.

Coding is the process of labelling and organizing your data in such a way that you can then identify themes in the data, and the relationships between these themes.

To simplify the coding process, you will take small samples of your customer feedback data, come up with a set of codes, or categories capturing themes, and label each piece of feedback, systematically, for patterns and meaning. Then you will take a larger sample of data, revising and refining the codes for greater accuracy and consistency as you go.

If you choose to use a feedback analytics platform, much of this process will be automated and accomplished for you.

The terms to describe different categories of meaning (‘theme’, ‘code’, ‘tag’, ‘category’ etc) can be confusing as they are often used interchangeably.  For clarity, this article will use the term ‘code’.

To code means to identify key words or phrases and assign them to a category of meaning. “I really hate the customer service of this computer software company” would be coded as “poor customer service”.

How to manually code your qualitative data

• Decide whether you will use deductive or inductive coding. Deductive coding is when you create a list of predefined codes, and then assign them to the qualitative data. Inductive coding is the opposite of this, you create codes based on the data itself. Codes arise directly from the data and you label them as you go. You need to weigh up the pros and cons of each coding method and select the most appropriate.
• Read through the feedback data to get a broad sense of what it reveals. Now it’s time to start assigning your first set of codes to statements and sections of text.
• Keep repeating step 2, adding new codes and revising the code description as often as necessary.  Once it has all been coded, go through everything again, to be sure there are no inconsistencies and that nothing has been overlooked.
• Create a code frame to group your codes. The coding frame is the organizational structure of all your codes. And there are two commonly used types of coding frames, flat, or hierarchical. A hierarchical code frame will make it easier for you to derive insights from your analysis.
• Based on the number of times a particular code occurs, you can now see the common themes in your feedback data. This is insightful! If ‘bad customer service’ is a common code, it’s time to take action.

We have a detailed guide dedicated to manually coding your qualitative data .

Using software to speed up manual coding of qualitative data

An Excel spreadsheet is still a popular method for coding. But various software solutions can help speed up this process. Here are some examples.

• CAQDAS / NVivo - CAQDAS software has built-in functionality that allows you to code text within their software. You may find the interface the software offers easier for managing codes than a spreadsheet.
• Dovetail/EnjoyHQ - You can tag transcripts and other textual data within these solutions. As they are also repositories you may find it simpler to keep the coding in one platform.
• IBM SPSS - SPSS is a statistical analysis software that may make coding easier than in a spreadsheet.
• Ascribe - Ascribe’s ‘Coder’ is a coding management system. Its user interface will make it easier for you to manage your codes.

Automating the qualitative coding process using thematic analysis software

In solutions which speed up the manual coding process, you still have to come up with valid codes and often apply codes manually to pieces of feedback. But there are also solutions that automate both the discovery and the application of codes.

Advances in machine learning have now made it possible to read, code and structure qualitative data automatically. This type of automated coding is offered by thematic analysis software .

Automation makes it far simpler and faster to code the feedback and group it into themes. By incorporating natural language processing (NLP) into the software, the AI looks across sentences and phrases to identify common themes meaningful statements. Some automated solutions detect repeating patterns and assign codes to them, others make you train the AI by providing examples. You could say that the AI learns the meaning of the feedback on its own.

Thematic automates the coding of qualitative feedback regardless of source. There’s no need to set up themes or categories in advance. Simply upload your data and wait a few minutes. You can also manually edit the codes to further refine their accuracy.  Experiments conducted indicate that Thematic’s automated coding is just as accurate as manual coding .

Paired with sentiment analysis and advanced text analytics - these automated solutions become powerful for deriving quality business or research insights.

You could also build your own , if you have the resources!

The key benefits of using an automated coding solution

Automated analysis can often be set up fast and there’s the potential to uncover things that would never have been revealed if you had given the software a prescribed list of themes to look for.

Because the model applies a consistent rule to the data, it captures phrases or statements that a human eye might have missed.

Complete and consistent analysis of customer feedback enables more meaningful findings. Leading us into step 4.

Step 4: Analyze your data: Find meaningful insights

Now we are going to analyze our data to find insights. This is where we start to answer our research questions. Keep in mind that step 4 and step 5 (tell the story) have some overlap . This is because creating visualizations is both part of analysis process and reporting.

The task of uncovering insights is to scour through the codes that emerge from the data and draw meaningful correlations from them. It is also about making sure each insight is distinct and has enough data to support it.

Part of the analysis is to establish how much each code relates to different demographics and customer profiles, and identify whether there’s any relationship between these data points.

Manually create sub-codes to improve the quality of insights

If your code frame only has one level, you may find that your codes are too broad to be able to extract meaningful insights. This is where it is valuable to create sub-codes to your primary codes. This process is sometimes referred to as meta coding.

Note: If you take an inductive coding approach, you can create sub-codes as you are reading through your feedback data and coding it.

While time-consuming, this exercise will improve the quality of your analysis. Here is an example of what sub-codes could look like.

You need to carefully read your qualitative data to create quality sub-codes. But as you can see, the depth of analysis is greatly improved. By calculating the frequency of these sub-codes you can get insight into which  customer service problems you can immediately address.

Correlate the frequency of codes to customer segments

Many businesses use customer segmentation . And you may have your own respondent segments that you can apply to your qualitative analysis. Segmentation is the practise of dividing customers or research respondents into subgroups.

Segments can be based on:

• Demographic
• And any other data type that you care to segment by

It is particularly useful to see the occurrence of codes within your segments. If one of your customer segments is considered unimportant to your business, but they are the cause of nearly all customer service complaints, it may be in your best interest to focus attention elsewhere. This is a useful insight!

Manually visualizing coded qualitative data

There are formulas you can use to visualize key insights in your data. The formulas we will suggest are imperative if you are measuring a score alongside your feedback.

If you are collecting a metric alongside your qualitative data this is a key visualization. Impact answers the question: “What’s the impact of a code on my overall score?”. Using Net Promoter Score (NPS) as an example, first you need to:

• Calculate overall NPS
• Calculate NPS in the subset of responses that do not contain that theme
• Subtract B from A

Then you can use this simple formula to calculate code impact on NPS .

You can then visualize this data using a bar chart.

You can download our CX toolkit - it includes a template to recreate this.

Trends over time

This analysis can help you answer questions like: “Which codes are linked to decreases or increases in my score over time?”

We need to compare two sequences of numbers: NPS over time and code frequency over time . Using Excel, calculate the correlation between the two sequences, which can be either positive (the more codes the higher the NPS, see picture below), or negative (the more codes the lower the NPS).

Now you need to plot code frequency against the absolute value of code correlation with NPS. Here is the formula:

The visualization could look like this:

These are two examples, but there are more. For a third manual formula, and to learn why word clouds are not an insightful form of analysis, read our visualizations article .

Using a text analytics solution to automate analysis

Automated text analytics solutions enable codes and sub-codes to be pulled out of the data automatically. This makes it far faster and easier to identify what’s driving negative or positive results. And to pick up emerging trends and find all manner of rich insights in the data.

Another benefit of AI-driven text analytics software is its built-in capability for sentiment analysis, which provides the emotive context behind your feedback and other qualitative textual data therein.

Thematic provides text analytics that goes further by allowing users to apply their expertise on business context to edit or augment the AI-generated outputs.

Since the move away from manual research is generally about reducing the human element, adding human input to the technology might sound counter-intuitive. However, this is mostly to make sure important business nuances in the feedback aren’t missed during coding. The result is a higher accuracy of analysis. This is sometimes referred to as augmented intelligence .

Step 5: Report on your data: Tell the story

The last step of analyzing your qualitative data is to report on it, to tell the story. At this point, the codes are fully developed and the focus is on communicating the narrative to the audience.

A coherent outline of the qualitative research, the findings and the insights is vital for stakeholders to discuss and debate before they can devise a meaningful course of action.

Creating graphs and reporting in Powerpoint

Typically, qualitative researchers take the tried and tested approach of distilling their report into a series of charts, tables and other visuals which are woven into a narrative for presentation in Powerpoint.

Using visualization software for reporting

With data transformation and APIs, the analyzed data can be shared with data visualisation software, such as Power BI or Tableau , Google Studio or Looker. Power BI and Tableau are among the most preferred options.

Visualizing your insights inside a feedback analytics platform

Feedback analytics platforms, like Thematic, incorporate visualisation tools that intuitively turn key data and insights into graphs.  This removes the time consuming work of constructing charts to visually identify patterns and creates more time to focus on building a compelling narrative that highlights the insights, in bite-size chunks, for executive teams to review.

Using a feedback analytics platform with visualization tools means you don’t have to use a separate product for visualizations. You can export graphs into Powerpoints straight from the platforms.

Conclusion - Manual or Automated?

There are those who remain deeply invested in the manual approach - because it’s familiar, because they’re reluctant to spend money and time learning new software, or because they’ve been burned by the overpromises of AI.  

For projects that involve small datasets, manual analysis makes sense. For example, if the objective is simply to quantify a simple question like “Do customers prefer X concepts to Y?”. If the findings are being extracted from a small set of focus groups and interviews, sometimes it’s easier to just read them

However, as new generations come into the workplace, it’s technology-driven solutions that feel more comfortable and practical. And the merits are undeniable.  Especially if the objective is to go deeper and understand the ‘why’ behind customers’ preference for X or Y. And even more especially if time and money are considerations.

The ability to collect a free flow of qualitative feedback data at the same time as the metric means AI can cost-effectively scan, crunch, score and analyze a ton of feedback from one system in one go. And time-intensive processes like focus groups, or coding, that used to take weeks, can now be completed in a matter of hours or days.

But aside from the ever-present business case to speed things up and keep costs down, there are also powerful research imperatives for automated analysis of qualitative data: namely, accuracy and consistency.

Finding insights hidden in feedback requires consistency, especially in coding.  Not to mention catching all the ‘unknown unknowns’ that can skew research findings and steering clear of cognitive bias.

Some say without manual data analysis researchers won’t get an accurate “feel” for the insights. However, the larger data sets are, the harder it is to sort through the feedback and organize feedback that has been pulled from different places.  And, the more difficult it is to stay on course, the greater the risk of drawing incorrect, or incomplete, conclusions grows.

Though the process steps for qualitative data analysis have remained pretty much unchanged since psychologist Paul Felix Lazarsfeld paved the path a hundred years ago, the impact digital technology has had on types of qualitative feedback data and the approach to the analysis are profound.  

If you want to try an automated feedback analysis solution on your own qualitative data, you can get started with Thematic .

Community & Marketing

Tyler manages our community of CX, insights & analytics professionals. Tyler's goal is to help unite insights professionals around common challenges.

We make it easy to discover the customer and product issues that matter.

Unlock the value of feedback at scale, in one platform. Try it for free now!

• Questions to ask your Feedback Analytics vendor
• How to end customer churn for good
• Scalable analysis of NPS verbatims
• 5 Text analytics approaches
• How to calculate the ROI of CX

Our experts will show you how Thematic works, how to discover pain points and track the ROI of decisions. To access your free trial, book a personal demo today.

Recent posts

Watercare is New Zealand's largest water and wastewater service provider. They are responsible for bringing clean water to 1.7 million people in Tamaki Makaurau (Auckland) and safeguarding the wastewater network to minimize impact on the environment. Water is a sector that often gets taken for granted, with drainage and

Become a qualitative theming pro! Creating a perfect code frame is hard, but thematic analysis software makes the process much easier.

Qualtrics is one of the most well-known and powerful Customer Feedback Management platforms. But even so, it has limitations. We recently hosted a live panel where data analysts from two well-known brands shared their experiences with Qualtrics, and how they extended this platform’s capabilities. Below, we’ll share the

Chapter 1. Introduction

“Science is in danger, and for that reason it is becoming dangerous” -Pierre Bourdieu, Science of Science and Reflexivity

Why an Open Access Textbook on Qualitative Research Methods?

I have been teaching qualitative research methods to both undergraduates and graduate students for many years.  Although there are some excellent textbooks out there, they are often costly, and none of them, to my mind, properly introduces qualitative research methods to the beginning student (whether undergraduate or graduate student).  In contrast, this open-access textbook is designed as a (free) true introduction to the subject, with helpful, practical pointers on how to conduct research and how to access more advanced instruction.  

Textbooks are typically arranged in one of two ways: (1) by technique (each chapter covers one method used in qualitative research); or (2) by process (chapters advance from research design through publication).  But both of these approaches are necessary for the beginner student.  This textbook will have sections dedicated to the process as well as the techniques of qualitative research.  This is a true “comprehensive” book for the beginning student.  In addition to covering techniques of data collection and data analysis, it provides a road map of how to get started and how to keep going and where to go for advanced instruction.  It covers aspects of research design and research communication as well as methods employed.  Along the way, it includes examples from many different disciplines in the social sciences.

The primary goal has been to create a useful, accessible, engaging textbook for use across many disciplines.  And, let’s face it.  Textbooks can be boring.  I hope readers find this to be a little different.  I have tried to write in a practical and forthright manner, with many lively examples and references to good and intellectually creative qualitative research.  Woven throughout the text are short textual asides (in colored textboxes) by professional (academic) qualitative researchers in various disciplines.  These short accounts by practitioners should help inspire students.  So, let’s begin!

What is Research?

When we use the word research , what exactly do we mean by that?  This is one of those words that everyone thinks they understand, but it is worth beginning this textbook with a short explanation.  We use the term to refer to “empirical research,” which is actually a historically specific approach to understanding the world around us.  Think about how you know things about the world. [1] You might know your mother loves you because she’s told you she does.  Or because that is what “mothers” do by tradition.  Or you might know because you’ve looked for evidence that she does, like taking care of you when you are sick or reading to you in bed or working two jobs so you can have the things you need to do OK in life.  Maybe it seems churlish to look for evidence; you just take it “on faith” that you are loved.

Only one of the above comes close to what we mean by research.  Empirical research is research (investigation) based on evidence.  Conclusions can then be drawn from observable data.  This observable data can also be “tested” or checked.  If the data cannot be tested, that is a good indication that we are not doing research.  Note that we can never “prove” conclusively, through observable data, that our mothers love us.  We might have some “disconfirming evidence” (that time she didn’t show up to your graduation, for example) that could push you to question an original hypothesis , but no amount of “confirming evidence” will ever allow us to say with 100% certainty, “my mother loves me.”  Faith and tradition and authority work differently.  Our knowledge can be 100% certain using each of those alternative methods of knowledge, but our certainty in those cases will not be based on facts or evidence.

For many periods of history, those in power have been nervous about “science” because it uses evidence and facts as the primary source of understanding the world, and facts can be at odds with what power or authority or tradition want you to believe.  That is why I say that scientific empirical research is a historically specific approach to understand the world.  You are in college or university now partly to learn how to engage in this historically specific approach.

In the sixteenth and seventeenth centuries in Europe, there was a newfound respect for empirical research, some of which was seriously challenging to the established church.  Using observations and testing them, scientists found that the earth was not at the center of the universe, for example, but rather that it was but one planet of many which circled the sun. [2]   For the next two centuries, the science of astronomy, physics, biology, and chemistry emerged and became disciplines taught in universities.  All used the scientific method of observation and testing to advance knowledge.  Knowledge about people , however, and social institutions, however, was still left to faith, tradition, and authority.  Historians and philosophers and poets wrote about the human condition, but none of them used research to do so. [3]

It was not until the nineteenth century that “social science” really emerged, using the scientific method (empirical observation) to understand people and social institutions.  New fields of sociology, economics, political science, and anthropology emerged.  The first sociologists, people like Auguste Comte and Karl Marx, sought specifically to apply the scientific method of research to understand society, Engels famously claiming that Marx had done for the social world what Darwin did for the natural world, tracings its laws of development.  Today we tend to take for granted the naturalness of science here, but it is actually a pretty recent and radical development.

To return to the question, “does your mother love you?”  Well, this is actually not really how a researcher would frame the question, as it is too specific to your case.  It doesn’t tell us much about the world at large, even if it does tell us something about you and your relationship with your mother.  A social science researcher might ask, “do mothers love their children?”  Or maybe they would be more interested in how this loving relationship might change over time (e.g., “do mothers love their children more now than they did in the 18th century when so many children died before reaching adulthood?”) or perhaps they might be interested in measuring quality of love across cultures or time periods, or even establishing “what love looks like” using the mother/child relationship as a site of exploration.  All of these make good research questions because we can use observable data to answer them.

What is Qualitative Research?

“All we know is how to learn. How to study, how to listen, how to talk, how to tell.  If we don’t tell the world, we don’t know the world.  We’re lost in it, we die.” -Ursula LeGuin, The Telling

At its simplest, qualitative research is research about the social world that does not use numbers in its analyses.  All those who fear statistics can breathe a sigh of relief – there are no mathematical formulae or regression models in this book! But this definition is less about what qualitative research can be and more about what it is not.  To be honest, any simple statement will fail to capture the power and depth of qualitative research.  One way of contrasting qualitative research to quantitative research is to note that the focus of qualitative research is less about explaining and predicting relationships between variables and more about understanding the social world.  To use our mother love example, the question about “what love looks like” is a good question for the qualitative researcher while all questions measuring love or comparing incidences of love (both of which require measurement) are good questions for quantitative researchers. Patton writes,

Qualitative data describe.  They take us, as readers, into the time and place of the observation so that we know what it was like to have been there.  They capture and communicate someone else’s experience of the world in his or her own words.  Qualitative data tell a story. ( Patton 2002:47 )

Qualitative researchers are asking different questions about the world than their quantitative colleagues.  Even when researchers are employed in “mixed methods” research ( both quantitative and qualitative), they are using different methods to address different questions of the study.  I do a lot of research about first-generation and working-college college students.  Where a quantitative researcher might ask, how many first-generation college students graduate from college within four years? Or does first-generation college status predict high student debt loads?  A qualitative researcher might ask, how does the college experience differ for first-generation college students?  What is it like to carry a lot of debt, and how does this impact the ability to complete college on time?  Both sets of questions are important, but they can only be answered using specific tools tailored to those questions.  For the former, you need large numbers to make adequate comparisons.  For the latter, you need to talk to people, find out what they are thinking and feeling, and try to inhabit their shoes for a little while so you can make sense of their experiences and beliefs.

Examples of Qualitative Research

You have probably seen examples of qualitative research before, but you might not have paid particular attention to how they were produced or realized that the accounts you were reading were the result of hours, months, even years of research “in the field.”  A good qualitative researcher will present the product of their hours of work in such a way that it seems natural, even obvious, to the reader.  Because we are trying to convey what it is like answers, qualitative research is often presented as stories – stories about how people live their lives, go to work, raise their children, interact with one another.  In some ways, this can seem like reading particularly insightful novels.  But, unlike novels, there are very specific rules and guidelines that qualitative researchers follow to ensure that the “story” they are telling is accurate , a truthful rendition of what life is like for the people being studied.  Most of this textbook will be spent conveying those rules and guidelines.  Let’s take a look, first, however, at three examples of what the end product looks like.  I have chosen these three examples to showcase very different approaches to qualitative research, and I will return to these five examples throughout the book.  They were all published as whole books (not chapters or articles), and they are worth the long read, if you have the time.  I will also provide some information on how these books came to be and the length of time it takes to get them into book version.  It is important you know about this process, and the rest of this textbook will help explain why it takes so long to conduct good qualitative research!

Example 1 : The End Game (ethnography + interviews)

Corey Abramson is a sociologist who teaches at the University of Arizona.   In 2015 he published The End Game: How Inequality Shapes our Final Years ( 2015 ). This book was based on the research he did for his dissertation at the University of California-Berkeley in 2012.  Actually, the dissertation was completed in 2012 but the work that was produced that took several years.  The dissertation was entitled, “This is How We Live, This is How We Die: Social Stratification, Aging, and Health in Urban America” ( 2012 ).  You can see how the book version, which was written for a more general audience, has a more engaging sound to it, but that the dissertation version, which is what academic faculty read and evaluate, has a more descriptive title.  You can read the title and know that this is a study about aging and health and that the focus is going to be inequality and that the context (place) is going to be “urban America.”  It’s a study about “how” people do something – in this case, how they deal with aging and death.  This is the very first sentence of the dissertation, “From our first breath in the hospital to the day we die, we live in a society characterized by unequal opportunities for maintaining health and taking care of ourselves when ill.  These disparities reflect persistent racial, socio-economic, and gender-based inequalities and contribute to their persistence over time” ( 1 ).  What follows is a truthful account of how that is so.

Cory Abramson spent three years conducting his research in four different urban neighborhoods.  We call the type of research he conducted “comparative ethnographic” because he designed his study to compare groups of seniors as they went about their everyday business.  It’s comparative because he is comparing different groups (based on race, class, gender) and ethnographic because he is studying the culture/way of life of a group. [4]   He had an educated guess, rooted in what previous research had shown and what social theory would suggest, that people’s experiences of aging differ by race, class, and gender.  So, he set up a research design that would allow him to observe differences.  He chose two primarily middle-class (one was racially diverse and the other was predominantly White) and two primarily poor neighborhoods (one was racially diverse and the other was predominantly African American).  He hung out in senior centers and other places seniors congregated, watched them as they took the bus to get prescriptions filled, sat in doctor’s offices with them, and listened to their conversations with each other.  He also conducted more formal conversations, what we call in-depth interviews, with sixty seniors from each of the four neighborhoods.  As with a lot of fieldwork , as he got closer to the people involved, he both expanded and deepened his reach –

By the end of the project, I expanded my pool of general observations to include various settings frequented by seniors: apartment building common rooms, doctors’ offices, emergency rooms, pharmacies, senior centers, bars, parks, corner stores, shopping centers, pool halls, hair salons, coffee shops, and discount stores. Over the course of the three years of fieldwork, I observed hundreds of elders, and developed close relationships with a number of them. ( 2012:10 )

When Abramson rewrote the dissertation for a general audience and published his book in 2015, it got a lot of attention.  It is a beautifully written book and it provided insight into a common human experience that we surprisingly know very little about.  It won the Outstanding Publication Award by the American Sociological Association Section on Aging and the Life Course and was featured in the New York Times .  The book was about aging, and specifically how inequality shapes the aging process, but it was also about much more than that.  It helped show how inequality affects people’s everyday lives.  For example, by observing the difficulties the poor had in setting up appointments and getting to them using public transportation and then being made to wait to see a doctor, sometimes in standing-room-only situations, when they are unwell, and then being treated dismissively by hospital staff, Abramson allowed readers to feel the material reality of being poor in the US.  Comparing these examples with seniors with adequate supplemental insurance who have the resources to hire car services or have others assist them in arranging care when they need it, jolts the reader to understand and appreciate the difference money makes in the lives and circumstances of us all, and in a way that is different than simply reading a statistic (“80% of the poor do not keep regular doctor’s appointments”) does.  Qualitative research can reach into spaces and places that often go unexamined and then reports back to the rest of us what it is like in those spaces and places.

Example 2: Racing for Innocence (Interviews + Content Analysis + Fictional Stories)

Jennifer Pierce is a Professor of American Studies at the University of Minnesota.  Trained as a sociologist, she has written a number of books about gender, race, and power.  Her very first book, Gender Trials: Emotional Lives in Contemporary Law Firms, published in 1995, is a brilliant look at gender dynamics within two law firms.  Pierce was a participant observer, working as a paralegal, and she observed how female lawyers and female paralegals struggled to obtain parity with their male colleagues.

Fifteen years later, she reexamined the context of the law firm to include an examination of racial dynamics, particularly how elite white men working in these spaces created and maintained a culture that made it difficult for both female attorneys and attorneys of color to thrive. Her book, Racing for Innocence: Whiteness, Gender, and the Backlash Against Affirmative Action , published in 2012, is an interesting and creative blending of interviews with attorneys, content analyses of popular films during this period, and fictional accounts of racial discrimination and sexual harassment.  The law firm she chose to study had come under an affirmative action order and was in the process of implementing equitable policies and programs.  She wanted to understand how recipients of white privilege (the elite white male attorneys) come to deny the role they play in reproducing inequality.  Through interviews with attorneys who were present both before and during the affirmative action order, she creates a historical record of the “bad behavior” that necessitated new policies and procedures, but also, and more importantly , probed the participants ’ understanding of this behavior.  It should come as no surprise that most (but not all) of the white male attorneys saw little need for change, and that almost everyone else had accounts that were different if not sometimes downright harrowing.

I’ve used Pierce’s book in my qualitative research methods courses as an example of an interesting blend of techniques and presentation styles.  My students often have a very difficult time with the fictional accounts she includes.  But they serve an important communicative purpose here.  They are her attempts at presenting “both sides” to an objective reality – something happens (Pierce writes this something so it is very clear what it is), and the two participants to the thing that happened have very different understandings of what this means.  By including these stories, Pierce presents one of her key findings – people remember things differently and these different memories tend to support their own ideological positions.  I wonder what Pierce would have written had she studied the murder of George Floyd or the storming of the US Capitol on January 6 or any number of other historic events whose observers and participants record very different happenings.

This is not to say that qualitative researchers write fictional accounts.  In fact, the use of fiction in our work remains controversial.  When used, it must be clearly identified as a presentation device, as Pierce did.  I include Racing for Innocence here as an example of the multiple uses of methods and techniques and the way that these work together to produce better understandings by us, the readers, of what Pierce studied.  We readers come away with a better grasp of how and why advantaged people understate their own involvement in situations and structures that advantage them.  This is normal human behavior , in other words.  This case may have been about elite white men in law firms, but the general insights here can be transposed to other settings.  Indeed, Pierce argues that more research needs to be done about the role elites play in the reproduction of inequality in the workplace in general.

Example 3: Amplified Advantage (Mixed Methods: Survey Interviews + Focus Groups + Archives)

The final example comes from my own work with college students, particularly the ways in which class background affects the experience of college and outcomes for graduates.  I include it here as an example of mixed methods, and for the use of supplementary archival research.  I’ve done a lot of research over the years on first-generation, low-income, and working-class college students.  I am curious (and skeptical) about the possibility of social mobility today, particularly with the rising cost of college and growing inequality in general.  As one of the few people in my family to go to college, I didn’t grow up with a lot of examples of what college was like or how to make the most of it.  And when I entered graduate school, I realized with dismay that there were very few people like me there.  I worried about becoming too different from my family and friends back home.  And I wasn’t at all sure that I would ever be able to pay back the huge load of debt I was taking on.  And so I wrote my dissertation and first two books about working-class college students.  These books focused on experiences in college and the difficulties of navigating between family and school ( Hurst 2010a, 2012 ).  But even after all that research, I kept coming back to wondering if working-class students who made it through college had an equal chance at finding good jobs and happy lives,

What happens to students after college?  Do working-class students fare as well as their peers?  I knew from my own experience that barriers continued through graduate school and beyond, and that my debtload was higher than that of my peers, constraining some of the choices I made when I graduated.  To answer these questions, I designed a study of students attending small liberal arts colleges, the type of college that tried to equalize the experience of students by requiring all students to live on campus and offering small classes with lots of interaction with faculty.  These private colleges tend to have more money and resources so they can provide financial aid to low-income students.  They also attract some very wealthy students.  Because they enroll students across the class spectrum, I would be able to draw comparisons.  I ended up spending about four years collecting data, both a survey of more than 2000 students (which formed the basis for quantitative analyses) and qualitative data collection (interviews, focus groups, archival research, and participant observation).  This is what we call a “mixed methods” approach because we use both quantitative and qualitative data.  The survey gave me a large enough number of students that I could make comparisons of the how many kind, and to be able to say with some authority that there were in fact significant differences in experience and outcome by class (e.g., wealthier students earned more money and had little debt; working-class students often found jobs that were not in their chosen careers and were very affected by debt, upper-middle-class students were more likely to go to graduate school).  But the survey analyses could not explain why these differences existed.  For that, I needed to talk to people and ask them about their motivations and aspirations.  I needed to understand their perceptions of the world, and it is very hard to do this through a survey.

By interviewing students and recent graduates, I was able to discern particular patterns and pathways through college and beyond.  Specifically, I identified three versions of gameplay.  Upper-middle-class students, whose parents were themselves professionals (academics, lawyers, managers of non-profits), saw college as the first stage of their education and took classes and declared majors that would prepare them for graduate school.  They also spent a lot of time building their resumes, taking advantage of opportunities to help professors with their research, or study abroad.  This helped them gain admission to highly-ranked graduate schools and interesting jobs in the public sector.  In contrast, upper-class students, whose parents were wealthy and more likely to be engaged in business (as CEOs or other high-level directors), prioritized building social capital.  They did this by joining fraternities and sororities and playing club sports.  This helped them when they graduated as they called on friends and parents of friends to find them well-paying jobs.  Finally, low-income, first-generation, and working-class students were often adrift.  They took the classes that were recommended to them but without the knowledge of how to connect them to life beyond college.  They spent time working and studying rather than partying or building their resumes.  All three sets of students thought they were “doing college” the right way, the way that one was supposed to do college.   But these three versions of gameplay led to distinct outcomes that advantaged some students over others.  I titled my work “Amplified Advantage” to highlight this process.

These three examples, Cory Abramson’s The End Game , Jennifer Peirce’s Racing for Innocence, and my own Amplified Advantage, demonstrate the range of approaches and tools available to the qualitative researcher.  They also help explain why qualitative research is so important.  Numbers can tell us some things about the world, but they cannot get at the hearts and minds, motivations and beliefs of the people who make up the social worlds we inhabit.  For that, we need tools that allow us to listen and make sense of what people tell us and show us.  That is what good qualitative research offers us.

How Is This Book Organized?

This textbook is organized as a comprehensive introduction to the use of qualitative research methods.  The first half covers general topics (e.g., approaches to qualitative research, ethics) and research design (necessary steps for building a successful qualitative research study).  The second half reviews various data collection and data analysis techniques.  Of course, building a successful qualitative research study requires some knowledge of data collection and data analysis so the chapters in the first half and the chapters in the second half should be read in conversation with each other.  That said, each chapter can be read on its own for assistance with a particular narrow topic.  In addition to the chapters, a helpful glossary can be found in the back of the book.  Rummage around in the text as needed.

Chapter Descriptions

Chapter 2 provides an overview of the Research Design Process.  How does one begin a study? What is an appropriate research question?  How is the study to be done – with what methods ?  Involving what people and sites?  Although qualitative research studies can and often do change and develop over the course of data collection, it is important to have a good idea of what the aims and goals of your study are at the outset and a good plan of how to achieve those aims and goals.  Chapter 2 provides a road map of the process.

Chapter 3 describes and explains various ways of knowing the (social) world.  What is it possible for us to know about how other people think or why they behave the way they do?  What does it mean to say something is a “fact” or that it is “well-known” and understood?  Qualitative researchers are particularly interested in these questions because of the types of research questions we are interested in answering (the how questions rather than the how many questions of quantitative research).  Qualitative researchers have adopted various epistemological approaches.  Chapter 3 will explore these approaches, highlighting interpretivist approaches that acknowledge the subjective aspect of reality – in other words, reality and knowledge are not objective but rather influenced by (interpreted through) people.

Chapter 4 focuses on the practical matter of developing a research question and finding the right approach to data collection.  In any given study (think of Cory Abramson’s study of aging, for example), there may be years of collected data, thousands of observations , hundreds of pages of notes to read and review and make sense of.  If all you had was a general interest area (“aging”), it would be very difficult, nearly impossible, to make sense of all of that data.  The research question provides a helpful lens to refine and clarify (and simplify) everything you find and collect.  For that reason, it is important to pull out that lens (articulate the research question) before you get started.  In the case of the aging study, Cory Abramson was interested in how inequalities affected understandings and responses to aging.  It is for this reason he designed a study that would allow him to compare different groups of seniors (some middle-class, some poor).  Inevitably, he saw much more in the three years in the field than what made it into his book (or dissertation), but he was able to narrow down the complexity of the social world to provide us with this rich account linked to the original research question.  Developing a good research question is thus crucial to effective design and a successful outcome.  Chapter 4 will provide pointers on how to do this.  Chapter 4 also provides an overview of general approaches taken to doing qualitative research and various “traditions of inquiry.”

Chapter 5 explores sampling .  After you have developed a research question and have a general idea of how you will collect data (Observations?  Interviews?), how do you go about actually finding people and sites to study?  Although there is no “correct number” of people to interview , the sample should follow the research question and research design.  Unlike quantitative research, qualitative research involves nonprobability sampling.  Chapter 5 explains why this is so and what qualities instead make a good sample for qualitative research.

Chapter 6 addresses the importance of reflexivity in qualitative research.  Related to epistemological issues of how we know anything about the social world, qualitative researchers understand that we the researchers can never be truly neutral or outside the study we are conducting.  As observers, we see things that make sense to us and may entirely miss what is either too obvious to note or too different to comprehend.  As interviewers, as much as we would like to ask questions neutrally and remain in the background, interviews are a form of conversation, and the persons we interview are responding to us .  Therefore, it is important to reflect upon our social positions and the knowledges and expectations we bring to our work and to work through any blind spots that we may have.  Chapter 6 provides some examples of reflexivity in practice and exercises for thinking through one’s own biases.

Chapter 7 is a very important chapter and should not be overlooked.  As a practical matter, it should also be read closely with chapters 6 and 8.  Because qualitative researchers deal with people and the social world, it is imperative they develop and adhere to a strong ethical code for conducting research in a way that does not harm.  There are legal requirements and guidelines for doing so (see chapter 8), but these requirements should not be considered synonymous with the ethical code required of us.   Each researcher must constantly interrogate every aspect of their research, from research question to design to sample through analysis and presentation, to ensure that a minimum of harm (ideally, zero harm) is caused.  Because each research project is unique, the standards of care for each study are unique.  Part of being a professional researcher is carrying this code in one’s heart, being constantly attentive to what is required under particular circumstances.  Chapter 7 provides various research scenarios and asks readers to weigh in on the suitability and appropriateness of the research.  If done in a class setting, it will become obvious fairly quickly that there are often no absolutely correct answers, as different people find different aspects of the scenarios of greatest importance.  Minimizing the harm in one area may require possible harm in another.  Being attentive to all the ethical aspects of one’s research and making the best judgments one can, clearly and consciously, is an integral part of being a good researcher.

Chapter 8 , best to be read in conjunction with chapter 7, explains the role and importance of Institutional Review Boards (IRBs) .  Under federal guidelines, an IRB is an appropriately constituted group that has been formally designated to review and monitor research involving human subjects .  Every institution that receives funding from the federal government has an IRB.  IRBs have the authority to approve, require modifications to (to secure approval), or disapprove research.  This group review serves an important role in the protection of the rights and welfare of human research subjects.  Chapter 8 reviews the history of IRBs and the work they do but also argues that IRBs’ review of qualitative research is often both over-inclusive and under-inclusive.  Some aspects of qualitative research are not well understood by IRBs, given that they were developed to prevent abuses in biomedical research.  Thus, it is important not to rely on IRBs to identify all the potential ethical issues that emerge in our research (see chapter 7).

Chapter 9 provides help for getting started on formulating a research question based on gaps in the pre-existing literature.  Research is conducted as part of a community, even if particular studies are done by single individuals (or small teams).  What any of us finds and reports back becomes part of a much larger body of knowledge.  Thus, it is important that we look at the larger body of knowledge before we actually start our bit to see how we can best contribute.  When I first began interviewing working-class college students, there was only one other similar study I could find, and it hadn’t been published (it was a dissertation of students from poor backgrounds).  But there had been a lot published by professors who had grown up working class and made it through college despite the odds.  These accounts by “working-class academics” became an important inspiration for my study and helped me frame the questions I asked the students I interviewed.  Chapter 9 will provide some pointers on how to search for relevant literature and how to use this to refine your research question.

Chapter 10 serves as a bridge between the two parts of the textbook, by introducing techniques of data collection.  Qualitative research is often characterized by the form of data collection – for example, an ethnographic study is one that employs primarily observational data collection for the purpose of documenting and presenting a particular culture or ethnos.  Techniques can be effectively combined, depending on the research question and the aims and goals of the study.   Chapter 10 provides a general overview of all the various techniques and how they can be combined.

The second part of the textbook moves into the doing part of qualitative research once the research question has been articulated and the study designed.  Chapters 11 through 17 cover various data collection techniques and approaches.  Chapters 18 and 19 provide a very simple overview of basic data analysis.  Chapter 20 covers communication of the data to various audiences, and in various formats.

Chapter 11 begins our overview of data collection techniques with a focus on interviewing , the true heart of qualitative research.  This technique can serve as the primary and exclusive form of data collection, or it can be used to supplement other forms (observation, archival).  An interview is distinct from a survey, where questions are asked in a specific order and often with a range of predetermined responses available.  Interviews can be conversational and unstructured or, more conventionally, semistructured , where a general set of interview questions “guides” the conversation.  Chapter 11 covers the basics of interviews: how to create interview guides, how many people to interview, where to conduct the interview, what to watch out for (how to prepare against things going wrong), and how to get the most out of your interviews.

Chapter 12 covers an important variant of interviewing, the focus group.  Focus groups are semistructured interviews with a group of people moderated by a facilitator (the researcher or researcher’s assistant).  Focus groups explicitly use group interaction to assist in the data collection.  They are best used to collect data on a specific topic that is non-personal and shared among the group.  For example, asking a group of college students about a common experience such as taking classes by remote delivery during the pandemic year of 2020.  Chapter 12 covers the basics of focus groups: when to use them, how to create interview guides for them, and how to run them effectively.

Chapter 13 moves away from interviewing to the second major form of data collection unique to qualitative researchers – observation .  Qualitative research that employs observation can best be understood as falling on a continuum of “fly on the wall” observation (e.g., observing how strangers interact in a doctor’s waiting room) to “participant” observation, where the researcher is also an active participant of the activity being observed.  For example, an activist in the Black Lives Matter movement might want to study the movement, using her inside position to gain access to observe key meetings and interactions.  Chapter  13 covers the basics of participant observation studies: advantages and disadvantages, gaining access, ethical concerns related to insider/outsider status and entanglement, and recording techniques.

Chapter 14 takes a closer look at “deep ethnography” – immersion in the field of a particularly long duration for the purpose of gaining a deeper understanding and appreciation of a particular culture or social world.  Clifford Geertz called this “deep hanging out.”  Whereas participant observation is often combined with semistructured interview techniques, deep ethnography’s commitment to “living the life” or experiencing the situation as it really is demands more conversational and natural interactions with people.  These interactions and conversations may take place over months or even years.  As can be expected, there are some costs to this technique, as well as some very large rewards when done competently.  Chapter 14 provides some examples of deep ethnographies that will inspire some beginning researchers and intimidate others.

Chapter 15 moves in the opposite direction of deep ethnography, a technique that is the least positivist of all those discussed here, to mixed methods , a set of techniques that is arguably the most positivist .  A mixed methods approach combines both qualitative data collection and quantitative data collection, commonly by combining a survey that is analyzed statistically (e.g., cross-tabs or regression analyses of large number probability samples) with semi-structured interviews.  Although it is somewhat unconventional to discuss mixed methods in textbooks on qualitative research, I think it is important to recognize this often-employed approach here.  There are several advantages and some disadvantages to taking this route.  Chapter 16 will describe those advantages and disadvantages and provide some particular guidance on how to design a mixed methods study for maximum effectiveness.

Chapter 16 covers data collection that does not involve live human subjects at all – archival and historical research (chapter 17 will also cover data that does not involve interacting with human subjects).  Sometimes people are unavailable to us, either because they do not wish to be interviewed or observed (as is the case with many “elites”) or because they are too far away, in both place and time.  Fortunately, humans leave many traces and we can often answer questions we have by examining those traces.  Special collections and archives can be goldmines for social science research.  This chapter will explain how to access these places, for what purposes, and how to begin to make sense of what you find.

Chapter 17 covers another data collection area that does not involve face-to-face interaction with humans: content analysis .  Although content analysis may be understood more properly as a data analysis technique, the term is often used for the entire approach, which will be the case here.  Content analysis involves interpreting meaning from a body of text.  This body of text might be something found in historical records (see chapter 16) or something collected by the researcher, as in the case of comment posts on a popular blog post.  I once used the stories told by student loan debtors on the website studentloanjustice.org as the content I analyzed.  Content analysis is particularly useful when attempting to define and understand prevalent stories or communication about a topic of interest.  In other words, when we are less interested in what particular people (our defined sample) are doing or believing and more interested in what general narratives exist about a particular topic or issue.  This chapter will explore different approaches to content analysis and provide helpful tips on how to collect data, how to turn that data into codes for analysis, and how to go about presenting what is found through analysis.

Where chapter 17 has pushed us towards data analysis, chapters 18 and 19 are all about what to do with the data collected, whether that data be in the form of interview transcripts or fieldnotes from observations.  Chapter 18 introduces the basics of coding , the iterative process of assigning meaning to the data in order to both simplify and identify patterns.  What is a code and how does it work?  What are the different ways of coding data, and when should you use them?  What is a codebook, and why do you need one?  What does the process of data analysis look like?

Chapter 19 goes further into detail on codes and how to use them, particularly the later stages of coding in which our codes are refined, simplified, combined, and organized.  These later rounds of coding are essential to getting the most out of the data we’ve collected.  As students are often overwhelmed with the amount of data (a corpus of interview transcripts typically runs into the hundreds of pages; fieldnotes can easily top that), this chapter will also address time management and provide suggestions for dealing with chaos and reminders that feeling overwhelmed at the analysis stage is part of the process.  By the end of the chapter, you should understand how “findings” are actually found.

The book concludes with a chapter dedicated to the effective presentation of data results.  Chapter 20 covers the many ways that researchers communicate their studies to various audiences (academic, personal, political), what elements must be included in these various publications, and the hallmarks of excellent qualitative research that various audiences will be expecting.  Because qualitative researchers are motivated by understanding and conveying meaning , effective communication is not only an essential skill but a fundamental facet of the entire research project.  Ethnographers must be able to convey a certain sense of verisimilitude , the appearance of true reality.  Those employing interviews must faithfully depict the key meanings of the people they interviewed in a way that rings true to those people, even if the end result surprises them.  And all researchers must strive for clarity in their publications so that various audiences can understand what was found and why it is important.

The book concludes with a short chapter ( chapter 21 ) discussing the value of qualitative research. At the very end of this book, you will find a glossary of terms. I recommend you make frequent use of the glossary and add to each entry as you find examples. Although the entries are meant to be simple and clear, you may also want to paraphrase the definition—make it “make sense” to you, in other words. In addition to the standard reference list (all works cited here), you will find various recommendations for further reading at the end of many chapters. Some of these recommendations will be examples of excellent qualitative research, indicated with an asterisk (*) at the end of the entry. As they say, a picture is worth a thousand words. A good example of qualitative research can teach you more about conducting research than any textbook can (this one included). I highly recommend you select one to three examples from these lists and read them along with the textbook.

A final note on the choice of examples – you will note that many of the examples used in the text come from research on college students.  This is for two reasons.  First, as most of my research falls in this area, I am most familiar with this literature and have contacts with those who do research here and can call upon them to share their stories with you.  Second, and more importantly, my hope is that this textbook reaches a wide audience of beginning researchers who study widely and deeply across the range of what can be known about the social world (from marine resources management to public policy to nursing to political science to sexuality studies and beyond).  It is sometimes difficult to find examples that speak to all those research interests, however. A focus on college students is something that all readers can understand and, hopefully, appreciate, as we are all now or have been at some point a college student.

Recommended Reading: Other Qualitative Research Textbooks

I’ve included a brief list of some of my favorite qualitative research textbooks and guidebooks if you need more than what you will find in this introductory text.  For each, I’ve also indicated if these are for “beginning” or “advanced” (graduate-level) readers.  Many of these books have several editions that do not significantly vary; the edition recommended is merely the edition I have used in teaching and to whose page numbers any specific references made in the text agree.

Barbour, Rosaline. 2014. Introducing Qualitative Research: A Student’s Guide. Thousand Oaks, CA: SAGE.  A good introduction to qualitative research, with abundant examples (often from the discipline of health care) and clear definitions.  Includes quick summaries at the ends of each chapter.  However, some US students might find the British context distracting and can be a bit advanced in some places.  Beginning .

Bloomberg, Linda Dale, and Marie F. Volpe. 2012. Completing Your Qualitative Dissertation . 2nd ed. Thousand Oaks, CA: SAGE.  Specifically designed to guide graduate students through the research process. Advanced .

Creswell, John W., and Cheryl Poth. 2018 Qualitative Inquiry and Research Design: Choosing among Five Traditions .  4th ed. Thousand Oaks, CA: SAGE.  This is a classic and one of the go-to books I used myself as a graduate student.  One of the best things about this text is its clear presentation of five distinct traditions in qualitative research.  Despite the title, this reasonably sized book is about more than research design, including both data analysis and how to write about qualitative research.  Advanced .

Lareau, Annette. 2021. Listening to People: A Practical Guide to Interviewing, Participant Observation, Data Analysis, and Writing It All Up .  Chicago: University of Chicago Press. A readable and personal account of conducting qualitative research by an eminent sociologist, with a heavy emphasis on the kinds of participant-observation research conducted by the author.  Despite its reader-friendliness, this is really a book targeted to graduate students learning the craft.  Advanced .

Lune, Howard, and Bruce L. Berg. 2018. 9th edition.  Qualitative Research Methods for the Social Sciences.  Pearson . Although a good introduction to qualitative methods, the authors favor symbolic interactionist and dramaturgical approaches, which limits the appeal primarily to sociologists.  Beginning .

Marshall, Catherine, and Gretchen B. Rossman. 2016. 6th edition. Designing Qualitative Research. Thousand Oaks, CA: SAGE.  Very readable and accessible guide to research design by two educational scholars.  Although the presentation is sometimes fairly dry, personal vignettes and illustrations enliven the text.  Beginning .

Maxwell, Joseph A. 2013. Qualitative Research Design: An Interactive Approach .  3rd ed. Thousand Oaks, CA: SAGE. A short and accessible introduction to qualitative research design, particularly helpful for graduate students contemplating theses and dissertations. This has been a standard textbook in my graduate-level courses for years.  Advanced .

Patton, Michael Quinn. 2002. Qualitative Research and Evaluation Methods . Thousand Oaks, CA: SAGE.  This is a comprehensive text that served as my “go-to” reference when I was a graduate student.  It is particularly helpful for those involved in program evaluation and other forms of evaluation studies and uses examples from a wide range of disciplines.  Advanced .

Rubin, Ashley T. 2021. Rocking Qualitative Social Science: An Irreverent Guide to Rigorous Research. Stanford : Stanford University Press.  A delightful and personal read.  Rubin uses rock climbing as an extended metaphor for learning how to conduct qualitative research.  A bit slanted toward ethnographic and archival methods of data collection, with frequent examples from her own studies in criminology. Beginning .

Weis, Lois, and Michelle Fine. 2000. Speed Bumps: A Student-Friendly Guide to Qualitative Research . New York: Teachers College Press.  Readable and accessibly written in a quasi-conversational style.  Particularly strong in its discussion of ethical issues throughout the qualitative research process.  Not comprehensive, however, and very much tied to ethnographic research.  Although designed for graduate students, this is a recommended read for students of all levels.  Beginning .

Patton’s Ten Suggestions for Doing Qualitative Research

The following ten suggestions were made by Michael Quinn Patton in his massive textbooks Qualitative Research and Evaluations Methods . This book is highly recommended for those of you who want more than an introduction to qualitative methods. It is the book I relied on heavily when I was a graduate student, although it is much easier to “dip into” when necessary than to read through as a whole. Patton is asked for “just one bit of advice” for a graduate student considering using qualitative research methods for their dissertation.  Here are his top ten responses, in short form, heavily paraphrased, and with additional comments and emphases from me:

• Make sure that a qualitative approach fits the research question. The following are the kinds of questions that call out for qualitative methods or where qualitative methods are particularly appropriate: questions about people’s experiences or how they make sense of those experiences; studying a person in their natural environment; researching a phenomenon so unknown that it would be impossible to study it with standardized instruments or other forms of quantitative data collection.
• Study qualitative research by going to the original sources for the design and analysis appropriate to the particular approach you want to take (e.g., read Glaser and Straus if you are using grounded theory )
• Find a dissertation adviser who understands or at least who will support your use of qualitative research methods. You are asking for trouble if your entire committee is populated by quantitative researchers, even if they are all very knowledgeable about the subject or focus of your study (maybe even more so if they are!)
• Really work on design. Doing qualitative research effectively takes a lot of planning.  Even if things are more flexible than in quantitative research, a good design is absolutely essential when starting out.
• Practice data collection techniques, particularly interviewing and observing. There is definitely a set of learned skills here!  Do not expect your first interview to be perfect.  You will continue to grow as a researcher the more interviews you conduct, and you will probably come to understand yourself a bit more in the process, too.  This is not easy, despite what others who don’t work with qualitative methods may assume (and tell you!)
• Have a plan for analysis before you begin data collection. This is often a requirement in IRB protocols , although you can get away with writing something fairly simple.  And even if you are taking an approach, such as grounded theory, that pushes you to remain fairly open-minded during the data collection process, you still want to know what you will be doing with all the data collected – creating a codebook? Writing analytical memos? Comparing cases?  Having a plan in hand will also help prevent you from collecting too much extraneous data.
• Be prepared to confront controversies both within the qualitative research community and between qualitative research and quantitative research. Don’t be naïve about this – qualitative research, particularly some approaches, will be derided by many more “positivist” researchers and audiences.  For example, is an “n” of 1 really sufficient?  Yes!  But not everyone will agree.
• Do not make the mistake of using qualitative research methods because someone told you it was easier, or because you are intimidated by the math required of statistical analyses. Qualitative research is difficult in its own way (and many would claim much more time-consuming than quantitative research).  Do it because you are convinced it is right for your goals, aims, and research questions.
• Find a good support network. This could be a research mentor, or it could be a group of friends or colleagues who are also using qualitative research, or it could be just someone who will listen to you work through all of the issues you will confront out in the field and during the writing process.  Even though qualitative research often involves human subjects, it can be pretty lonely.  A lot of times you will feel like you are working without a net.  You have to create one for yourself.  Take care of yourself.
• And, finally, in the words of Patton, “Prepare to be changed. Looking deeply at other people’s lives will force you to look deeply at yourself.”
• We will actually spend an entire chapter ( chapter 3 ) looking at this question in much more detail! ↵
• Note that this might have been news to Europeans at the time, but many other societies around the world had also come to this conclusion through observation.  There is often a tendency to equate “the scientific revolution” with the European world in which it took place, but this is somewhat misleading. ↵
• Historians are a special case here.  Historians have scrupulously and rigorously investigated the social world, but not for the purpose of understanding general laws about how things work, which is the point of scientific empirical research.  History is often referred to as an idiographic field of study, meaning that it studies things that happened or are happening in themselves and not for general observations or conclusions. ↵
• Don’t worry, we’ll spend more time later in this book unpacking the meaning of ethnography and other terms that are important here.  Note the available glossary ↵

An approach to research that is “multimethod in focus, involving an interpretative, naturalistic approach to its subject matter.  This means that qualitative researchers study things in their natural settings, attempting to make sense of, or interpret, phenomena in terms of the meanings people bring to them.  Qualitative research involves the studied use and collection of a variety of empirical materials – case study, personal experience, introspective, life story, interview, observational, historical, interactional, and visual texts – that describe routine and problematic moments and meanings in individuals’ lives." ( Denzin and Lincoln 2005:2 ). Contrast with quantitative research .

In contrast to methodology, methods are more simply the practices and tools used to collect and analyze data.  Examples of common methods in qualitative research are interviews , observations , and documentary analysis .  One’s methodology should connect to one’s choice of methods, of course, but they are distinguishable terms.  See also methodology .

A proposed explanation for an observation, phenomenon, or scientific problem that can be tested by further investigation.  The positing of a hypothesis is often the first step in quantitative research but not in qualitative research.  Even when qualitative researchers offer possible explanations in advance of conducting research, they will tend to not use the word “hypothesis” as it conjures up the kind of positivist research they are not conducting.

The foundational question to be addressed by the research study.  This will form the anchor of the research design, collection, and analysis.  Note that in qualitative research, the research question may, and probably will, alter or develop during the course of the research.

An approach to research that collects and analyzes numerical data for the purpose of finding patterns and averages, making predictions, testing causal relationships, and generalizing results to wider populations.  Contrast with qualitative research .

Data collection that takes place in real-world settings, referred to as “the field;” a key component of much Grounded Theory and ethnographic research.  Patton ( 2002 ) calls fieldwork “the central activity of qualitative inquiry” where “‘going into the field’ means having direct and personal contact with people under study in their own environments – getting close to people and situations being studied to personally understand the realities of minutiae of daily life” (48).

The people who are the subjects of a qualitative study.  In interview-based studies, they may be the respondents to the interviewer; for purposes of IRBs, they are often referred to as the human subjects of the research.

The branch of philosophy concerned with knowledge.  For researchers, it is important to recognize and adopt one of the many distinguishing epistemological perspectives as part of our understanding of what questions research can address or fully answer.  See, e.g., constructivism , subjectivism, and  objectivism .

An approach that refutes the possibility of neutrality in social science research.  All research is “guided by a set of beliefs and feelings about the world and how it should be understood and studied” (Denzin and Lincoln 2005: 13).  In contrast to positivism , interpretivism recognizes the social constructedness of reality, and researchers adopting this approach focus on capturing interpretations and understandings people have about the world rather than “the world” as it is (which is a chimera).

The cluster of data-collection tools and techniques that involve observing interactions between people, the behaviors, and practices of individuals (sometimes in contrast to what they say about how they act and behave), and cultures in context.  Observational methods are the key tools employed by ethnographers and Grounded Theory .

Research based on data collected and analyzed by the research (in contrast to secondary “library” research).

The process of selecting people or other units of analysis to represent a larger population. In quantitative research, this representation is taken quite literally, as statistically representative.  In qualitative research, in contrast, sample selection is often made based on potential to generate insight about a particular topic or phenomenon.

A method of data collection in which the researcher asks the participant questions; the answers to these questions are often recorded and transcribed verbatim. There are many different kinds of interviews - see also semistructured interview , structured interview , and unstructured interview .

The specific group of individuals that you will collect data from.  Contrast population.

The practice of being conscious of and reflective upon one’s own social location and presence when conducting research.  Because qualitative research often requires interaction with live humans, failing to take into account how one’s presence and prior expectations and social location affect the data collected and how analyzed may limit the reliability of the findings.  This remains true even when dealing with historical archives and other content.  Who we are matters when asking questions about how people experience the world because we, too, are a part of that world.

The science and practice of right conduct; in research, it is also the delineation of moral obligations towards research participants, communities to which we belong, and communities in which we conduct our research.

An administrative body established to protect the rights and welfare of human research subjects recruited to participate in research activities conducted under the auspices of the institution with which it is affiliated. The IRB is charged with the responsibility of reviewing all research involving human participants. The IRB is concerned with protecting the welfare, rights, and privacy of human subjects. The IRB has the authority to approve, disapprove, monitor, and require modifications in all research activities that fall within its jurisdiction as specified by both the federal regulations and institutional policy.

Research, according to US federal guidelines, that involves “a living individual about whom an investigator (whether professional or student) conducting research:  (1) Obtains information or biospecimens through intervention or interaction with the individual, and uses, studies, or analyzes the information or biospecimens; or  (2) Obtains, uses, studies, analyzes, or generates identifiable private information or identifiable biospecimens.”

One of the primary methodological traditions of inquiry in qualitative research, ethnography is the study of a group or group culture, largely through observational fieldwork supplemented by interviews. It is a form of fieldwork that may include participant-observation data collection. See chapter 14 for a discussion of deep ethnography. 

A form of interview that follows a standard guide of questions asked, although the order of the questions may change to match the particular needs of each individual interview subject, and probing “follow-up” questions are often added during the course of the interview.  The semi-structured interview is the primary form of interviewing used by qualitative researchers in the social sciences.  It is sometimes referred to as an “in-depth” interview.  See also interview and  interview guide .

A method of observational data collection taking place in a natural setting; a form of fieldwork .  The term encompasses a continuum of relative participation by the researcher (from full participant to “fly-on-the-wall” observer).  This is also sometimes referred to as ethnography , although the latter is characterized by a greater focus on the culture under observation.

A research design that employs both quantitative and qualitative methods, as in the case of a survey supplemented by interviews.

An epistemological perspective that posits the existence of reality through sensory experience similar to empiricism but goes further in denying any non-sensory basis of thought or consciousness.  In the social sciences, the term has roots in the proto-sociologist August Comte, who believed he could discern “laws” of society similar to the laws of natural science (e.g., gravity).  The term has come to mean the kinds of measurable and verifiable science conducted by quantitative researchers and is thus used pejoratively by some qualitative researchers interested in interpretation, consciousness, and human understanding.  Calling someone a “positivist” is often intended as an insult.  See also empiricism and objectivism.

A place or collection containing records, documents, or other materials of historical interest; most universities have an archive of material related to the university’s history, as well as other “special collections” that may be of interest to members of the community.

A method of both data collection and data analysis in which a given content (textual, visual, graphic) is examined systematically and rigorously to identify meanings, themes, patterns and assumptions.  Qualitative content analysis (QCA) is concerned with gathering and interpreting an existing body of material.    

A word or short phrase that symbolically assigns a summative, salient, essence-capturing, and/or evocative attribute for a portion of language-based or visual data (Saldaña 2021:5).

Usually a verbatim written record of an interview or focus group discussion.

The primary form of data for fieldwork , participant observation , and ethnography .  These notes, taken by the researcher either during the course of fieldwork or at day’s end, should include as many details as possible on what was observed and what was said.  They should include clear identifiers of date, time, setting, and names (or identifying characteristics) of participants.

The process of labeling and organizing qualitative data to identify different themes and the relationships between them; a way of simplifying data to allow better management and retrieval of key themes and illustrative passages.  See coding frame and  codebook.

A methodological tradition of inquiry and approach to analyzing qualitative data in which theories emerge from a rigorous and systematic process of induction.  This approach was pioneered by the sociologists Glaser and Strauss (1967).  The elements of theory generated from comparative analysis of data are, first, conceptual categories and their properties and, second, hypotheses or generalized relations among the categories and their properties – “The constant comparing of many groups draws the [researcher’s] attention to their many similarities and differences.  Considering these leads [the researcher] to generate abstract categories and their properties, which, since they emerge from the data, will clearly be important to a theory explaining the kind of behavior under observation.” (36).

A detailed description of any proposed research that involves human subjects for review by IRB.  The protocol serves as the recipe for the conduct of the research activity.  It includes the scientific rationale to justify the conduct of the study, the information necessary to conduct the study, the plan for managing and analyzing the data, and a discussion of the research ethical issues relevant to the research.  Protocols for qualitative research often include interview guides, all documents related to recruitment, informed consent forms, very clear guidelines on the safekeeping of materials collected, and plans for de-identifying transcripts or other data that include personal identifying information.

Introduction to Qualitative Research Methods Copyright © 2023 by Allison Hurst is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License , except where otherwise noted.

18 Qualitative Research Examples

Qualitative research is an approach to scientific research that involves using observation to gather and analyze non-numerical, in-depth, and well-contextualized datasets.

It serves as an integral part of academic, professional, and even daily decision-making processes (Baxter & Jack, 2008).

Methods of qualitative research encompass a wide range of techniques, from in-depth personal encounters, like ethnographies (studying cultures in-depth) and autoethnographies (examining one’s own cultural experiences), to collection of diverse perspectives on topics through methods like interviewing focus groups (gatherings of individuals to discuss specific topics).

Qualitative Research Examples

1. ethnography.

Definition: Ethnography is a qualitative research design aimed at exploring cultural phenomena. Rooted in the discipline of anthropology , this research approach investigates the social interactions, behaviors, and perceptions within groups, communities, or organizations.

Ethnographic research is characterized by extended observation of the group, often through direct participation, in the participants’ environment. An ethnographer typically lives with the study group for extended periods, intricately observing their everyday lives (Khan, 2014).

It aims to present a complete, detailed and accurate picture of the observed social life, rituals, symbols, and values from the perspective of the study group.

Example of Ethnographic Research

Title: “ The Everyday Lives of Men: An Ethnographic Investigation of Young Adult Male Identity “

Citation: Evans, J. (2010). The Everyday Lives of Men: An Ethnographic Investigation of Young Adult Male Identity. Peter Lang.

Overview: This study by Evans (2010) provides a rich narrative of young adult male identity as experienced in everyday life. The author immersed himself among a group of young men, participating in their activities and cultivating a deep understanding of their lifestyle, values, and motivations. This research exemplified the ethnographic approach, revealing complexities of the subjects’ identities and societal roles, which could hardly be accessed through other qualitative research designs.

Read my Full Guide on Ethnography Here

2. Autoethnography

Definition: Autoethnography is an approach to qualitative research where the researcher uses their own personal experiences to extend the understanding of a certain group, culture, or setting. Essentially, it allows for the exploration of self within the context of social phenomena.

Unlike traditional ethnography, which focuses on the study of others, autoethnography turns the ethnographic gaze inward, allowing the researcher to use their personal experiences within a culture as rich qualitative data (Durham, 2019).

The objective is to critically appraise one’s personal experiences as they navigate and negotiate cultural, political, and social meanings. The researcher becomes both the observer and the participant, intertwining personal and cultural experiences in the research.

Example of Autoethnographic Research

Title: “ A Day In The Life Of An NHS Nurse “

Citation: Osben, J. (2019). A day in the life of a NHS nurse in 21st Century Britain: An auto-ethnography. The Journal of Autoethnography for Health & Social Care. 1(1).

Overview: This study presents an autoethnography of a day in the life of an NHS nurse (who, of course, is also the researcher). The author uses the research to achieve reflexivity, with the researcher concluding: “Scrutinising my practice and situating it within a wider contextual backdrop has compelled me to significantly increase my level of scrutiny into the driving forces that influence my practice.”

Read my Full Guide on Autoethnography Here

3. Semi-Structured Interviews

Definition: Semi-structured interviews stand as one of the most frequently used methods in qualitative research. These interviews are planned and utilize a set of pre-established questions, but also allow for the interviewer to steer the conversation in other directions based on the responses given by the interviewee.

In semi-structured interviews, the interviewer prepares a guide that outlines the focal points of the discussion. However, the interview is flexible, allowing for more in-depth probing if the interviewer deems it necessary (Qu, & Dumay, 2011). This style of interviewing strikes a balance between structured ones which might limit the discussion, and unstructured ones, which could lack focus.

Example of Semi-Structured Interview Research

Title: “ Factors influencing adherence to cancer treatment in older adults with cancer: a systematic review “

Citation: Puts, M., et al. (2014). Factors influencing adherence to cancer treatment in older adults with cancer: a systematic review. Annals of oncology, 25 (3), 564-577.

Overview: Puts et al. (2014) executed an extensive systematic review in which they conducted semi-structured interviews with older adults suffering from cancer to examine the factors influencing their adherence to cancer treatment. The findings suggested that various factors, including side effects, faith in healthcare professionals, and social support have substantial impacts on treatment adherence. This research demonstrates how semi-structured interviews can provide rich and profound insights into the subjective experiences of patients.

4. Focus Groups

Definition: Focus groups are a qualitative research method that involves organized discussion with a selected group of individuals to gain their perspectives on a specific concept, product, or phenomenon. Typically, these discussions are guided by a moderator.

During a focus group session, the moderator has a list of questions or topics to discuss, and participants are encouraged to interact with each other (Morgan, 2010). This interactivity can stimulate more information and provide a broader understanding of the issue under scrutiny. The open format allows participants to ask questions and respond freely, offering invaluable insights into attitudes, experiences, and group norms.

Example of Focus Group Research

Title: “ Perspectives of Older Adults on Aging Well: A Focus Group Study “

Citation: Halaweh, H., Dahlin-Ivanoff, S., Svantesson, U., & Willén, C. (2018). Perspectives of older adults on aging well: a focus group study. Journal of aging research .

Overview: This study aimed to explore what older adults (aged 60 years and older) perceived to be ‘aging well’. The researchers identified three major themes from their focus group interviews: a sense of well-being, having good physical health, and preserving good mental health. The findings highlight the importance of factors such as positive emotions, social engagement, physical activity, healthy eating habits, and maintaining independence in promoting aging well among older adults.

5. Phenomenology

Definition: Phenomenology, a qualitative research method, involves the examination of lived experiences to gain an in-depth understanding of the essence or underlying meanings of a phenomenon.

The focus of phenomenology lies in meticulously describing participants’ conscious experiences related to the chosen phenomenon (Padilla-Díaz, 2015).

In a phenomenological study, the researcher collects detailed, first-hand perspectives of the participants, typically via in-depth interviews, and then uses various strategies to interpret and structure these experiences, ultimately revealing essential themes (Creswell, 2013). This approach focuses on the perspective of individuals experiencing the phenomenon, seeking to explore, clarify, and understand the meanings they attach to those experiences.

Example of Phenomenology Research

Title: “ A phenomenological approach to experiences with technology: current state, promise, and future directions for research ”

Citation: Cilesiz, S. (2011). A phenomenological approach to experiences with technology: Current state, promise, and future directions for research. Educational Technology Research and Development, 59 , 487-510.

Overview: A phenomenological approach to experiences with technology by Sebnem Cilesiz represents a good starting point for formulating a phenomenological study. With its focus on the ‘essence of experience’, this piece presents methodological, reliability, validity, and data analysis techniques that phenomenologists use to explain how people experience technology in their everyday lives.

6. Grounded Theory

Definition: Grounded theory is a systematic methodology in qualitative research that typically applies inductive reasoning . The primary aim is to develop a theoretical explanation or framework for a process, action, or interaction grounded in, and arising from, empirical data (Birks & Mills, 2015).

In grounded theory, data collection and analysis work together in a recursive process. The researcher collects data, analyses it, and then collects more data based on the evolving understanding of the research context. This ongoing process continues until a comprehensive theory that represents the data and the associated phenomenon emerges – a point known as theoretical saturation (Charmaz, 2014).

Example of Grounded Theory Research

Title: “ Student Engagement in High School Classrooms from the Perspective of Flow Theory “

Citation: Shernoff, D. J., Csikszentmihalyi, M., Shneider, B., & Shernoff, E. S. (2003). Student engagement in high school classrooms from the perspective of flow theory. School Psychology Quarterly, 18 (2), 158–176.

Overview: Shernoff and colleagues (2003) used grounded theory to explore student engagement in high school classrooms. The researchers collected data through student self-reports, interviews, and observations. Key findings revealed that academic challenge, student autonomy, and teacher support emerged as the most significant factors influencing students’ engagement, demonstrating how grounded theory can illuminate complex dynamics within real-world contexts.

7. Narrative Research

Definition: Narrative research is a qualitative research method dedicated to storytelling and understanding how individuals experience the world. It focuses on studying an individual’s life and experiences as narrated by that individual (Polkinghorne, 2013).

In narrative research, the researcher collects data through methods such as interviews, observations , and document analysis. The emphasis is on the stories told by participants – narratives that reflect their experiences, thoughts, and feelings.

These stories are then interpreted by the researcher, who attempts to understand the meaning the participant attributes to these experiences (Josselson, 2011).

Example of Narrative Research

Title: “Narrative Structures and the Language of the Self”

Citation: McAdams, D. P., Josselson, R., & Lieblich, A. (2006). Identity and story: Creating self in narrative . American Psychological Association.

Overview: In this innovative study, McAdams et al. (2006) employed narrative research to explore how individuals construct their identities through the stories they tell about themselves. By examining personal narratives, the researchers discerned patterns associated with characters, motivations, conflicts, and resolutions, contributing valuable insights about the relationship between narrative and individual identity.

8. Case Study Research

Definition: Case study research is a qualitative research method that involves an in-depth investigation of a single instance or event: a case. These ‘cases’ can range from individuals, groups, or entities to specific projects, programs, or strategies (Creswell, 2013).

The case study method typically uses multiple sources of information for comprehensive contextual analysis. It aims to explore and understand the complexity and uniqueness of a particular case in a real-world context (Merriam & Tisdell, 2015). This investigation could result in a detailed description of the case, a process for its development, or an exploration of a related issue or problem.

Example of Case Study Research

Title: “ Teacher’s Role in Fostering Preschoolers’ Computational Thinking: An Exploratory Case Study “

Citation: Wang, X. C., Choi, Y., Benson, K., Eggleston, C., & Weber, D. (2021). Teacher’s role in fostering preschoolers’ computational thinking: An exploratory case study. Early Education and Development , 32 (1), 26-48.

Overview: This study investigates the role of teachers in promoting computational thinking skills in preschoolers. The study utilized a qualitative case study methodology to examine the computational thinking scaffolding strategies employed by a teacher interacting with three preschoolers in a small group setting. The findings highlight the importance of teachers’ guidance in fostering computational thinking practices such as problem reformulation/decomposition, systematic testing, and debugging.

Read about some Famous Case Studies in Psychology Here

9. Participant Observation

Definition: Participant observation has the researcher immerse themselves in a group or community setting to observe the behavior of its members. It is similar to ethnography, but generally, the researcher isn’t embedded for a long period of time.

The researcher, being a participant, engages in daily activities, interactions, and events as a way of conducting a detailed study of a particular social phenomenon (Kawulich, 2005).

The method involves long-term engagement in the field, maintaining detailed records of observed events, informal interviews, direct participation, and reflexivity. This approach allows for a holistic view of the participants’ lived experiences, behaviours, and interactions within their everyday environment (Dewalt, 2011).

Example of Participant Observation Research

Title: Conflict in the boardroom: a participant observation study of supervisory board dynamics

Citation: Heemskerk, E. M., Heemskerk, K., & Wats, M. M. (2017). Conflict in the boardroom: a participant observation study of supervisory board dynamics. Journal of Management & Governance , 21 , 233-263.

Overview: This study examined how conflicts within corporate boards affect their performance. The researchers used a participant observation method, where they actively engaged with 11 supervisory boards and observed their dynamics. They found that having a shared understanding of the board’s role called a common framework, improved performance by reducing relationship conflicts, encouraging task conflicts, and minimizing conflicts between the board and CEO.

10. Non-Participant Observation

Definition: Non-participant observation is a qualitative research method in which the researcher observes the phenomena of interest without actively participating in the situation, setting, or community being studied.

This method allows the researcher to maintain a position of distance, as they are solely an observer and not a participant in the activities being observed (Kawulich, 2005).

During non-participant observation, the researcher typically records field notes on the actions, interactions, and behaviors observed , focusing on specific aspects of the situation deemed relevant to the research question.

This could include verbal and nonverbal communication , activities, interactions, and environmental contexts (Angrosino, 2007). They could also use video or audio recordings or other methods to collect data.

Example of Non-Participant Observation Research

Title: Mental Health Nurses’ attitudes towards mental illness and recovery-oriented practice in acute inpatient psychiatric units: A non-participant observation study

Citation: Sreeram, A., Cross, W. M., & Townsin, L. (2023). Mental Health Nurses’ attitudes towards mental illness and recovery‐oriented practice in acute inpatient psychiatric units: A non‐participant observation study. International Journal of Mental Health Nursing .

Overview: This study investigated the attitudes of mental health nurses towards mental illness and recovery-oriented practice in acute inpatient psychiatric units. The researchers used a non-participant observation method, meaning they observed the nurses without directly participating in their activities. The findings shed light on the nurses’ perspectives and behaviors, providing valuable insights into their attitudes toward mental health and recovery-focused care in these settings.

11. Content Analysis

Definition: Content Analysis involves scrutinizing textual, visual, or spoken content to categorize and quantify information. The goal is to identify patterns, themes, biases, or other characteristics (Hsieh & Shannon, 2005).

Content Analysis is widely used in various disciplines for a multitude of purposes. Researchers typically use this method to distill large amounts of unstructured data, like interview transcripts, newspaper articles, or social media posts, into manageable and meaningful chunks.

When wielded appropriately, Content Analysis can illuminate the density and frequency of certain themes within a dataset, provide insights into how specific terms or concepts are applied contextually, and offer inferences about the meanings of their content and use (Duriau, Reger, & Pfarrer, 2007).

Example of Content Analysis

Title: Framing European politics: A content analysis of press and television news .

Citation: Semetko, H. A., & Valkenburg, P. M. (2000). Framing European politics: A content analysis of press and television news. Journal of Communication, 50 (2), 93-109.

Overview: This study analyzed press and television news articles about European politics using a method called content analysis. The researchers examined the prevalence of different “frames” in the news, which are ways of presenting information to shape audience perceptions. They found that the most common frames were attribution of responsibility, conflict, economic consequences, human interest, and morality.

Read my Full Guide on Content Analysis Here

12. Discourse Analysis

Definition: Discourse Analysis, a qualitative research method, interprets the meanings, functions, and coherence of certain languages in context.

Discourse analysis is typically understood through social constructionism, critical theory , and poststructuralism and used for understanding how language constructs social concepts (Cheek, 2004).

Discourse Analysis offers great breadth, providing tools to examine spoken or written language, often beyond the level of the sentence. It enables researchers to scrutinize how text and talk articulate social and political interactions and hierarchies.

Insight can be garnered from different conversations, institutional text, and media coverage to understand how topics are addressed or framed within a specific social context (Jorgensen & Phillips, 2002).

Example of Discourse Analysis

Title: The construction of teacher identities in educational policy documents: A critical discourse analysis

Citation: Thomas, S. (2005). The construction of teacher identities in educational policy documents: A critical discourse analysis. Critical Studies in Education, 46 (2), 25-44.

Overview: The author examines how an education policy in one state of Australia positions teacher professionalism and teacher identities. While there are competing discourses about professional identity, the policy framework privileges a  narrative that frames the ‘good’ teacher as one that accepts ever-tightening control and regulation over their professional practice.

Read my Full Guide on Discourse Analysis Here

13. Action Research

Definition: Action Research is a qualitative research technique that is employed to bring about change while simultaneously studying the process and results of that change.

This method involves a cyclical process of fact-finding, action, evaluation, and reflection (Greenwood & Levin, 2016).

Typically, Action Research is used in the fields of education, social sciences , and community development. The process isn’t just about resolving an issue but also developing knowledge that can be used in the future to address similar or related problems.

The researcher plays an active role in the research process, which is normally broken down into four steps: 

• developing a plan to improve what is currently being done
• implementing the plan
• observing the effects of the plan, and
• reflecting upon these effects (Smith, 2010).

Example of Action Research

Title: Using Digital Sandbox Gaming to Improve Creativity Within Boys’ Writing

Citation: Ellison, M., & Drew, C. (2020). Using digital sandbox gaming to improve creativity within boys’ writing. Journal of Research in Childhood Education , 34 (2), 277-287.

Overview: This was a research study one of my research students completed in his own classroom under my supervision. He implemented a digital game-based approach to literacy teaching with boys and interviewed his students to see if the use of games as stimuli for storytelling helped draw them into the learning experience.

Read my Full Guide on Action Research Here

14. Semiotic Analysis

Definition: Semiotic Analysis is a qualitative method of research that interprets signs and symbols in communication to understand sociocultural phenomena. It stems from semiotics, the study of signs and symbols and their use or interpretation (Chandler, 2017).

In a Semiotic Analysis, signs (anything that represents something else) are interpreted based on their significance and the role they play in representing ideas.

This type of research often involves the examination of images, sounds, and word choice to uncover the embedded sociocultural meanings. For example, an advertisement for a car might be studied to learn more about societal views on masculinity or success (Berger, 2010).

Example of Semiotic Research

Title: Shielding the learned body: a semiotic analysis of school badges in New South Wales, Australia

Citation: Symes, C. (2023). Shielding the learned body: a semiotic analysis of school badges in New South Wales, Australia. Semiotica , 2023 (250), 167-190.

Overview: This study examines school badges in New South Wales, Australia, and explores their significance through a semiotic analysis. The badges, which are part of the school’s visual identity, are seen as symbolic representations that convey meanings. The analysis reveals that these badges often draw on heraldic models, incorporating elements like colors, names, motifs, and mottoes that reflect local culture and history, thus connecting students to their national identity. Additionally, the study highlights how some schools have shifted from traditional badges to modern logos and slogans, reflecting a more business-oriented approach.

15. Qualitative Longitudinal Studies

Definition: Qualitative Longitudinal Studies are a research method that involves repeated observation of the same items over an extended period of time.

Unlike a snapshot perspective, this method aims to piece together individual histories and examine the influences and impacts of change (Neale, 2019).

Qualitative Longitudinal Studies provide an in-depth understanding of change as it happens, including changes in people’s lives, their perceptions, and their behaviors.

For instance, this method could be used to follow a group of students through their schooling years to understand the evolution of their learning behaviors and attitudes towards education (Saldaña, 2003).

Example of Qualitative Longitudinal Research

Title: Patient and caregiver perspectives on managing pain in advanced cancer: a qualitative longitudinal study

Citation: Hackett, J., Godfrey, M., & Bennett, M. I. (2016). Patient and caregiver perspectives on managing pain in advanced cancer: a qualitative longitudinal study.  Palliative medicine ,  30 (8), 711-719.

Overview: This article examines how patients and their caregivers manage pain in advanced cancer through a qualitative longitudinal study. The researchers interviewed patients and caregivers at two different time points and collected audio diaries to gain insights into their experiences, making this study longitudinal.

Read my Full Guide on Longitudinal Research Here

16. Open-Ended Surveys

Definition: Open-Ended Surveys are a type of qualitative research method where respondents provide answers in their own words. Unlike closed-ended surveys, which limit responses to predefined options, open-ended surveys allow for expansive and unsolicited explanations (Fink, 2013).

Open-ended surveys are commonly used in a range of fields, from market research to social studies. As they don’t force respondents into predefined response categories, these surveys help to draw out rich, detailed data that might uncover new variables or ideas.

For example, an open-ended survey might be used to understand customer opinions about a new product or service (Lavrakas, 2008).

Contrast this to a quantitative closed-ended survey, like a Likert scale, which could theoretically help us to come up with generalizable data but is restricted by the questions on the questionnaire, meaning new and surprising data and insights can’t emerge from the survey results in the same way.

Example of Open-Ended Survey Research

Title: Advantages and disadvantages of technology in relationships: Findings from an open-ended survey

Citation: Hertlein, K. M., & Ancheta, K. (2014). Advantages and disadvantages of technology in relationships: Findings from an open-ended survey.  The Qualitative Report ,  19 (11), 1-11.

Overview: This article examines the advantages and disadvantages of technology in couple relationships through an open-ended survey method. Researchers analyzed responses from 410 undergraduate students to understand how technology affects relationships. They found that technology can contribute to relationship development, management, and enhancement, but it can also create challenges such as distancing, lack of clarity, and impaired trust.

17. Naturalistic Observation

Definition: Naturalistic Observation is a type of qualitative research method that involves observing individuals in their natural environments without interference or manipulation by the researcher.

Naturalistic observation is often used when conducting research on behaviors that cannot be controlled or manipulated in a laboratory setting (Kawulich, 2005).

It is frequently used in the fields of psychology, sociology, and anthropology. For instance, to understand the social dynamics in a schoolyard, a researcher could spend time observing the children interact during their recess, noting their behaviors, interactions, and conflicts without imposing their presence on the children’s activities (Forsyth, 2010).

Example of Naturalistic Observation Research

Title: Dispositional mindfulness in daily life: A naturalistic observation study

Citation: Kaplan, D. M., Raison, C. L., Milek, A., Tackman, A. M., Pace, T. W., & Mehl, M. R. (2018). Dispositional mindfulness in daily life: A naturalistic observation study. PloS one , 13 (11), e0206029.

Overview: In this study, researchers conducted two studies: one exploring assumptions about mindfulness and behavior, and the other using naturalistic observation to examine actual behavioral manifestations of mindfulness. They found that trait mindfulness is associated with a heightened perceptual focus in conversations, suggesting that being mindful is expressed primarily through sharpened attention rather than observable behavioral or social differences.

Read my Full Guide on Naturalistic Observation Here

18. Photo-Elicitation

Definition: Photo-elicitation utilizes photographs as a means to trigger discussions and evoke responses during interviews. This strategy aids in bringing out topics of discussion that may not emerge through verbal prompting alone (Harper, 2002).

Traditionally, Photo-Elicitation has been useful in various fields such as education, psychology, and sociology. The method involves the researcher or participants taking photographs, which are then used as prompts for discussion.

For instance, a researcher studying urban environmental issues might invite participants to photograph areas in their neighborhood that they perceive as environmentally detrimental, and then discuss each photo in depth (Clark-Ibáñez, 2004).

Example of Photo-Elicitation Research

Title: Early adolescent food routines: A photo-elicitation study

Citation: Green, E. M., Spivak, C., & Dollahite, J. S. (2021). Early adolescent food routines: A photo-elicitation study. Appetite, 158 .

Overview: This study focused on early adolescents (ages 10-14) and their food routines. Researchers conducted in-depth interviews using a photo-elicitation approach, where participants took photos related to their food choices and experiences. Through analysis, the study identified various routines and three main themes: family, settings, and meals/foods consumed, revealing how early adolescents view and are influenced by their eating routines.

Features of Qualitative Research

Qualitative research is a research method focused on understanding the meaning individuals or groups attribute to a social or human problem (Creswell, 2013).

Some key features of this method include:

• Naturalistic Inquiry: Qualitative research happens in the natural setting of the phenomena, aiming to understand “real world” situations (Patton, 2015). This immersion in the field or subject allows the researcher to gather a deep understanding of the subject matter.
• Emphasis on Process: It aims to understand how events unfold over time rather than focusing solely on outcomes (Merriam & Tisdell, 2015). The process-oriented nature of qualitative research allows researchers to investigate sequences, timing, and changes.
• Interpretive: It involves interpreting and making sense of phenomena in terms of the meanings people assign to them (Denzin & Lincoln, 2011). This interpretive element allows for rich, nuanced insights into human behavior and experiences.
• Holistic Perspective: Qualitative research seeks to understand the whole phenomenon rather than focusing on individual components (Creswell, 2013). It emphasizes the complex interplay of factors, providing a richer, more nuanced view of the research subject.
• Prioritizes Depth over Breadth: Qualitative research favors depth of understanding over breadth, typically involving a smaller but more focused sample size (Hennink, Hutter, & Bailey, 2020). This enables detailed exploration of the phenomena of interest, often leading to rich and complex data.

Qualitative vs Quantitative Research

Qualitative research centers on exploring and understanding the meaning individuals or groups attribute to a social or human problem (Creswell, 2013).

It involves an in-depth approach to the subject matter, aiming to capture the richness and complexity of human experience.

Examples include conducting interviews, observing behaviors, or analyzing text and images.

There are strengths inherent in this approach. In its focus on understanding subjective experiences and interpretations, qualitative research can yield rich and detailed data that quantitative research may overlook (Denzin & Lincoln, 2011).

Additionally, qualitative research is adaptive, allowing the researcher to respond to new directions and insights as they emerge during the research process.

However, there are also limitations. Because of the interpretive nature of this research, findings may not be generalizable to a broader population (Marshall & Rossman, 2014). Well-designed quantitative research, on the other hand, can be generalizable.

Moreover, the reliability and validity of qualitative data can be challenging to establish due to its subjective nature, unlike quantitative research, which is ideally more objective.

Compare Qualitative and Quantitative Research Methodologies in This Guide Here

In conclusion, qualitative research methods provide distinctive ways to explore social phenomena and understand nuances that quantitative approaches might overlook. Each method, from Ethnography to Photo-Elicitation, presents its strengths and weaknesses but they all offer valuable means of investigating complex, real-world situations. The goal for the researcher is not to find a definitive tool, but to employ the method best suited for their research questions and the context at hand (Almalki, 2016). Above all, these methods underscore the richness of human experience and deepen our understanding of the world around us.

Angrosino, M. (2007). Doing ethnographic and observational research. Sage Publications.

Areni, C. S., & Kim, D. (1994). The influence of in-store lighting on consumers’ examination of merchandise in a wine store. International Journal of Research in Marketing, 11 (2), 117-125.

Barker, C., Pistrang, N., & Elliott, R. (2016). Research Methods in Clinical Psychology: An Introduction for Students and Practitioners. John Wiley & Sons.

Baxter, P. & Jack, S. (2008). Qualitative case study methodology: Study design and implementation for novice researchers. The Qualitative Report, 13 (4), 544-559.

Berger, A. A. (2010). The Objects of Affection: Semiotics and Consumer Culture. Palgrave Macmillan.

Bevan, M. T. (2014). A method of phenomenological interviewing. Qualitative health research, 24 (1), 136-144.

Birks, M., & Mills, J. (2015). Grounded theory: A practical guide . Sage Publications.

Bryman, A. (2015) . The SAGE Handbook of Qualitative Research. Sage Publications.

Chandler, D. (2017). Semiotics: The Basics. Routledge.

Charmaz, K. (2014). Constructing grounded theory. Sage Publications.

Cheek, J. (2004). At the margins? Discourse analysis and qualitative research. Qualitative Health Research, 14(8), 1140-1150.

Clark-Ibáñez, M. (2004). Framing the social world with photo-elicitation interviews. American Behavioral Scientist, 47(12), 1507-1527.

Creswell, J. W. (2013). Research Design: Qualitative, Quantitative and Mixed Methods Approaches. Sage Publications.

Creswell, J. W., & Creswell, J. D. (2017). Research design: Qualitative, quantitative, and mixed methods approaches. Sage publications.

Crowe, S., Cresswell, K., Robertson, A., Huby, G., Avery, A., & Sheikh, A. (2011). The case study approach. BMC Medical Research Methodology, 11(100), 1-9.

Denzin, N. K., & Lincoln, Y. S. (2011). The Sage Handbook of Qualitative Research. Sage.

Dewalt, K. M., & Dewalt, B. R. (2011). Participant observation: A guide for fieldworkers. Rowman Altamira.

Doody, O., Slevin, E., & Taggart, L. (2013). Focus group interviews in nursing research: part 1. British Journal of Nursing, 22(1), 16-19.

Durham, A. (2019). Autoethnography. In P. Atkinson (Ed.), Qualitative Research Methods. Oxford University Press.

Duriau, V. J., Reger, R. K., & Pfarrer, M. D. (2007). A content analysis of the content analysis literature in organization studies: Research themes, data sources, and methodological refinements. Organizational Research Methods, 10(1), 5-34.

Evans, J. (2010). The Everyday Lives of Men: An Ethnographic Investigation of Young Adult Male Identity. Peter Lang.

Farrall, S. (2006). What is qualitative longitudinal research? Papers in Social Research Methods, Qualitative Series, No.11, London School of Economics, Methodology Institute.

Fielding, J., & Fielding, N. (2008). Synergy and synthesis: integrating qualitative and quantitative data. The SAGE handbook of social research methods, 555-571.

Fink, A. (2013). How to conduct surveys: A step-by-step guide . SAGE.

Forsyth, D. R. (2010). Group Dynamics . Wadsworth Cengage Learning.

Fugard, A. J. B., & Potts, H. W. W. (2015). Supporting thinking on sample sizes for thematic analyses: A quantitative tool. International Journal of Social Research Methodology, 18 (6), 669–684.

Glaser, B. G., & Strauss, A. L. (1967). The discovery of grounded theory: Strategies for qualitative research. Aldine de Gruyter.

Gray, J. R., Grove, S. K., & Sutherland, S. (2017). Burns and Grove’s the Practice of Nursing Research E-Book: Appraisal, Synthesis, and Generation of Evidence. Elsevier Health Sciences.

Greenwood, D. J., & Levin, M. (2016). Introduction to action research: Social research for social change. SAGE.

Harper, D. (2002). Talking about pictures: A case for photo elicitation. Visual Studies, 17 (1), 13-26.

Heinonen, T. (2012). Making Sense of the Social: Human Sciences and the Narrative Turn. Rozenberg Publishers.

Heisley, D. D., & Levy, S. J. (1991). Autodriving: A photoelicitation technique. Journal of Consumer Research, 18 (3), 257-272.

Hennink, M. M., Hutter, I., & Bailey, A. (2020). Qualitative Research Methods . SAGE Publications Ltd.

Hsieh, H. F., & Shannon, S. E. (2005). Three Approaches to Qualitative Content Analysis. Qualitative Health Research, 15 (9), 1277–1288.

Jorgensen, D. L. (2015). Participant Observation. In Emerging Trends in the Social and Behavioral Sciences: An Interdisciplinary, Searchable, and Linkable Resource. John Wiley & Sons, Inc.

Jorgensen, M., & Phillips, L. (2002). Discourse Analysis as Theory and Method . SAGE.

Josselson, R. (2011). Narrative research: Constructing, deconstructing, and reconstructing story. In Five ways of doing qualitative analysis . Guilford Press.

Kawulich, B. B. (2005). Participant observation as a data collection method. Forum: Qualitative Social Research, 6 (2).

Khan, S. (2014). Qualitative Research Method: Grounded Theory. Journal of Basic and Clinical Pharmacy, 5 (4), 86-88.

Koshy, E., Koshy, V., & Waterman, H. (2010). Action Research in Healthcare . SAGE.

Krippendorff, K. (2013). Content Analysis: An Introduction to its Methodology. SAGE.

Lannon, J., & Cooper, P. (2012). Humanistic Advertising: A Holistic Cultural Perspective. International Journal of Advertising, 15 (2), 97–111.

Lavrakas, P. J. (2008). Encyclopedia of survey research methods. SAGE Publications.

Lieblich, A., Tuval-Mashiach, R., & Zilber, T. (2008). Narrative research: Reading, analysis and interpretation. Sage Publications.

Mackey, A., & Gass, S. M. (2015). Second language research: Methodology and design. Routledge.

Marshall, C., & Rossman, G. B. (2014). Designing qualitative research. Sage publications.

McAdams, D. P., Josselson, R., & Lieblich, A. (2006). Identity and story: Creating self in narrative. American Psychological Association.

Merriam, S. B., & Tisdell, E. J. (2015). Qualitative Research: A Guide to Design and Implementation. Jossey-Bass.

Mick, D. G. (1986). Consumer Research and Semiotics: Exploring the Morphology of Signs, Symbols, and Significance. Journal of Consumer Research, 13 (2), 196-213.

Morgan, D. L. (2010). Focus groups as qualitative research. Sage Publications.

Mulhall, A. (2003). In the field: notes on observation in qualitative research. Journal of Advanced Nursing, 41 (3), 306-313.

Neale, B. (2019). What is Qualitative Longitudinal Research? Bloomsbury Publishing.

Nolan, L. B., & Renderos, T. B. (2012). A focus group study on the influence of fatalism and religiosity on cancer risk perceptions in rural, eastern North Carolina. Journal of religion and health, 51 (1), 91-104.

Padilla-Díaz, M. (2015). Phenomenology in educational qualitative research: Philosophy as science or philosophical science? International Journal of Educational Excellence, 1 (2), 101-110.

Parker, I. (2014). Discourse dynamics: Critical analysis for social and individual psychology . Routledge.

Patton, M. Q. (2015). Qualitative research & evaluation methods: Integrating theory and practice . Sage Publications.

Polkinghorne, D. E. (2013). Narrative configuration in qualitative analysis. In Life history and narrative. Routledge.

Puts, M. T., Tapscott, B., Fitch, M., Howell, D., Monette, J., Wan-Chow-Wah, D., Krzyzanowska, M., Leighl, N. B., Springall, E., & Alibhai, S. (2014). Factors influencing adherence to cancer treatment in older adults with cancer: a systematic review. Annals of oncology, 25 (3), 564-577.

Qu, S. Q., & Dumay, J. (2011). The qualitative research interview . Qualitative research in accounting & management.

Ali, J., & Bhaskar, S. B. (2016). Basic statistical tools in research and data analysis. Indian Journal of Anaesthesia, 60 (9), 662–669.

Rosenbaum, M. S. (2017). Exploring the social supportive role of third places in consumers’ lives. Journal of Service Research, 20 (1), 26-42.

Saldaña, J. (2003). Longitudinal Qualitative Research: Analyzing Change Through Time . AltaMira Press.

Saldaña, J. (2014). The Coding Manual for Qualitative Researchers. SAGE.

Shernoff, D. J., Csikszentmihalyi, M., Shneider, B., & Shernoff, E. S. (2003). Student engagement in high school classrooms from the perspective of flow theory. School Psychology Quarterly, 18 (2), 158-176.

Smith, J. A. (2015). Qualitative Psychology: A Practical Guide to Research Methods . Sage Publications.

Smith, M. K. (2010). Action Research. The encyclopedia of informal education.

Sue, V. M., & Ritter, L. A. (2012). Conducting online surveys . SAGE Publications.

Van Auken, P. M., Frisvoll, S. J., & Stewart, S. I. (2010). Visualising community: using participant-driven photo-elicitation for research and application. Local Environment, 15 (4), 373-388.

Van Voorhis, F. L., & Morgan, B. L. (2007). Understanding Power and Rules of Thumb for Determining Sample Sizes. Tutorials in Quantitative Methods for Psychology, 3 (2), 43–50.

Wodak, R., & Meyer, M. (2015). Methods of Critical Discourse Analysis . SAGE.

Zuber-Skerritt, O. (2018). Action research for developing educational theories and practices . Routledge.

Chris Drew (PhD)

Dr. Chris Drew is the founder of the Helpful Professor. He holds a PhD in education and has published over 20 articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education. [Image Descriptor: Photo of Chris]

• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd/ 5 Top Tips for Succeeding at University
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd/ 50 Durable Goods Examples
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd/ 100 Consumer Goods Examples
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd/ 30 Globalization Pros and Cons

Leave a Comment Cancel Reply

Your email address will not be published. Required fields are marked *

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, automatically generate references for free.

• Knowledge Base
• Methodology
• What Is Qualitative Research? | Methods & Examples

What Is Qualitative Research? | Methods & Examples

Published on 4 April 2022 by Pritha Bhandari . Revised on 30 January 2023.

Qualitative research involves collecting and analysing non-numerical data (e.g., text, video, or audio) to understand concepts, opinions, or experiences. It can be used to gather in-depth insights into a problem or generate new ideas for research.

Qualitative research is the opposite of quantitative research , which involves collecting and analysing numerical data for statistical analysis.

Qualitative research is commonly used in the humanities and social sciences, in subjects such as anthropology, sociology, education, health sciences, and history.

• How does social media shape body image in teenagers?
• How do children and adults interpret healthy eating in the UK?
• What factors influence employee retention in a large organisation?
• How is anxiety experienced around the world?
• How can teachers integrate social issues into science curriculums?

Table of contents

Approaches to qualitative research, qualitative research methods, qualitative data analysis, advantages of qualitative research, disadvantages of qualitative research, frequently asked questions about qualitative research.

Qualitative research is used to understand how people experience the world. While there are many approaches to qualitative research, they tend to be flexible and focus on retaining rich meaning when interpreting data.

Common approaches include grounded theory, ethnography, action research, phenomenological research, and narrative research. They share some similarities, but emphasise different aims and perspectives.

Prevent plagiarism, run a free check.

Each of the research approaches involve using one or more data collection methods . These are some of the most common qualitative methods:

• Observations: recording what you have seen, heard, or encountered in detailed field notes.
• Interviews:  personally asking people questions in one-on-one conversations.
• Focus groups: asking questions and generating discussion among a group of people.
• Surveys : distributing questionnaires with open-ended questions.
• Secondary research: collecting existing data in the form of texts, images, audio or video recordings, etc.
• You take field notes with observations and reflect on your own experiences of the company culture.
• You distribute open-ended surveys to employees across all the company’s offices by email to find out if the culture varies across locations.
• You conduct in-depth interviews with employees in your office to learn about their experiences and perspectives in greater detail.

Qualitative researchers often consider themselves ‘instruments’ in research because all observations, interpretations and analyses are filtered through their own personal lens.

For this reason, when writing up your methodology for qualitative research, it’s important to reflect on your approach and to thoroughly explain the choices you made in collecting and analysing the data.

Qualitative data can take the form of texts, photos, videos and audio. For example, you might be working with interview transcripts, survey responses, fieldnotes, or recordings from natural settings.

Most types of qualitative data analysis share the same five steps:

• Prepare and organise your data. This may mean transcribing interviews or typing up fieldnotes.
• Review and explore your data. Examine the data for patterns or repeated ideas that emerge.
• Develop a data coding system. Based on your initial ideas, establish a set of codes that you can apply to categorise your data.
• Assign codes to the data. For example, in qualitative survey analysis, this may mean going through each participant’s responses and tagging them with codes in a spreadsheet. As you go through your data, you can create new codes to add to your system if necessary.
• Identify recurring themes. Link codes together into cohesive, overarching themes.

There are several specific approaches to analysing qualitative data. Although these methods share similar processes, they emphasise different concepts.

Qualitative research often tries to preserve the voice and perspective of participants and can be adjusted as new research questions arise. Qualitative research is good for:

• Flexibility

The data collection and analysis process can be adapted as new ideas or patterns emerge. They are not rigidly decided beforehand.

• Natural settings

Data collection occurs in real-world contexts or in naturalistic ways.

• Meaningful insights

Detailed descriptions of people’s experiences, feelings and perceptions can be used in designing, testing or improving systems or products.

• Generation of new ideas

Open-ended responses mean that researchers can uncover novel problems or opportunities that they wouldn’t have thought of otherwise.

Researchers must consider practical and theoretical limitations in analysing and interpreting their data. Qualitative research suffers from:

• Unreliability

The real-world setting often makes qualitative research unreliable because of uncontrolled factors that affect the data.

• Subjectivity

Due to the researcher’s primary role in analysing and interpreting data, qualitative research cannot be replicated . The researcher decides what is important and what is irrelevant in data analysis, so interpretations of the same data can vary greatly.

• Limited generalisability

Small samples are often used to gather detailed data about specific contexts. Despite rigorous analysis procedures, it is difficult to draw generalisable conclusions because the data may be biased and unrepresentative of the wider population .

• Labour-intensive

Although software can be used to manage and record large amounts of text, data analysis often has to be checked or performed manually.

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to test a hypothesis by systematically collecting and analysing data, while qualitative methods allow you to explore ideas and experiences in depth.

There are five common approaches to qualitative research :

• Grounded theory involves collecting data in order to develop new theories.
• Ethnography involves immersing yourself in a group or organisation to understand its culture.
• Narrative research involves interpreting stories to understand how people make sense of their experiences and perceptions.
• Phenomenological research involves investigating phenomena through people’s lived experiences.
• Action research links theory and practice in several cycles to drive innovative changes.

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organisations.

There are various approaches to qualitative data analysis , but they all share five steps in common:

• Prepare and organise your data.
• Review and explore your data.
• Develop a data coding system.
• Assign codes to the data.
• Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the ‘Cite this Scribbr article’ button to automatically add the citation to our free Reference Generator.

Bhandari, P. (2023, January 30). What Is Qualitative Research? | Methods & Examples. Scribbr. Retrieved 22 April 2024, from https://www.scribbr.co.uk/research-methods/introduction-to-qualitative-research/

Is this article helpful?

Pritha Bhandari

An official website of the United States government

The .gov means it's official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you're on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings
• Browse Titles

NCBI Bookshelf. A service of the National Library of Medicine, National Institutes of Health.

StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2024 Jan-.

StatPearls [Internet].

Qualitative study.

Steven Tenny ; Janelle M. Brannan ; Grace D. Brannan .

Affiliations

Last Update: September 18, 2022 .

• Introduction

Qualitative research is a type of research that explores and provides deeper insights into real-world problems. [1] Instead of collecting numerical data points or intervene or introduce treatments just like in quantitative research, qualitative research helps generate hypotheses as well as further investigate and understand quantitative data. Qualitative research gathers participants' experiences, perceptions, and behavior. It answers the hows and whys instead of how many or how much. It could be structured as a stand-alone study, purely relying on qualitative data or it could be part of mixed-methods research that combines qualitative and quantitative data. This review introduces the readers to some basic concepts, definitions, terminology, and application of qualitative research.

Qualitative research at its core, ask open-ended questions whose answers are not easily put into numbers such as ‘how’ and ‘why’. [2] Due to the open-ended nature of the research questions at hand, qualitative research design is often not linear in the same way quantitative design is. [2] One of the strengths of qualitative research is its ability to explain processes and patterns of human behavior that can be difficult to quantify. [3] Phenomena such as experiences, attitudes, and behaviors can be difficult to accurately capture quantitatively, whereas a qualitative approach allows participants themselves to explain how, why, or what they were thinking, feeling, and experiencing at a certain time or during an event of interest. Quantifying qualitative data certainly is possible, but at its core, qualitative data is looking for themes and patterns that can be difficult to quantify and it is important to ensure that the context and narrative of qualitative work are not lost by trying to quantify something that is not meant to be quantified.

However, while qualitative research is sometimes placed in opposition to quantitative research, where they are necessarily opposites and therefore ‘compete’ against each other and the philosophical paradigms associated with each, qualitative and quantitative work are not necessarily opposites nor are they incompatible. [4] While qualitative and quantitative approaches are different, they are not necessarily opposites, and they are certainly not mutually exclusive. For instance, qualitative research can help expand and deepen understanding of data or results obtained from quantitative analysis. For example, say a quantitative analysis has determined that there is a correlation between length of stay and level of patient satisfaction, but why does this correlation exist? This dual-focus scenario shows one way in which qualitative and quantitative research could be integrated together.

Examples of Qualitative Research Approaches

Ethnography

Ethnography as a research design has its origins in social and cultural anthropology, and involves the researcher being directly immersed in the participant’s environment. [2] Through this immersion, the ethnographer can use a variety of data collection techniques with the aim of being able to produce a comprehensive account of the social phenomena that occurred during the research period. [2] That is to say, the researcher’s aim with ethnography is to immerse themselves into the research population and come out of it with accounts of actions, behaviors, events, etc. through the eyes of someone involved in the population. Direct involvement of the researcher with the target population is one benefit of ethnographic research because it can then be possible to find data that is otherwise very difficult to extract and record.

Grounded Theory

Grounded Theory is the “generation of a theoretical model through the experience of observing a study population and developing a comparative analysis of their speech and behavior.” [5] As opposed to quantitative research which is deductive and tests or verifies an existing theory, grounded theory research is inductive and therefore lends itself to research that is aiming to study social interactions or experiences. [3] [2] In essence, Grounded Theory’s goal is to explain for example how and why an event occurs or how and why people might behave a certain way. Through observing the population, a researcher using the Grounded Theory approach can then develop a theory to explain the phenomena of interest.

Phenomenology

Phenomenology is defined as the “study of the meaning of phenomena or the study of the particular”. [5] At first glance, it might seem that Grounded Theory and Phenomenology are quite similar, but upon careful examination, the differences can be seen. At its core, phenomenology looks to investigate experiences from the perspective of the individual. [2] Phenomenology is essentially looking into the ‘lived experiences’ of the participants and aims to examine how and why participants behaved a certain way, from their perspective . Herein lies one of the main differences between Grounded Theory and Phenomenology. Grounded Theory aims to develop a theory for social phenomena through an examination of various data sources whereas Phenomenology focuses on describing and explaining an event or phenomena from the perspective of those who have experienced it.

Narrative Research

One of qualitative research’s strengths lies in its ability to tell a story, often from the perspective of those directly involved in it. Reporting on qualitative research involves including details and descriptions of the setting involved and quotes from participants. This detail is called ‘thick’ or ‘rich’ description and is a strength of qualitative research. Narrative research is rife with the possibilities of ‘thick’ description as this approach weaves together a sequence of events, usually from just one or two individuals, in the hopes of creating a cohesive story, or narrative. [2] While it might seem like a waste of time to focus on such a specific, individual level, understanding one or two people’s narratives for an event or phenomenon can help to inform researchers about the influences that helped shape that narrative. The tension or conflict of differing narratives can be “opportunities for innovation”. [2]

Research Paradigm

Research paradigms are the assumptions, norms, and standards that underpin different approaches to research. Essentially, research paradigms are the ‘worldview’ that inform research. [4] It is valuable for researchers, both qualitative and quantitative, to understand what paradigm they are working within because understanding the theoretical basis of research paradigms allows researchers to understand the strengths and weaknesses of the approach being used and adjust accordingly. Different paradigms have different ontology and epistemologies . Ontology is defined as the "assumptions about the nature of reality” whereas epistemology is defined as the “assumptions about the nature of knowledge” that inform the work researchers do. [2] It is important to understand the ontological and epistemological foundations of the research paradigm researchers are working within to allow for a full understanding of the approach being used and the assumptions that underpin the approach as a whole. Further, it is crucial that researchers understand their own ontological and epistemological assumptions about the world in general because their assumptions about the world will necessarily impact how they interact with research. A discussion of the research paradigm is not complete without describing positivist, postpositivist, and constructivist philosophies.

Positivist vs Postpositivist

To further understand qualitative research, we need to discuss positivist and postpositivist frameworks. Positivism is a philosophy that the scientific method can and should be applied to social as well as natural sciences. [4] Essentially, positivist thinking insists that the social sciences should use natural science methods in its research which stems from positivist ontology that there is an objective reality that exists that is fully independent of our perception of the world as individuals. Quantitative research is rooted in positivist philosophy, which can be seen in the value it places on concepts such as causality, generalizability, and replicability.

Conversely, postpositivists argue that social reality can never be one hundred percent explained but it could be approximated. [4] Indeed, qualitative researchers have been insisting that there are “fundamental limits to the extent to which the methods and procedures of the natural sciences could be applied to the social world” and therefore postpositivist philosophy is often associated with qualitative research. [4] An example of positivist versus postpositivist values in research might be that positivist philosophies value hypothesis-testing, whereas postpositivist philosophies value the ability to formulate a substantive theory.

Constructivist

Constructivism is a subcategory of postpositivism. Most researchers invested in postpositivist research are constructivist as well, meaning they think there is no objective external reality that exists but rather that reality is constructed. Constructivism is a theoretical lens that emphasizes the dynamic nature of our world. “Constructivism contends that individuals’ views are directly influenced by their experiences, and it is these individual experiences and views that shape their perspective of reality”. [6] Essentially, Constructivist thought focuses on how ‘reality’ is not a fixed certainty and experiences, interactions, and backgrounds give people a unique view of the world. Constructivism contends, unlike in positivist views, that there is not necessarily an ‘objective’ reality we all experience. This is the ‘relativist’ ontological view that reality and the world we live in are dynamic and socially constructed. Therefore, qualitative scientific knowledge can be inductive as well as deductive.” [4]

So why is it important to understand the differences in assumptions that different philosophies and approaches to research have? Fundamentally, the assumptions underpinning the research tools a researcher selects provide an overall base for the assumptions the rest of the research will have and can even change the role of the researcher themselves. [2] For example, is the researcher an ‘objective’ observer such as in positivist quantitative work? Or is the researcher an active participant in the research itself, as in postpositivist qualitative work? Understanding the philosophical base of the research undertaken allows researchers to fully understand the implications of their work and their role within the research, as well as reflect on their own positionality and bias as it pertains to the research they are conducting.

Data Sampling 

The better the sample represents the intended study population, the more likely the researcher is to encompass the varying factors at play. The following are examples of participant sampling and selection: [7]

• Purposive sampling- selection based on the researcher’s rationale in terms of being the most informative.
• Criterion sampling-selection based on pre-identified factors.
• Convenience sampling- selection based on availability.
• Snowball sampling- the selection is by referral from other participants or people who know potential participants.
• Extreme case sampling- targeted selection of rare cases.
• Typical case sampling-selection based on regular or average participants. 

Data Collection and Analysis

Qualitative research uses several techniques including interviews, focus groups, and observation. [1] [2] [3] Interviews may be unstructured, with open-ended questions on a topic and the interviewer adapts to the responses. Structured interviews have a predetermined number of questions that every participant is asked. It is usually one on one and is appropriate for sensitive topics or topics needing an in-depth exploration. Focus groups are often held with 8-12 target participants and are used when group dynamics and collective views on a topic are desired. Researchers can be a participant-observer to share the experiences of the subject or a non-participant or detached observer.

While quantitative research design prescribes a controlled environment for data collection, qualitative data collection may be in a central location or in the environment of the participants, depending on the study goals and design. Qualitative research could amount to a large amount of data. Data is transcribed which may then be coded manually or with the use of Computer Assisted Qualitative Data Analysis Software or CAQDAS such as ATLAS.ti or NVivo. [8] [9] [10]

After the coding process, qualitative research results could be in various formats. It could be a synthesis and interpretation presented with excerpts from the data. [11] Results also could be in the form of themes and theory or model development.

Dissemination

To standardize and facilitate the dissemination of qualitative research outcomes, the healthcare team can use two reporting standards. The Consolidated Criteria for Reporting Qualitative Research or COREQ is a 32-item checklist for interviews and focus groups. [12] The Standards for Reporting Qualitative Research (SRQR) is a checklist covering a wider range of qualitative research. [13]

Examples of Application

Many times a research question will start with qualitative research. The qualitative research will help generate the research hypothesis which can be tested with quantitative methods. After the data is collected and analyzed with quantitative methods, a set of qualitative methods can be used to dive deeper into the data for a better understanding of what the numbers truly mean and their implications. The qualitative methods can then help clarify the quantitative data and also help refine the hypothesis for future research. Furthermore, with qualitative research researchers can explore subjects that are poorly studied with quantitative methods. These include opinions, individual's actions, and social science research.

A good qualitative study design starts with a goal or objective. This should be clearly defined or stated. The target population needs to be specified. A method for obtaining information from the study population must be carefully detailed to ensure there are no omissions of part of the target population. A proper collection method should be selected which will help obtain the desired information without overly limiting the collected data because many times, the information sought is not well compartmentalized or obtained. Finally, the design should ensure adequate methods for analyzing the data. An example may help better clarify some of the various aspects of qualitative research.

A researcher wants to decrease the number of teenagers who smoke in their community. The researcher could begin by asking current teen smokers why they started smoking through structured or unstructured interviews (qualitative research). The researcher can also get together a group of current teenage smokers and conduct a focus group to help brainstorm factors that may have prevented them from starting to smoke (qualitative research).

In this example, the researcher has used qualitative research methods (interviews and focus groups) to generate a list of ideas of both why teens start to smoke as well as factors that may have prevented them from starting to smoke. Next, the researcher compiles this data. The research found that, hypothetically, peer pressure, health issues, cost, being considered “cool,” and rebellious behavior all might increase or decrease the likelihood of teens starting to smoke.

The researcher creates a survey asking teen participants to rank how important each of the above factors is in either starting smoking (for current smokers) or not smoking (for current non-smokers). This survey provides specific numbers (ranked importance of each factor) and is thus a quantitative research tool.

The researcher can use the results of the survey to focus efforts on the one or two highest-ranked factors. Let us say the researcher found that health was the major factor that keeps teens from starting to smoke, and peer pressure was the major factor that contributed to teens to start smoking. The researcher can go back to qualitative research methods to dive deeper into each of these for more information. The researcher wants to focus on how to keep teens from starting to smoke, so they focus on the peer pressure aspect.

The researcher can conduct interviews and/or focus groups (qualitative research) about what types and forms of peer pressure are commonly encountered, where the peer pressure comes from, and where smoking first starts. The researcher hypothetically finds that peer pressure often occurs after school at the local teen hangouts, mostly the local park. The researcher also hypothetically finds that peer pressure comes from older, current smokers who provide the cigarettes.

The researcher could further explore this observation made at the local teen hangouts (qualitative research) and take notes regarding who is smoking, who is not, and what observable factors are at play for peer pressure of smoking. The researcher finds a local park where many local teenagers hang out and see that a shady, overgrown area of the park is where the smokers tend to hang out. The researcher notes the smoking teenagers buy their cigarettes from a local convenience store adjacent to the park where the clerk does not check identification before selling cigarettes. These observations fall under qualitative research.

If the researcher returns to the park and counts how many individuals smoke in each region of the park, this numerical data would be quantitative research. Based on the researcher's efforts thus far, they conclude that local teen smoking and teenagers who start to smoke may decrease if there are fewer overgrown areas of the park and the local convenience store does not sell cigarettes to underage individuals.

The researcher could try to have the parks department reassess the shady areas to make them less conducive to the smokers or identify how to limit the sales of cigarettes to underage individuals by the convenience store. The researcher would then cycle back to qualitative methods of asking at-risk population their perceptions of the changes, what factors are still at play, as well as quantitative research that includes teen smoking rates in the community, the incidence of new teen smokers, among others. [14] [15]

Qualitative research functions as a standalone research design or in combination with quantitative research to enhance our understanding of the world. Qualitative research uses techniques including structured and unstructured interviews, focus groups, and participant observation to not only help generate hypotheses which can be more rigorously tested with quantitative research but also to help researchers delve deeper into the quantitative research numbers, understand what they mean, and understand what the implications are.  Qualitative research provides researchers with a way to understand what is going on, especially when things are not easily categorized. [16]

• Issues of Concern

As discussed in the sections above, quantitative and qualitative work differ in many different ways, including the criteria for evaluating them. There are four well-established criteria for evaluating quantitative data: internal validity, external validity, reliability, and objectivity. The correlating concepts in qualitative research are credibility, transferability, dependability, and confirmability. [4] [11] The corresponding quantitative and qualitative concepts can be seen below, with the quantitative concept is on the left, and the qualitative concept is on the right:

• Internal validity--- Credibility
• External validity---Transferability
• Reliability---Dependability
• Objectivity---Confirmability

In conducting qualitative research, ensuring these concepts are satisfied and well thought out can mitigate potential issues from arising. For example, just as a researcher will ensure that their quantitative study is internally valid so should qualitative researchers ensure that their work has credibility.  

Indicators such as triangulation and peer examination can help evaluate the credibility of qualitative work.

• Triangulation: Triangulation involves using multiple methods of data collection to increase the likelihood of getting a reliable and accurate result. In our above magic example, the result would be more reliable by also interviewing the magician, back-stage hand, and the person who "vanished." In qualitative research, triangulation can include using telephone surveys, in-person surveys, focus groups, and interviews as well as surveying an adequate cross-section of the target demographic.
• Peer examination: Results can be reviewed by a peer to ensure the data is consistent with the findings.

‘Thick’ or ‘rich’ description can be used to evaluate the transferability of qualitative research whereas using an indicator such as an audit trail might help with evaluating the dependability and confirmability.

• Thick or rich description is a detailed and thorough description of details, the setting, and quotes from participants in the research. [5] Thick descriptions will include a detailed explanation of how the study was carried out. Thick descriptions are detailed enough to allow readers to draw conclusions and interpret the data themselves, which can help with transferability and replicability.
• Audit trail: An audit trail provides a documented set of steps of how the participants were selected and the data was collected. The original records of information should also be kept (e.g., surveys, notes, recordings).

One issue of concern that qualitative researchers should take into consideration is observation bias. Here are a few examples:

• Hawthorne effect: The Hawthorne effect is the change in participant behavior when they know they are being observed. If a researcher was wanting to identify factors that contribute to employee theft and tells the employees they are going to watch them to see what factors affect employee theft, one would suspect employee behavior would change when they know they are being watched.
• Observer-expectancy effect: Some participants change their behavior or responses to satisfy the researcher's desired effect. This happens in an unconscious manner for the participant so it is important to eliminate or limit transmitting the researcher's views.
• Artificial scenario effect: Some qualitative research occurs in artificial scenarios and/or with preset goals. In such situations, the information may not be accurate because of the artificial nature of the scenario. The preset goals may limit the qualitative information obtained.
• Clinical Significance

Qualitative research by itself or combined with quantitative research helps healthcare providers understand patients and the impact and challenges of the care they deliver. Qualitative research provides an opportunity to generate and refine hypotheses and delve deeper into the data generated by quantitative research. Qualitative research does not exist as an island apart from quantitative research, but as an integral part of research methods to be used for the understanding of the world around us. [17]

• Enhancing Healthcare Team Outcomes

Qualitative research is important for all members of the health care team as all are affected by qualitative research. Qualitative research may help develop a theory or a model for health research that can be further explored by quantitative research.  Much of the qualitative research data acquisition is completed by numerous team members including social works, scientists, nurses, etc.  Within each area of the medical field, there is copious ongoing qualitative research including physician-patient interactions, nursing-patient interactions, patient-environment interactions, health care team function, patient information delivery, etc. 

• Review Questions
• Access free multiple choice questions on this topic.
• Comment on this article.

Disclosure: Steven Tenny declares no relevant financial relationships with ineligible companies.

Disclosure: Janelle Brannan declares no relevant financial relationships with ineligible companies.

Disclosure: Grace Brannan declares no relevant financial relationships with ineligible companies.

This book is distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) ( http://creativecommons.org/licenses/by-nc-nd/4.0/ ), which permits others to distribute the work, provided that the article is not altered or used commercially. You are not required to obtain permission to distribute this article, provided that you credit the author and journal.

• Cite this Page Tenny S, Brannan JM, Brannan GD. Qualitative Study. [Updated 2022 Sep 18]. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2024 Jan-.

In this Page

Bulk download.

• Bulk download StatPearls data from FTP

Related information

• PMC PubMed Central citations
• PubMed Links to PubMed

Similar articles in PubMed

• Suicidal Ideation. [StatPearls. 2024] Suicidal Ideation. Harmer B, Lee S, Duong TVH, Saadabadi A. StatPearls. 2024 Jan
• Folic acid supplementation and malaria susceptibility and severity among people taking antifolate antimalarial drugs in endemic areas. [Cochrane Database Syst Rev. 2022] Folic acid supplementation and malaria susceptibility and severity among people taking antifolate antimalarial drugs in endemic areas. Crider K, Williams J, Qi YP, Gutman J, Yeung L, Mai C, Finkelstain J, Mehta S, Pons-Duran C, Menéndez C, et al. Cochrane Database Syst Rev. 2022 Feb 1; 2(2022). Epub 2022 Feb 1.
• Macromolecular crowding: chemistry and physics meet biology (Ascona, Switzerland, 10-14 June 2012). [Phys Biol. 2013] Macromolecular crowding: chemistry and physics meet biology (Ascona, Switzerland, 10-14 June 2012). Foffi G, Pastore A, Piazza F, Temussi PA. Phys Biol. 2013 Aug; 10(4):040301. Epub 2013 Aug 2.
• Review Evidence Brief: The Effectiveness Of Mandatory Computer-Based Trainings On Government Ethics, Workplace Harassment, Or Privacy And Information Security-Related Topics [ 2014] Review Evidence Brief: The Effectiveness Of Mandatory Computer-Based Trainings On Government Ethics, Workplace Harassment, Or Privacy And Information Security-Related Topics Peterson K, McCleery E. 2014 May
• Review Public sector reforms and their impact on the level of corruption: A systematic review. [Campbell Syst Rev. 2021] Review Public sector reforms and their impact on the level of corruption: A systematic review. Mugellini G, Della Bella S, Colagrossi M, Isenring GL, Killias M. Campbell Syst Rev. 2021 Jun; 17(2):e1173. Epub 2021 May 24.

Recent Activity

• Qualitative Study - StatPearls Qualitative Study - StatPearls

Your browsing activity is empty.

Activity recording is turned off.

Turn recording back on

Connect with NLM

National Library of Medicine 8600 Rockville Pike Bethesda, MD 20894

Web Policies FOIA HHS Vulnerability Disclosure

Help Accessibility Careers

• (855) 776-7763

Training Maker

All Products

Qualaroo Insights

ProProfs.com

• Sign Up Free

Do you want a free Survey Software?

We have the #1 Online Survey Maker Software to get actionable user insights.

Qualitative Research Methods: Types, Examples, and Analysis

In a universe swarming with data, numbers, and algorithms, lies a lesser-known realm where emotions, stories, and intimate revelations take center stage. When you want to get inside your customers’ heads to understand their thoughts, feelings, perceptions, beliefs, and emotions, numbers are unlikely to provide a complete picture.

Let’s set the scene: picture a cozy bakery buzzing with conversations. People from different walks of life gather, each carrying a unique story to tell. You observe that your sale of pancakes is more than that of pastries, numerical data will tell you that much. But numbers won’t tell you why.

This is exactly where qualitative surveys come into play; they take you right to the heart of people’s minds and experiences – the “why” behind the statistics.

Quantitative data may offer a bird’s-eye view of the crowd, but qualitative surveys open the doorways to your audience’s individual tales. In this blog, we are going to explore qualitative research, its types, analytical procedures, positive and negative aspects, and examples.

Here we go!

What Is Qualitative Research?

Qualitative research is a branch of market research that involves collecting and analyzing qualitative data through open-ended communication. The primary purpose of conducting qualitative research is to understand the individual’s thoughts, feelings, opinions, and reasons behind these emotions.

It is used to gather in-depth and rich insights into a particular topic. Understanding how your audience feels about a specific subject helps make informed decisions in research.

As opposed to quantitative research, qualitative research does not deal with the collection of numerical data for statistical analysis. The application of this research method is usually found in humanities and social science subjects like sociology, history, anthropology, health science, education, etc.

Types of Qualitative Research Methods

Qualitative research methods are designed to understand the behavior and perception of the target audience about a particular subject.

Qualitative research methods include observations, one-on-one interviews, case study research, focus groups, ethnographic research, phenomenology, and grounded theory.

Let’s discuss them one by one.

1. Observations

Observation is one of the oldest qualitative methods of research used to collect systematic data using subjective methodologies. It is based on five primary sense organs – smell, sight, taste, touch, and hearing, and their functioning. This method focuses on characteristics and qualities rather than numbers.

The qualitative observation technique involves observing the interaction patterns in a particular situation. Researchers collect data by closely watching the behaviors of others. They rely on their ability to observe the target audience rather than communicating with people about their thoughts on a particular subject.

2. One-on-One Interviews

Conducting one-on-one interviews is one of the most common types of qualitative research methods. Although both open-ended and closed-ended questions can be a part of these interviews, open-ended conservation between researchers and participants related to a particular subject is still the preferred mode of communication. This is to gather in-depth qualitative data for the research purpose.

Here, the researcher asks pre-determined questions to the participants to collect specific information about their research topic. Interviews can be conducted face-to-face, by email, or by phone. The drawback of this method is that sometimes the participants feel uncomfortable sharing honest answers with the researcher.

3. Focus Groups

A Focus group involves collecting qualitative data by conducting a group discussion of 6-12 members along with a moderator related to a particular subject. Here the moderator asks respondents a set of predetermined questions so that they can interact with each other and form a group discussion. It helps researchers to collect rich qualitative data about their market research.

However, it is essential to ensure that the moderator asks open-ended questions like “how,” “what,” and “why” that will enable participants to share their thoughts and feelings.

Close-ended questions like “yes” and “no” should be avoided as they do not lead to engagement among participants.

4. Case Study Research

A case study is another example of qualitative research that involves a comprehensive examination of a particular subject, person, or event.

This method is used to obtain in-depth data and complete knowledge of the subject. The data is collected from various sources like interviews and observation to supplement the conclusion.

This qualitative approach is extensively used in the field of social sciences, law, business, and health. Many companies use this technique when marketing their products/services to new customers. It tells them how their business offerings can solve a particular problem. Let’s discuss an example of this method of qualitative research.

5. Digital Ethnography

This is an innovative form of qualitative research that focuses on understanding people and their cultures in the context of the digital realm. Digital ethnography aims to study individuals’ behavior, interactions, and social dynamics within online environments and digital communities.

In digital ethnography, the researcher acts as both an observer and a participant in these said online communities to gain firsthand insight into the lifestyles, cultures, and traditions of people navigating these digital landscapes.

Unlike traditional ethnography, digital ethnography is more efficient and accessible. The studies are conducted remotely, reducing the need for extended physical presence in a specific location, and the data collection process is often more streamlined.

6. Grounded Theory

This is another data collection method of qualitative research used across various disciplines. The Grounded Theory aims to provide the reasons, theories, and explanations behind an event. It focuses on why a course of action has happened the way it did.

The grounded theory model collects and analyzes the data to develop new theories about the subject. The data is collected using different techniques like observation, literature review, and document analysis.

This qualitative method is majorly used in business for conducting user satisfaction surveys to explain why a customer purchases a particular product or service. It helps companies in managing customer loyalty.

Watch: How to Create a Customer Satisfaction Survey

7. Phenomenology

Phenomenology is another qualitative research example that describes how an individual experiences or feels about a particular event. It also explores the experience of a specific event in a community.

Here, the researcher interviews people who have experienced a particular event to find similarities between their experiences. The researcher can also record what they learn from the target audience to maintain the credibility of the data.

Although this qualitative technique depends majorly on interviews, other data collection methods like observation, interviews, and survey questionnaires are also used to supplement the findings. The application of this method is found in psychology, philosophy, and education.

For example, to prompt a participant to share their experience around an event they encountered, you can ask:

“What was your experience like when you first encountered [a specific phenomenon or event]?”

8. Record Keeping

This approach involves using existing trustworthy documents and other reliable sources as the basis of data for new research. It’s comparable to visiting a library, where you can explore books and reference materials to gather relevant data that might be helpful for your research.

How Do You Analyze Qualitative Data?

1. Arranging the Data

Qualitative data is collected in different forms like audio recordings, interviews, video transcriptions, etc. This step involves arranging all the collected data in the text format in the spreadsheet. This can be done either manually or with the help of data analysis tools.

2. Organizing the Data

Even after putting the data into a spreadsheet, the data is still messy and hard to read. Due to this, the data needs to be organized in a readable and understandable pattern.

For example, you can organize data based on questions asked. Organize your data in such a way that it appears visually clear. Data organization can be tedious, but it is essential for the next step.

3. Assigning Codes

Developing codes for the data helps simplify the data analysis methods in qualitative research. Assigning code implies categorizing and setting patterns and properties to the collected data. It helps in compressing the vast amount of information collected. By developing codes for your data, you can gather deep insight into the data to make informed business decisions.

4. Analyzing the Data

Qualitative data cannot be analyzed based on any universally accepted equation like quantitative data. Qualitative data analysis depends on the thinking and logical skill of the researcher.

However, there are a few techniques by which you can easily interpret data by identifying themes and patterns between sample responses:

• Checking the data for repetitive words and phrases commonly used by the audience in their answers.
• Comparing the primary and secondary data collection to find the difference between them.
• Scanning the data for expected information that has not been included in answers provided by respondents.

5. Summarizing the Data

The final stage is to link the qualitative data to the hypothesis. Highlight significant themes, patterns, and trends by using essential quotes from the data, as well as any possible contradictions.

One of the main things about qualitative data is that there isn’t a single, formal way to collect and analyze data. Each research project will have its own set of methods and techniques that it needs to use.

The key is to look at the specific needs of each project and change the research method accordingly.

Advantages and Limitations of Qualitative Research

Qualitative market research techniques offer a more comprehensive and complete picture of the subject than quantitative research, which focuses on specific and narrow areas. Other advantages of using qualitative research methods are:

• Explore the subject in-depth: Qualitative research is personal and offers a deep understanding of the respondent’s feelings, thoughts, and actions so that the researcher can perform an in-depth analysis of the subject.
• Promotes discussion: Qualitative research methods are open-ended in approach rather than rigorously following a predetermined set of questions. It adds context to the research rather than just numbers.
• More flexibility: The interviewer can study and ask questions on the subject they feel is pertinent or had not previously thought about during the discussions. Moreover, open-ended questions enable respondents to be free to share their thoughts, leading to more information.
• Capture trends as they change: Qualitative research can track how people’s feelings and attitudes change over time. Respondents’ opinions can change during the conversation, and qualitative research can show this.

With that being said, however, we do not mean that qualitative data is entirely devoid of flaws. Like most things, it, too, has its fair share of limitations, the prime among them being:

• Subjectivity: Qualitative data can be influenced by the researcher’s bias or interpretation, potentially affecting the objectivity of the findings. The absence of strict guidelines in qualitative research can lead to variations in data collection and analysis too.
• Time-Consuming & Resource-Intensive: Conducting qualitative research can be a lengthy process, from data collection through transcription and analysis. It also often requires skilled researchers, making it more resource-intensive compared to some quantitative methods.
• Difficulty in Analysis: Analyzing qualitative data can be complex, as it involves coding, categorizing, and interpreting open-ended responses. This data category often does not lend itself well to traditional statistical tests, limiting the depth of statistical analysis as well.
• Challenges in Replication: Replicating qualitative studies can be challenging due to the unique context and interactions involved.

Advantages of Using Website Surveys for Qualitative Research

The role of surveys and questionnaires in collecting quantitative data is pretty obvious, but how exactly would you use them to capture qualitative data, and why? Well, for starters, website surveys offer numerous advantages here, such as letting researchers explore diverse perspectives, collect rich and detailed data, conduct cost-effective and time-efficient studies, etc.

Let’s have a brief rundown of the significant benefits below:

Reach and Diversity: Website surveys enable researchers to engage with a diverse and global audience. They break geographical barriers, allowing participation from individuals residing in different regions, cultures, and backgrounds, leading to a richer pool of perspectives.

• Cost-Effectiveness: Conducting traditional face-to-face qualitative research can be expensive and time-consuming. In contrast, website surveys are cost-effective, as they eliminate the need for travel, venue rentals, and other logistical expenses.
• Convenience and Flexibility: Website surveys offer unparalleled convenience to both researchers and participants. Respondents can take part in the study at their own pace and preferred time, promoting higher response rates and reducing non-response bias.
• Anonymity and Honesty: Participants often feel more comfortable expressing themselves honestly in online surveys. Anonymity ensures confidentiality, encouraging candid responses, and allowing researchers to gain deeper insights into personal experiences and opinions.
• Rich Data Collection: Website surveys can accommodate various question types, including open-ended questions, allowing respondents to elaborate on their thoughts. This results in the collection of rich, detailed, and nuanced data, enriching the qualitative analysis.
• Time-Efficient Data Collection: Website surveys facilitate efficient data collection, reaching a large number of participants in a short span. Researchers can access real-time data, enabling quick analysis and timely decision-making.
• Ease of Analysis: Online survey platforms often provide tools for automated data analysis, simplifying the coding and categorization process. Researchers can swiftly identify themes and patterns, expediting the interpretation of qualitative findings.
• Longitudinal Studies: Website surveys are well-suited for longitudinal studies, as they allow researchers to follow up with the same participants over an extended period. This longitudinal approach enables the exploration of changes in attitudes or behaviors over time.
• Integration with Multimedia: Website surveys can seamlessly incorporate multimedia elements, such as images, videos, or audio clips, enabling respondents to provide more context and depth to their responses.
• Eco-Friendly Approach: By reducing the need for paper and physical materials, website surveys promote a sustainable and eco-friendly approach to data collection, aligning with responsible research practices.

Most website survey tools are equipped with features that efficiently collect and analyze diverse perspectives, ultimately furthering your data collection process. For example:

• Question Customization: These tools allow users to create and customize a wide range of questions, including open-ended, closed-ended, rating scale, and more. This flexibility allows participants to express their thoughts and feelings in their own words, paving the way for gathering diverse qualitative data.
• Anonymity and Confidentiality: Ensuring confidentiality in qualitative research is crucial for building trust and obtaining more accurate and sensitive data. Participants can often remain anonymous when using website survey tools, which can encourage them to provide honest and candid responses.
• Data Analysis Support: Many website survey tools offer built-in data analysis features, such as basic statistical summaries and visualizations. While these features are more suited for quantitative data, they can still aid in organizing and understanding qualitative responses, making the analysis process more manageable.
• Flexibility in Survey Design: Researchers can use skip logic and branching features in these tools to create dynamic surveys that adapt based on participants’ responses. This can be greatly valuable in qualitative research, where participants’ experiences might vary widely.
• Ease of Participation: Participants can access website surveys using various devices like computers, tablets, or smartphones, making it convenient and accessible for them to take part in the research. This ease of participation can contribute to a higher response rate and a more diverse participant pool.
• Data Storage and Security: Many website survey tools offer secure data storage and backup, ensuring the safety of the collected qualitative data. This feature is essential for maintaining the confidentiality and integrity of participants’ responses.

Examples of Website Survey Questions for Qualitative Research

These examples can greatly help in targeting customers through Click-to-WhatsApp Ads on various social media platforms. Crafting effective survey questions is crucial for qualitative research. Ensuring clarity, avoiding leading questions, and maintaining a balanced mix of question types is paramount if you are looking to gather comprehensive and valuable qualitative data.

With well-designed website survey questions, you can delve deep into participants’ thoughts, emotions, and experiences, providing a solid foundation for insightful qualitative analysis.

Let’s explore some of the prime examples:

1. Open-Ended Questions (Exploratory):

• “Please describe your experience with our product/service in your own words.”
• “What are the main challenges you face in your daily work?”

2. Multiple-Choice Questions (Categorization):

“Which age group do you belong to?”

• 18-25 years
• 26-35 years
• 36-45 years
• 46-55 years

3. Likert Scale Questions (Rating/Opinion): “On a scale of 1 to 5, how satisfied are you with our customer support?” 1 (Not satisfied at all) 2 (Slightly satisfied) 3 (Moderately satisfied) 4 (Very satisfied) 5 (Extremely satisfied)

4. Ranking Questions (Preference):

“Please rank the following factors in order of importance for choosing a smartphone:”

• Battery life
• Camera quality
• Processor speed
• Display resolution

5. Semantic Differential Questions (Contrast): “How would you describe our website’s user interface?”

• Difficult _ Easy Unattractive Attractive
• Confusing ___ Clear

6. Picture Choice Questions (Visual Feedback):

“Which logo do you find more appealing for our brand?”

• Option A (Image)
• Option B (Image)

7. Demographic Questions (Participant Profiling):

“Which of the following best describes your occupation?”

• Professional

8. Dichotomous Questions (Yes/No):

• “Have you ever purchased products from our online store?”

9. Follow-Up Probing Questions (In-depth Insight):

• “You mentioned facing challenges at work. Could you please elaborate on the specific challenges you encounter?”

10. Experience-Based Questions (Narrative):

• “Tell us about a memorable customer service experience you’ve had, whether positive or negative.”

Ready to Obtain Quality Data Using Qualitative Research?

So, there you have it all about qualitative research methods: their types, examples, use, and importance. Quantitative research is one of the most effective instruments to understand individuals’ thoughts and feelings or identify their needs and problems.

After figuring out the problem, quantitative research is used to make the conclusion and offer a reliable solution for business.

You can also supplement your qualitative market research with ProProfs Survey Maker to reach your target audience more effectively and in a shorter duration. Use the 15-day free trial to enhance your qualitative research – no commitment, no credit card details!

About the author

Emma David is a seasoned market research professional with 8+ years of experience. Having kick-started her journey in research, she has developed rich expertise in employee engagement, survey creation and administration, and data management. Emma believes in the power of data to shape business performance positively. She continues to help brands and businesses make strategic decisions and improve their market standing through her understanding of research methodologies.

Popular Posts in This Category

How to Use Real Time Feedback Survey for Business Growth & Development

Top Advantages & Disadvantages of Customer Feedback Surveys

8 Best Cognito Forms Alternatives in 2024

Guide to Create an Effective Employee Surveys [Questions + Templates]

User Experience Surveys: 10 Questions to Ask

Employee Engagement Surveys: Everything You Need To Know

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• My Account Login
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Open access
• Published: 25 April 2024

A framework for the analysis of historical newsreels

• Mila Oiva   ORCID: orcid.org/0000-0002-5241-7436 1 ,
• Ksenia Mukhina 1 ,
• Vejune Zemaityte   ORCID: orcid.org/0000-0001-9714-7903 1 ,
• Andres Karjus   ORCID: orcid.org/0000-0002-2445-5072 1 , 2 ,
• Mikhail Tamm 1 ,
• Tillmann Ohm 1 ,
• Mark Mets 1 ,
• Daniel Chávez Heras   ORCID: orcid.org/0000-0002-9877-7496 3 ,
• Mar Canet Sola   ORCID: orcid.org/0000-0001-5986-3239 1 ,
• Helena Hanna Juht 4 &
• Maximilian Schich 1  

Humanities and Social Sciences Communications volume  11 , Article number:  530 ( 2024 ) Cite this article

Metrics details

• Cultural and media studies

Audiovisual news is a critical cultural phenomenon that has been influencing audience worldviews for more than a hundred years. To understand historical trends in multimodal audiovisual news, we need to explore them longitudinally using large sets of data. Despite promising developments in film history, computational video analysis, and other relevant fields, current research streams have limitations related to the scope of data used, the systematism of analysis, and the modalities and elements to be studied in audiovisual material and its metadata. Simultaneously, each disciplinary approach contributes significant input to research reducing these limitations. We therefore advocate for combining the strengths of several disciplines. Here we propose a multidisciplinary framework for systematically studying large collections of historical audiovisual news to gain a coherent picture of their temporal dynamics, cultural diversity, and potential societal effects across several quantitative and qualitative dimensions of analysis. By using newsreels as an example of such complex historically formed data, we combine the context crucial to qualitative approaches with the systematicity and ability to cover large amounts of data from quantitative methods. The framework template for historical newsreels is exemplified by a case study of the “News of the Day” newsreel series produced in the Soviet Union during 1944–1992. The proposed framework enables a more nuanced analysis of longitudinal collections of audiovisual news, expanding our understanding of the dynamics of global knowledge cultures.

Introduction

Audiovisual news has affected the global knowledge landscape for over a century. As a media format, audiovisual is impactful, and news as a genre is particularly effective for forming knowledge about the world. Although what counts as news is debatable (Tworek, 2019 ), labelling a story as ‘news’ suggests that the offered content has contemporary relevance and that it provides truthful information on the surrounding world—even if we know that this is not always the case (Winston, 2018 ; Lazer et al., 2018 ). Understanding audiovisual news content is crucial because news, taking part in creating the ‘media reality’ (Morgan, 2008 ), steers our gaze to the world, affects our opinions, and shapes our identities (Imesch et al., 2016 ; Hoffmann, 2018 ; Werenskjold, 2018 ). Even if some audiences may disagree with the content (Sampaio, 2022 ), news sets the agenda for societal discussions and contributes to what we consider worth knowing.

To fully understand the functions of audiovisual news content, production, and dissemination, exploring them through large and consistent sets of data, covering a long time span, can be helpful. Examining a consistent set of data that, for example, covers all the issues of a newsreel series, gives an understanding of the variety of individual findings and contextualises them. Detecting long-term continuities, and short-term trends helps us better understand the past information culture, alongside what is perhaps specific to our time or area. However, so far, studying audiovisual news via large quantities of data or across a long time span systematically in ways that would take into account their complexity has been hampered by availability of data and integration of methods across disciplines. Here we work towards a unified approach to study audiovisual news that enables the comparison of data coming from different sources to reveal the cultural and temporal variations of the global news scene.

Newsreels were the first widely spread form of audiovisual news. Starting in France in 1909, these approximately ten-minute-long news films, shown in the weekly changing series in cinemas, informed audiences about the latest political events, innovations, sports competitions, and fashion trends. Each newsreel issue contained around five to twelve short news stories, often showing the ‘more serious’ ones first and ending with entertaining topics. Until the mid-1950s, newsreels were the main source of audiovisual news for audiences globally that also conveyed both political propaganda and commercial interests. Their production continued in some countries under state support until the 1990s (Chambers et al., 2018 ; Pozharliev and Gallo González, 2018 ; Fielding, 2009 ).

Like other audiovisual products, newsreels are multifaceted. They are multimodal combinations of moving images, sounds, music, and of spoken and written language, gestures, iconographies, and signs as deeply rooted in the surrounding societies. The meanings created are interrelated across modalities with an individual news story gaining additional meaning, depending on its embedding and temporal position in a newsreel issue. In fact, one may argue, the meaning of a newsreel issue can be understood only when looking at the contents of the other issues of the newsreel series. Therefore, in order to understand the messages and role of individual news stories in a society, it is necessary to study newsreels as a whole (Hickethier, 2016 ) and comparatively, systematically analysing larger collections through the interconnections of small-scale units. This, we argue, has to transcend the debate of a single community of practice, such as media studies or communication, which is why this paper brings forth the expertise from a broad range of research streams—including film history, computational video analysis, film studies, and the so-called New Cinema History—to study film and video in a comprehensive way.

Lately, many newsreel series have been digitised, and several national film archives as well as transnational collections, such as Europeana, the Internet Archive, and Wikimedia Commons, all increasingly provide access to newsreels in digital form. This has opened new possibilities for studying long-term patterns of audiovisual news. However, despite promising developments in various disciplines, current approaches to newsreels, as discussed below, do not allow us to fully grasp these complex cultural products. Many established research fields are relevant to the longitudinal study of historical newsreels and audiovisual news in general. However, if the current approaches from each research stream are used separately, they produce considerable gaps in the nuanced understanding of newsreels in a long temporal continuum. The available approaches are either qualitative and do not allow a systematic analysis of large-scale data, or quantitative and reveal only one aspect of the multimodal newsreel data. Table 1 summarises the related research streams and their gaps, ranging from the scope of data used, comparability of analysis, the modalities taken into account, and the elements creating meaning in audiovisual material. Simultaneously, each research stream offers a contribution that helps fill the gaps in other fields. In the following paragraphs, we elaborate further on the gaps in each of these research streams together with the beneficial contributions that they may bring.

Qualitative film history

Qualitative film historical studies on newsreels have been demonstrating the variety of production conditions, core messages, and distribution channels in a number of countries and at different times (Chambers et al., 2018 ; Garrett Cooper et al., 2018 ; Imesch et al., 2016 ). Their strength is that they take into account the interplay of multiple modalities in the film material and produce nuanced analyses of the messages they have conveyed to the audiences. Simultaneously, however, they focus on temporally restricted segments of data, use analysis methods that are hard to apply to a large quantities of data, and do not usually utilise categories that would allow systematic comparisons between different studies (Chambers, 2018 , Pozharliev and Gallo González, 2018 , Bergström and Jönsson, 2018 , Vande Winkel and Biltereyst, 2018 ; Pozdorovkin, 2012 , Veldi et al., 2019 ). The main limitation of qualitative enquiry is its incomplete ability to offer an understanding of what is prevailing and what is marginal in wider terms in the data, which makes it difficult to see the bigger picture and contextualise the findings. As van Noord ( 2022 ) notes, exploring recurring motifs or patterns in cultural data is crucial for a deeper understanding. Although an experienced qualitative scholar is usually able to point out some of the repeating patterns based on their accumulated knowledge of the field, computational methods can back that up, measure the prevalence of the pattern in the collection, and detect also other, possibly unnoticed patterns.

Computational video analysis

Computational video analysis focuses on the systematic study of large collections of data, while typically addressing a single modality rather than aggregates of contextual and temporal factors. Examples include increasingly accurate and effective methods for recognising shot and scene boundaries (Hanjalic, 2002 ; Rasheed and Shah, 2003 ), persons (Wang and Zhang, 2022 ), objects (Brasó et al., 2022 ), human poses (Broadwell and Tangherlini, 2021 ), number of individuals in a crowd (Zhang and Chan, 2022 ), events (Wan et al., 2021 ), sounds (Park et al., 2021 ), human and animal behaviour (Gulshad et al., 2023 ; Bain et al., 2021 ; Sommer et al., 2020 ) and to perform image segmentation (Hu et al., 2022 ). Different solutions for condensing audiovisual content have also been developed, either for creating video representations to enable efficient browsing (Zhao et al., 2021 ) or numerical fingerprints allowing comparisons of video content for retrieval and recommendation systems (Kordopatis-Zilos et al., 2022 ; Nazir et al., 2020 ). Deep Learning applications in computer vision have been used for various item recognition tasks in images and videos (Bhargav et al., 2019 ; Liu et al., 2020 ; Kong and Fu, 2022 ; Brissman et al., 2022 ; Kandukuri et al., 2022 ). While mainstream computational video content analysis has focused on images, other modalities, like sound, have been also gaining increased attention (Valverde et al., 2021 ; Yang et al., 2020 ; Senocak et al., 2018 ; Hasegawa and Kato, 2019 ; Hu et al., 2022 ; Ye and Kovashka, 2022 ; Sanguineti et al., 2022 ; Pérez et al., 2020 ), eventually feeding into multi-modal analysis (Mourchid et al., 2019 ; Ren et al., 2018 ). However, considering different modalities of audiovisual data, particularly within the historical focus of this paper, remains beyond mainstream in video analysis. In addition, there is a lack of discussion on how certain units of analysis, such as recognised objects or condensed forms of video content, can be credibly used to detect the ways audiovisual content creates and conveys meaning to audiences.

Computational film studies

Situated between the qualitative and quantitative study of audiovisual contents, computational film studies often combine the two approaches. This stream of literature started by using shot detection to analyse film dynamics and editing styles (Salt, 1974 ; Tsivian, 2009 ; Pustu-Iren et al., 2020 ). In addition to addressing dynamics as an important modality of audiovisual content, computational film scholars have also been combining different modalities, such as images and sound (Grósz et al., 2022 ), spoken texts (Carrive et al., 2021 ; van Noord et al., 2021 ), or shown locations (Olesen et al., 2016 ). Computational studies of newsreels more specifically have addressed the contents of news either on the level of textual descriptions of news story topics (Althaus et al., 2018 ; Althaus and Britzman, 2018 ) or at a more granular level combining different modalities by analysing the voice-over text and automatically recognising well-known individuals in the film material (Carrive et al., 2021 ).

An ongoing debate in computational film studies concerns how film creates meaning, what are the most important meaning-making units, and how they could be best extracted (Chávez Heras, 2024 ; Burghardt et al., 2020 ; Burges et al., 2021 ). A profound challenge is that many modalities of film, such as images, can be interpreted in divergent ways depending on the viewer and their context (van Noord, 2022 ; Arnold and Tilton, 2019 ; Pozdorovkin, 2012 ). Different modalities may also create juxtaposing messages (Pozharliev and Gallo González, 2018 ). David Bordwell ( 1991 ) has argued that films contain ‘cues’ on which the further comprehension and interpretation of their meaning is based. Although the spectators may have differing opinions on the profound message of a film, an important hypothesis is that they nevertheless usually agree upon what the meaning-making cues are (such as shown activities or spoken sentences). This means that the variety of “credible” interpretations of the message of the film is limited (Bordwell, 1991 ). A central premise of computational film studies is thus that it can be possible to detect these cues and reach for an aggregate meaning of films through them.

Lately, in pursuit of understanding the meanings carried by film, a number of scholars have been using recognition and annotation of pre-set categories or stylistic features, discussing whether human interpretation should be applied already at the event of recognising the items, or at a later stage of the analysis (Carrive et al., 2021 ; Bhargav et al., 2019 ; Heftberger, 2018 ; Burges et al., 2021 ; Williams and Bell, 2021 ; Hielscher, 2020 ; Cooper et al., 2021 ; Bakels et al., 2020 ; authors discussing this issue: Burghardt et al., 2020 ; Arnold et al., 2021 ; Masson et al., 2020 ). There are also scholars further problematising object recognition by stating that in addition to recognising an object we should know how it is portrayed in order to understand its meaning (Hielscher, 2020 ) and calling for more thorough thinking of which measures can be used to analyse film contents (Olesen and Kisjes, 2018 ). This discussion connects with the wider question if there are cues in film that create meaning, how to find them, how to decide what to measure, and how to make sure that what is being measured gives responses to salient research questions. Although computational studies of historical newsreels use elaborate methods (Carrive et al., 2021 ; Althaus et al., 2018 ; Althaus and Britzman, 2018 ) including more explicit discussion on the connection of the research question and the variables can be an important methodological amendment to research.

New cinema history

New Cinema History (Maltby et al., 2011 ) stresses the importance of societal and temporal context in recent studies on film production (Dang, 2020 ), circulation (Clariana-Rodagut and Hagener, 2023 ; van Oort et al., 2020 ; Verhoeven et al., 2019 ; Navitski and Poppe, 2017 ), and reception (Treveri Gennari and Sedgwick, 2022 ). The premise of this discipline is that alongside the content, the surrounding context and its change over time are crucial factors in creating the meaning of film (as also pointed out by van Noord et al., 2022 ). Focusing on the contextual factors, this research stream has dealt less with content, yet because the meaning of cultural artefacts relies on both, these aspects need to be combined to reach a more nuanced understanding of newsreels or their aggregated meaning.

Digital hermeneutics

Examining historical material adds its particularities to a study. Current digital historical research has used the concept of ‘digital hermeneutics’ to call for epistemological data aka source criticism and method criticism (Fickers et al., 2022 ; Oberbichler et al., 2022 ; Salmi, 2020 ; Paju et al., 2020 ). It is crucial to understand how the data was formed and by whom, and what kinds of activities and worldviews it reflects. Firstly, the temporal meaning change of the formally similar units has to be taken into account. For example, showing a horse in a newsreel in 1910 and 1990 most likely creates very different interpretations. Secondly, digitised data are no longer in their original format (Fickers, 2021 ), and may contain traces left by the production, storage, archiving, digitising, and acquisition processes. For instance, textual descriptions of newsreel content are often added during the digitisation of the material and thus might reflect the perceptions or diligence of the digitisers rather than the activities of the original newsreel authors (Elo, 2020 ; see also Althaus and Britzman, 2018 ). As our case study shows in Section III, heavily censored data can also offer relevant results, when interpreted with an understanding that it provides the view of the authorities. Gaps in the data can produce meaningful insights. Therefore, it is important to account for which activities and to whom the traces that are being analysed belong. Furthermore, off-the-shelf computational analysis methods are often trained on contemporary materials and may not work similarly well with historical materials without adaptation (Grósz et al., 2022 ; Bhargav et al., 2019 ; Wevers, 2021 ; Wevers and Smits, 2020 ). Finally, the quality of cultural heritage materials can vary greatly, which poses additional challenges when studying long-term developments of audiovisual news.

Towards a unified approach

To summarise, while computational video analysis customarily assumes meaning to be contained in the artefact (i.e. the video), qualitative research and New Cinema History argue that meaning only arises when the artefact comes into contact with its audience and can be perceived as having different meanings. Simultaneously, an analysis that ignores inter-subjective contingency is blind to context; an interpretive framework that ignores inter-objective dependency is blind to structure. Both the content and the context should be taken into account, and, we argue, substantial advances in audiovisual (news) studies can be made by coupling these two positions.

The analysis framework for audiovisual newsreel corpora, as outlined in this paper was co-designed within a research process that started with experimental explorations of newsreel data, while negotiating and integrating methods from a spectrum of disciplines as brought together in the CUDAN ERA Chair project for Cultural Data Analytics at Tallinn University. Oscillating between joint reflections in collaborative group work, including two three-day hackathons, and more concentrated work on individual aspects, eventually led to the proposed generalisation of multidisciplinary collaboration in a systematic research process to make sense of historical newsreels at corpus scale. Following C.P. Snow’s call regarding the necessity to bridge the so-called “two worlds” of scholarly enquiry (Snow, 2001 [1959]), our starting point was that multidisciplinary integration brings forth more than a sum of its components. The specific stages of the proposed framework, explained in more detail below, were discovered by combining the established research processes of cultural data analytics and digital history, while experimenting with different ways of integrating quantitative and qualitative approaches, including expertise that is usually found in computation and the natural sciences

The objective of the framework is to exemplify how qualitative and quantitative approaches can be successfully brought together into a joint research pipeline. Towards this purpose, we combine the strengths of qualitative film history, computational video analysis, computational film studies, and New Cinema History listed in Table 1 , while closing their mutual and common gaps. In sum, we present a framework for systematically studying large collections of historical newsreels covering several decades in the context of their temporal and cultural dynamics, diversity, and functions. We propose bringing together a comprehensive set of aspects for a nuanced understanding of newsreels as an interplay of different modalities and contextual factors. The framework includes both qualitative and quantitative research feeding into a systematic approach and ability to cover large quantities of data. The framework, which we discuss in Section II, constitutes a schematic template for research projects combining quantitative and qualitative approaches (see Fig. 1 ). In Section III, we exemplify the framework using a dataset of “News of the Day” newsreel series produced in the Soviet Union in 1944–1992. Finally, Section IV contains the discussion and concludes the article.

The newsreel framework combines qualitative and quantitative approaches into a research pipeline. It contains ( a ) pairing meaning-making units with variables , ( b ) digital data (source) and method criticism, and ( c ) combining quantitative analysis with qualitative conclusions.

Newsreel Framework

Our framework essentially centres around a workflow pipeline configuration (Oberbichler et al., 2022 ) that includes qualitative and quantitative enquiry (Fig. 1 ). There are three important stages in the pipeline: detecting and pairing the meaning-making units and variables, digital data (source) and methods criticism, plus merging and explaining analysis visualisations of different dimensions of the data (Fig. 1a–c ). The study of newsreels begins with identifying meaningful research questions and data, in relation to preceding research. Perhaps more explicit than in established qualitative approaches, we propose to identify relevant meaning-making units arising from preceding research and qualitative enquiry, and pair them with available variables at the first stage (Fig. 1a ). In the second stage, we account for different temporal layers embedded in digitised heritage data to gain a better understanding of how the variables connect with the meaning-making units and the final conclusions of the study (Fig. 1b ). After this, appropriate analysis methods are selected, keeping in mind the available variables and research questions, followed by computational analysis. In the third stage the selected variables are studied quantitatively, feeding into an examination of the resulting dimensions of analysis to jointly produce final qualitative conclusions (Fig. 1c ). This stage brings the dimensions of analysis together, critically evaluates what the findings jointly report, contextualises them, and responds to the research questions. Adding these three stages to the research pipeline ensures that newsreels are analysed systematically by considering the multidimensional nature of meaning of cultural data (Schich, 2017 ; Cassirer, 1927 ), focusing on variables relevant to the research questions, and accounting for multimodality in the final results. The framework is modular, which means that it allows selecting methods that suit the particular research question or using multiple methods comparatively, while dealing with particular meaning-making units and variables. Qualitative and quantitative enquiry are firmly intermingled and mutually dependent in this research process, as exemplified in our case study below. Importantly, different parts of the research project are continuously adjusted in relation to each other (Schich, 2017 ; Gadamer, 2013 (1960).

While meaning-making units are the elements related to human understanding of what the phenomenon under study is composed of, the variables are the metadata entries or other features of the data that can be directly analysed computationally (cf. the distinction of elements and features in GIS; Zeiler, 1999 ). Detection and pairing of the meaning-making units with the available or traceable variables (Fig. 1a , see also Fig. 2 ) improve critical evaluation of meaning and comparability identified as gaps in preceding research (see Table 1 ). Furthermore, it establishes an explicit connection between the analysed variables and the phenomenon under study, enabling critical evaluation. The preceding literature uses the term ‘cue’ both when referring to what we call here the meaning-making units and variables (e.g. Bordwell, 1991 ; Ren et al., 2018 ), which complicates differentiating between the two. The meaning-making units come from the initial idea of the study, the research question, and the preceding literature, while variables are concretely present in the data. While they arise from different roots, the successful pairing of the two concepts is crucial for a fruitful study.

a Meaning-making units selected from Supplementary Table 1 for further analysis. b Existing and enriched variables of the News of the Day data. Arrows signify data enrichment based on the original data. c Resulting dimensions of analysis that interconnect the meaning-making units and variables.

The meaning-making units are elements that make up the phenomenon under scrutiny. Examples of meaning-making units, as relevant for newsreel research and broadly agreed in literature, include images, voice-over narration, acoustic motifs, the persons, activities, or locations shown, and content topics (Supplementary Table 1 ). Contextual factors are also important, including the socio-political circumstances, other concurrently available mass-communication media, and agency-related issues, like funding and the role of audiences. Relevant meaning-making units can be identified via an extensive literature review of qualitative studies on the topic to see what elements are often suggested and by critically evaluating the gaps. Of course, they may also emerge from analysis itself, in which case the research is firmly going beyond the state of the art.

In addition to the existing feature variables, others can be added, by either manually or algorithmically enriching the data, or adding additional data sources. As Table 2 shows, the most frequent metadata entries in the largest openly available collections of digitised newsreels contain information on production year, newsreel series title, duration, and content annotations either as text or keywords. The metadata entries, together with the available newsreel videos, form the basis for extracting variables. They can be further enriched with information concerning the newsreel authors, distribution, audience reactions, etc. To obtain well-selected units for computational analysis it is crucial to critically evaluate and pair the meaning-making units necessary for responding to research questions with variables that are available or traceable via enrichment. Notably, some variables might reveal meaning-making units indirectly (e.g. the number of people working on newsreels can be indicative of funding and the societal importance of newsreels).

The second stage we propose for the analysis part of the pipeline is to incorporate digital data (source) criticism by taking into account the historical multidimensionality of heritage data, as well as the temporal change affecting the meaning-making units and variables into the study (Fig. 1b ). This stage includes qualitative historical reflection, complementing the two other stages of the framework (Fig. 1 a, c). At this stage, firstly, the researchers scrutinise how the historical traces of the data, coming from production, storage, archiving, or digitising, are present in the data, affecting which variables should be selected for further analysis. The variable can be connected with the different meaning-making units, depending on the point in time and by whom it was created. For example, if a textual description of the contents was created as a newspaper advertisement or censorship card at the time of producing the newsreels (Werenskjold, 2018 ; Althaus and Britzman, 2018 ), the variable connects to the distribution and competition within the cinema market or the political context. If it was created within the digitisation process at a later stage, it should be combined with the interpretations of the later generations of what is noticeable in the contents. Secondly, the researchers will return to this stage after completing computational analysis of the variables to weight the effect of temporal change to the analysis results. As an example, they might reflect upon whether an increasing number of cars detected is due to an explicit choice of the filmmakers, the overall increase of the amount of cars in the society, the fact that the used algorithm detects better new car models than the old ones, or some other reason. Some results may also be absent due to conscious selections in data handling. For example, as our case study in Section III demonstrates, the qualitatively observed absence of footage portraying Stalin before his death in 1953 is most likely a result of de-selection of this material from the data (Fig. 3a ). With the twofold reflections concerning the content and method dependency, this stage addresses the lack of historical contextualisation identified in the preceding literature (see Table 1 ) by proposing to take into consideration the temporal aspects of data both when selecting the variables and when performing the final analysis.

a All News of the Day issues (scatter plot): x-axis publication years, y-axis issue number; the total number of news stories per year based on textual outlines; b number of shots per issue over time; c mean shot length per issue; d shares of news story topics per year classified based on textual descriptions of newsreels using an instructable zero-shot classifier. Each news story is classified with a single class. e A UMAP projection of story embeddings, coloured by the content predictions in ( e ) and ( f ); f annual news story topic distribution averaged over years.

The third stage is that selected variables are computationally examined and visualised as different dimensions of analysis (Fig. 1c ). Evidently, the used research methods should be selected so that they respond to the research questions when applied to the available variables (for method selection and comparison cf. for e.g. Opoku et al., 2016 ; Gentles et al., 2016 ). This stage addresses the lack of multimodality identified in preceding literature (see Table 1 ), and allows to combine newsreel contents with the contexts in a more streamlined manner. These dimensions, focusing, for example, on newsreel production conditions, or visual and content dynamics of newsreels, are further combined thematically or temporally into preliminary findings. Ideally, the dimensions of analysis represent different parts of the newsreel production, content, and distribution process to reach for a more comprehensive understanding of them. The findings are merged with the wider contextual information from the preceding literature.

The approach proposed here arises from discussions within the field of Cultural Data Analytics (Arnold and Tilton, 2023 ; van Noord et al., 2022 ; van Noord, 2022 ; Manovich, 2020 ; Arnold and Tilton, 2019 ; Schich, 2017 ; CUDAN, 2020 –2024). The starting points of this multidisciplinary approach are that cultural phenomena are inherently multi-scale and vary through time and space, that the interactions of particularity and universality are important, and that the meaning of cultural phenomena lies in the multidimensional relations of entities. When reaching for a bigger picture through longitudinal exploration, the main challenge is in maintaining the multitude of the phenomenon under study and simultaneously tracking the dynamics of selected variables. In this circumstance, recognising plurality and multidimensionality is crucial for understanding cultural phenomena, and we should be careful when reducing this multitude into means or homogenous groups (van Noord et al., 2022 ; van Noord, 2022 ; Manovich, 2020 ).

The design of our newsreel framework supports maintaining the multitude of cultural data while tracking its dynamics in a manner that allows comparisons across time and datasets. The following section exemplifies the application of the proposed framework to the analysis of the “News of the Day” newsreel series, published weekly in the Soviet Union from 1944 to 1992.

Materials and Methods

The data used in the case study is a collection of 1747 issues of the Russian-language Soviet newsreel journal News of the Day digitised by Net-Film company covering the years 1944–1992. We scraped the video files of newsreels with metadata containing information on the production year, issue number, authors and brief content descriptions in Russian and English with the permission of the data provider, the Net-Film company. The data is incomplete in many ways: the collection lacks some newsreel issues; the image and audio quality of the videos is low; and the metadata is imperfect. When working with digitised historical data and analysing the results it provides, incompleteness of the data is a common feature that needs to be taken into account (Carrive et al., 2021 ). Simultaneously, as our case study shows below, systematic holes in data can reveal crucial source-critical aspects of the data, informing the whole research. It is part of a historians’ skillset to be able to work with incomplete data, and to decide how far conclusions can be drawn from it (Howell and Prevenier, 2001 ).

The methodology of our case study followed the above proposed phenomenon categorisation by defining the central meaning-making units, and organisation and enrichment of the data to receive corresponding variables. We selected the methods used for analysing the resulting variables based on the team members’ domain expertise and their evaluations on the methods that would best respond to the research question of how the world was depicted in the News of the Day and by what kinds of groups of individuals involved in newsreel production. As the more detailed description of the methods below shows, all the steps of the research process involved intermingled qualitative, quantitative, computational, and human-made processes.

Meaning-making units

The table containing the meaning-making units of newsreels (Supplementary Table 1 ) was prepared by extensive reading of the preceding qualitative literature on newsreels. Identifying meaning-making units in qualitative research was purposeful because qualitative analysis takes a more holistic view to the phenomenon under scrutiny that quantitative approaches. We collected all the meaning-making units mentioned also in passing in the studies. Because scholars use varying terminology, we homogenised and aggregated the labels of the units. In addition to giving a general view, it also helps to pinpoint groups of studies that have different emphases, for example, on more abstract motifs, or those ones emphasising the contextual and agency-related meaning-making units instead of contents.

The matrix of the most frequent variables in the largest openly accessible collections of digitised newsreels (Table 2 ) lists the most commonly used metadata fields and their presence in some of the most well-known digitised newsreel collections. For the purpose of mapping the variety and prevalence of the metadata fields, the matrix lists the metadata entries using a common description, and not the specific entry titles each individual collection uses. Different digitising and archiving projects may use different types of metadata in variable formats, which may necessitate harmonising data in projects using several collections (see also Beals and Bell, 2020 ). In addition to the listed metadata entries, many collections also contain other data. For this mapping, we did not study how well the metadata entries have been filled or the consistency of the data. We have marked with “x” those entries that already exist, and with “i” those entries that can be extracted from the data. When selecting the variables for a study, qualitative evaluation of the historical dimensions of the data is essential.

Data enrichment

We amended the data by explicating further information both from the newsreel videos and metadata. For the videos, we ran shot boundary detection analysis (SBD), extracted the middle frames of each shot, and produced a ResNet50 (He et al., 2015 ) embedding for those frames. From the textual descriptions of newsreel contents in the metadata, we identified places mentioned using Named Entity Recognition (NER), and further geocoded the recognised location by adding lat/long coordinates. We also applied automatic detection of the assumed gender of newsreel directors and other crew members based on the surnames, which are grammatically gendered in Russian (Fig. 2b ). All automated steps involved qualitative and manual validation and correction of the processed results with human expertise in the loop.

News story categories

Each News of the Day issue is split into individual stories (12,707 across the 1747 reels), which have synopsis-like descriptions in the metadata. We also corrected small numbering and consistency issues in a minority of them by hand. We then applied two types of automatic content categorisation to the stories, topic modelling and content classification. Topic modelling (often using Latent Dirichlet Allocation, a form of “soft” clustering) is a common approach in digital humanities and other fields dealing with large text collections. For topics, we use the pretrained model driven approach (Angelov, 2020 , Grootendorst, 2022 ) where texts are first embedded using a word or sentence embedding (we use fasttext; Bojanowski et al., 2017 ) and then clustered, with cluster keywords derived via grouped term-frequency inverse-document-frequency (TF-IDF) scaling. The upside of topic modelling as an explorative approach is that the topics need not be known in advance. The downside is that the clusters may be hard to interpret or even meaningless, and the number of clusters must still be defined in advance. We therefore also experimented with another classification approach.

While in the recent past classifying content or topics would have required purpose-trained supervised classifiers, the advent of instructable large language models (LLMs, such as ChatGPT) makes it possible to predict topic or class prevalence in a “zero-shot” manner. Instead of training or tuning a classifier in a supervised manner on annotated examples, generative LLMs can be simply prompted (instructed) to output relevant text, including topic tags given an input example accompanied with the prompt. The simplest example would be along the lines of “Tag this sentence as being of topic X or Y. Example: [text]”, but we find more verbose prompts with topic definitions yield more accurate results. We defined eight topics of interest based on previous qualitative literature and Soviet history: USSR politics, sports, military (defence, wars), scientific and industrial progress (includes innovation, construction projects, space and aviation), USSR economy and industry, USSR agriculture (excludes other economy topics), natural disasters, social issues and lifestyle (includes education, family, health, leisure, culture, religion topics), and a “misc” topic meant to cover everything else (for the prompts, see the Supplementary material). We tested the zero-shot classification accuracy of two models, OpenAI’s generative pre-trained transformer (GPT) models gpt-3.5-turbo-0301 and gpt-4-0301 (OpenAI, 2023 ). These achieved 88 and 84% accuracy respectively on a hand-annotated 100-story test set. We therefore applied the 3.5 model to the rest of the story synopses, as illustrated in the Results section.

Visual characteristics

We extracted 117 shots on average (ranging from 20 to 247) per newsreel video, with 126 frames (5 s) per shot on average (ranging from 4 to 4508 frames or 0.2 to 180 s). Representing each shot with one frame, the corpus consists of 205.678 frames in total. We used a pre-trained ResNet Convolutional Neural Network (CNN), to embed the extracted video frames in high-dimensional feature space. The original training set for the ResNet50 is ImageNet (Deng et al., 2009 ), a standard collection of contemporary images, and here we apply it to a collection of low-resolution mostly grayscale images. To identify clusters of visually similar frames and detect common themes across reels we projected the embedding space in 2D using common dimension reduction methods such as t-SNE (van der Maaten and Hinton, 2008 ) and UMAP (McInnes et al., 2020 ). Using the Collection Space Navigator (Ohm et al., 2023 ), an interactive open source tool for exploring image collections, was instrumental in exploring the large-scale visual data and gaining new insights to it. We also visualised all the newsreels by sequencing one frame per shot next to one another, effectively in this way creating a storyboard covering all the examined newsreels. In this part we used standard methods with known biases (see, for example Studer et al., 2019 ).

From the results of the Named Entity Recognition (NER) we extracted mentions of cities. We used Wiktionary and authors’ knowledge of Russian grammar to extract additional name-derivative words related to cities. Using this list, we counted mentions of cities in the story descriptions. We qualitatively distinguish five types of city mentions: a) city itself and city dwellers; b) organisations located in the city and named after it; c) names of a region named after the capital (for example ‘Leningrad oblast’) and organisations located there; d) toponyms named after the city which are not located there or in its vicinity including entities, treatises, and historical events (for example ‘Warsaw Pact’); e) not a mention (coincidences and homonyms). We added geo-coordinates taken from Wikipedia to the list of cities to visualise them on a map.

Crew composition

We used newsreel crew metadata to construct a directed graph of co-working relations (Verhoeven et al., 2020 ) where directors and other crew members act as nodes, and edges indicate collaboration on a newsreel issue. The edge direction is drawn from the director to all other crew members and signifies hiring and supervisory relationships. We utilised Levenshtein distance (Levenshtein, 1965 , see also Navarro, 2001 ) to detect potentially misspelt duplicate names and manually checked the need to merge nodes. The crew dataset contains information about 1251 people who worked on 1730 newsreel productions during 1954–1991 across different positions: director (1740 roles by 104 persons), cinematographer (15,145 roles by 1132 persons) and other crew (editors, sounds designers, etc.; 158 roles by 45 persons). Notably, a small portion of staff work across different roles. The dataset results in a network with 1251 unique person nodes and 15,425 person-to-person links. The first nine years of the data collection period were omitted from network analysis due to inconsistent data.

Cinematic and topic trends

The cinematic and topic trends of the News of the Day data show that newsreel production and release as measured by the number of newsreel issues appears to be stable over fifty years (Fig. 3a ) with consistent content shares dedicated to different topics (Fig. 3e ). The first and last few years (1945–1953 and 1990–1992) look somewhat different, but they have much less data than the rest of the period (Fig. 3a ). Newsreel issue numbers recorded, leased, and preserved in the sparse available data before 1954 seem to indicate that newsreels were produced more or less weekly during that period, but only a tiny fragment has been stored and/or digitised (Fig. 3a ). The absence of data before 1954 most likely relates to the ‘de-Stalinization’ of film materials after Stalin’s death in 1953, which included the confiscation of materials with excessive references to the former leader (Heftberger, 2018 ). During 1954–1986, the weekly production was stable, and newsreels were archived, kept, and later digitised systematically (apart from 1965 with missing data). From 1987, the annual number of produced newsreels decreased by half. The 1987 drop in newsreel production volumes coincides with the time of perestroika characterised by economic turbulence and the rethinking of the Soviet media ecosystem (Rodgers, 2014 ).

Topic-wise, the shares of political, economic, agricultural, and social news, classified using the zero-shot prediction approach, remained relatively stable until the mid-1980s when the social, and later political themes began to take more room of the preserved newsreels (Fig. 3f ). The trend shows an annual rhythm (Fig. 3d ), where social news topics usually increased around issue numbers 8–9, which coincided with International Women’s Day, and around issue numbers 48–52 coinciding with the New Year, both officially recognised celebrations in the Soviet Union. Also the topic of agriculture was more prominent around issues 30–40 published in August and September, which were the most important months of harvest.

With a closer look, it is possible to identify subtle changes across the observed period. Although the annual number of issues remained relatively stable during 1954–1986, the number of news stories per issue, determined based on the textual outlines in the metadata, decreased gradually during this time (Fig. 3a ). Also the number of shots in a newsreel decreased over time (Fig. 3b ), while the mean length of shots started to increase towards the end of the period (Fig. 3c ). These results show a contrary trend to the findings of scholars studying Hollywood feature films that indicate shortening shot lengths towards the end of the 20th century (Cutting et al., 2011 ). The reasons for the ‘stagnating’ Soviet newsreel dynamics should be further explored, with candidates obviously including the availability of film material of extended length, and labour cost in post production, such as cutting and composition. While we provide preliminary exploratory results here, quantitative data like these also naturally allow for the testing of specific hypotheses.

Our examination of the central frames of each shot reveals recurring visual patterns that repeat during the whole studied period (Fig. 4 ). Laying out all the frames of every issue into a storyboard shows subtle length and darkness variation of the (digitised) film material, as well as the launch of colour film in the mid-1980s (Fig. 4a right). Placing the frames in the order of year, issue, and scene number allows for comparing the recurring patterns and changes of the newsreel series. For example, the closeup of the storyboard shows that the opening title frames were customarily followed by frames showing a city scene, indicating the place of the news story. This prelude was followed by scenes depicting activities, such as leaders meeting each other (Fig. 4a left). Using the ResNet50 CNN embedding to extract visual features from the central frame of each shot allows us to examine visual similarities across reels. A UMAP projection of the embedded frames reveals aspects of these similarities at least at a coarse-grain level (Fig. 4b ). Consequently the UMAP allows for visual examination, grouping, and annotation of the most prominent image types in the collection, such as “Nature”, “Monumental gatherings”, “People in meetings”, “Closeups of people at work”, ”Industrial production”, “Title frames and other texts”, and “City views”.

a A storyboard of all newsreel issues, x-axis shot number, y-axis publication years and issue numbers in ascending order. The layout of all issues (4a right) shows the temporal variation of issue lengths and the closeup of the storyboard ( a left) visualises the first scenes of issues 6–14 from 1970. b A UMAP projection of ResNet50 embedding of all central frames of each shot with seven most prominent image clusters named by the authors as (1) “Nature”; (2) “Monumental gatherings”; (3) “People in meetings”; (4) “Closeups of people at work”; (5) ”Industrial production”; (6) “Title frames and other texts”; and (7) “City views”. We used the Collection Space Navigator (Ohm et al., 2023 ), i.e. a flexible open-source user interface, for examining the frames and to produce the figure.

City mentions

Our examination of the cities mentioned in the textual descriptions of the newsreel metadata is summarised in Fig. 5 . Spatially, it demonstrates a heavy emphasis on Europe, both within the Soviet Union and globally, while the Asian part of the Soviet Union in East of the Ural Mountains, was far less covered, matching its lower population rates (Fig. 5a, b ). Outside the Soviet Union, the Warsaw Pact socialist countries are the most frequently covered (36% of all mentions despite being 3% of world population in 1970), as well as ‘neutral’ capitalist countries such as Austria and Finland (9% of all mentions despite being less than 0.4% of world population) (Fig. 5 a, b, d). These findings match the consensus among historians studying Soviet history generally (Koivunen, 2016 ; Gilburd, 2013 ; Turoma and Waldstein, 2013 ). Timewise, the number of mentions per year trends downwards (Fig. 5c–e ), which matches the general decrease in the number of stories per year (Fig. 3a ) and is mostly due to the newsreel issues typically having fewer and longer stories in the 1970s and the 1980s than in the earlier period. It is, however, noteworthy that the number of mentions of foreign cities is shrinking even faster (Fig. 5 c, e), emphasising the decline of the fraction of stories dedicated to international events after around 1960. The temporal patterns for some cities demonstrate a variety of interesting qualitative behaviour (Fig. 5e ). Constant popularity of Leningrad/St. Petersburg seems natural in the view of its importance as the second-largest city in the USSR and the “cradle of the revolution”, the upward trend in the mentions of Minsk correlates with the rapid growth of its population in the period under consideration, and the bump in the popularity of Krasnoyarsk in the 1960s coincides with the building of the Krasnoyarsk Hydroelectric Dam, which was a topic of multiple newsreel stories. The decline of the mentions of Odesa require further historical analysis. The data for individual cities is rather sparse and noisy so extracting statistically significant information from it requires application of advanced statistical techniques and will be done in detail elsewhere.

Map showing all the cities mentioned in 1944–1992, ( a ) globally and ( b ) in Europe. The bubble size indicates the number of mentions. c Average number of mentions of top 50 cities per 1000 stories, the red line is the Soviet cities, and the blue line foreign cities. d Heatmap of city mentions per year for the top 50 most-mentioned cities (Moscow excluded due to heavy overrepresentation). e Heatmap of the top-50 most mentioned cities (Moscow excluded) per 1000 stories in the periods of 1954–1964, 1966–1976, and 1977–1992.

The analysis of newsreel production crews reveals production labour market dynamics and labour division between genders over time. Newsreel production crew numbers (Fig. 6a ) closely follow newsreel production volumes (Fig. 3a ), with ten people working on a newsreel on average. Directors who lead the productions are expectedly vastly outnumbered by other crews since newsreels contain multiple stories often shot by different cinematographers (on average nine versus a single director per newsreel). The historical labour market features several prominent directors, who lead multiple teams (as seen from high degree-centrality nodes in the director–crew network and node degree distribution in Fig. 6d–e ), and who pursue long-lasting careers (Fig. 6c ). The analysis of director gender composition reveals the existence of three distinct periods: gender equality during 1945–1959, a women director’s era during 1960–1974, and men director’s era during 1975–1992 (Fig. 6b ).

a Number of individuals working on newsreel production over time, coloured by role. b Number of individuals working as directors over time, coloured by the assumed gender (women and men). c Director career longevity for the top-20 most productive directors. d Newsreel production crew network during 1954–1991, edges drawn from directors to other crew, coloured by role. e Degree distribution for the unipartite directed newsreel production crew network, both axes in logarithm.

Merging and explaining

As we have shown above, each dimension of analysis reveals new avenues for further qualitative and quantitative enquiry. In addition, analysing similar trends, interrelated themes, or temporal sequences overarching different dimensions of analysis, including combining them in statistical modelling, may help to explain the studied phenomenon better. In our case study, interested in the worldviews portrayed in the Soviet newsreels, bringing the results from different dimensions of analysis together points out a period with emerging shifts. The most prominent temporal change, found across all dimensions of analysis, was the time of perestroika , which introduced major political and cultural changes in the Soviet Union (1985–1991). Although some of the identified changes during this period, such as the launch of colour film (Fig. 4a ), likely had little to do with the political changes, the dimensions of analysis show how profoundly the time of change was affecting different spheres of society. The number of yearly newsreel issues was cut in half, and the published issues contained far fewer news stories (1–3 stories per newsreel against the earlier number of 8–10 stories, Fig. 3a ). Simultaneously, the number of filmmakers producing newsreels was rapidly decreasing following the shrinking newsreel production (Fig. 6a, b ). It is possible that the collapsing Soviet economy and decentralising cultural policy together with the prevalence of television overran the outdated media of newsreels in the era characterised by a gradual increase of freedom of speech and press (Rodgers, 2014 ). Digging deeper via qualitative inspection, we can see that in the 1990s the newsreel contents became focused on political meetings held in Moscow, which is visible in the emphasis on political and social topics covered (Fig. 3f ), and in geographical concentration on only a few cities (Fig. 5d ). The newsreels of the perestroika were characterised by long shots of speeches (Fig. 3c ), as many newsreel issues at the time covered extensively the political discussions on the direction of the country, which provided the public in a way first-hand knowledge of who said what in the discussions. Clearly, the worldview that the News of the Day depicted to its audiences, changed in many ways.

While many of these observations are not novel to studies on Soviet history, seeing a signal pointing out the particularity of this period in all the dimensions of analysis is important. It shows that the change of policy in the mid-1980s had way more profound effects than for example, the change of leadership from Khrushchev to Brezhnev in 1964. Quantitative analysis of large amounts of data provides the necessary contextualisation emphasising the specificity of the period, which would not be possible to show in such a concrete manner by a solely qualitative study. The signal evidence furthermore becomes visible to a broader audience, beyond experts whose formation requires years of qualitative research. Additionally, harnessing the findings of the different yet complementary dimensions of analysis together reveals trends that may be interrelated. For example, the diminishing number of crew members can partially explain the decreasing number of issues and shots, and the concentration of the newsreels in only a few cities. With fewer people, it was impossible to cover a larger volume of news material from different places. Focusing only on one dimension of analysis in our case study would not have revealed this possible connection. Finally, all these findings can be enhanced by further qualitative enquiry referencing back to the historical dimensions of the data corpus used in this study and in preceding studies, as well as statistical modelling focusing on any particular questions of interest.

Discussion and conclusions

In this paper, we proposed a framework for studying historical newsreels specifically and audiovisual news more generally in large quantities, while simultaneously maintaining an understanding of the multimodality and complexity of audiovisual data and the relational way of meaning-making associated with them. Analysing newsreels using long-term and large-scale data is beneficial for our understanding of societies in question of the global information landscape, its geographical differences, and the generic features of news content. As our case study on worldviews in the News of the Day newsreel series produced weekly in the Soviet Union during 1944–1992 has demonstrated, combining different dimensions of quantitative analysis together with qualitative enquiry, helps to understand newsreel contents in a long continuum and in a more nuanced way than previously achieved. Quantitative visualisations driven by computational analysis methods help to contextualise smaller-scale qualitative analysis, simultaneously as qualitative analysis allows to explain the detected long-term changes and their nuances. Acknowledging the complexity of the data, i.e. that new quality emerges from large quantities of data, allows for a better-rounded understanding of audiovisual culture. Necessitating a range of co-authors, our approach makes an argument for multidisciplinary research and advocates studying culture by combining different methods and approaches.

The outlined framework is the first attempt to combine the different disciplinary approaches into a comprehensive study of newsreels. Weaknesses in our proposition may of course become apparent when applying it in a variety of studies, yet we argue that this too will necessitate similar multidisciplinary expertise, collaboration, and negotiation. The case study we have presented here provides a brief glimpse into the application of the framework. One limitation of our approach is that while it selects dimensions of analysis intuitively, yet based on expertise of the crowd of co-authors, it does not explore in detail the selection of analysis methods. This will be further explored in the future. In our case study, we have focused on preliminary exploratory enquiry and less on confirmatory analysis or hypothesis testing. Examining the different ways to compare a variety of datasets, coming from different sources, has not been touched upon in this article, and should be further studied to enhance transnational approaches to the study of newsreels. This article has proposed a methodological solution for studying audiovisual news, while the questions of copyright and access to comprehensive collections of audiovisual data and corresponding metadata continue to be major obstacles to further development of this field (Arnold et al., 2021 ). A further potential hurdle in scaling the approach is the necessity of access to high-performance computation infrastructure for the effective processing of large-scale audiovisual data. In sum, however, with this framework, we hope to open a discussion on how to best study audiovisual news in long-term and large-scale data.

Data availability

The data is available at the company’s website ( https://www.net-film.ru/ ). The code used for accessing the data is available at the supplementary materials.

Althaus SL, Britzman K (2018) Researching the issued content of American newsreels. In: Vande Winkel R, Chambers C, Jönsson M (eds.) Researching Newsreels: local, national and transnational case studies. Palgrave Macmillan, Switzerland, p 247–263

Chapter   Google Scholar  

Althaus SL, Usry K, Richards S, Van Thuyle B, Aron I, Huang L, Leetaru K et al. (2018) Global news broadcasting in the pre-television era: a cross-national comparative analysis of World War II Newsreel coverage. J Broadcast Electron Media 62(1):147–167. https://doi.org/10.1080/08838151.2017.1375500

Article   Google Scholar  

Angelov D (2020) Top2Vec: distributed representations of topics, arXiv, https://doi.org/10.48550/arXiv.2008.09470

Arnold T, Scagliola S, Tilton L, and Van Gorp J (2021) Introduction: special issue on audiovisual data in DH. Digit Hum Q 15 (1). http://digitalhumanities.org/dhq/vol/15/1/000541/000541.html

Arnold T, Tilton L (2019) Distant viewing: analyzing large visual corpora. Digit Scholarsh Hum 34:i3–16. https://doi.org/10.1093/llc/fqz013

Arnold T, Tilton L (2023) Distant viewing: computational exploration of digital images. MIT Press, Cambridge, MA. https://doi.org/10.7551/mitpress/14046.001.0001

Bain M, Nagrani A, Schofield D, Berdugo S, Bessa J, Owen J, Hockings KJ, et al. (2021) Automated audiovisual behavior recognition in wild primates. Sci Adv 7(46). https://doi.org/10.1126/sciadv.abi4883

Bakels J-H, Grotkopp M, Scherer T, Stratil J (2020) Matching computational analysis and human experience: performative arts and the digital humanities. Digit Hum Q 14:4

Google Scholar  

Beals M, Bell E (2020) The atlas of digitised newspapers and metadata: reports from oceanic exchanges. https://doi.org/10.6084/m9.figshare.11560059.v1

Bergström Å, Jönsson M (2018) Screening war and peace: newsreel pragmatism in neutral Sweden, September 1939 and May 1945. In: Vande Winkel R, Chambers C, Jönsson M (eds.) Researching newsreels: local, national and transnational case studies. Palgrave Macmillan, Switzerland, p 157–182

Bhargav S, van Noord N, Kamps J (2019) Deep learning as a tool for early cinema analysis. Proceedings of the 1st workshop on structuring and understanding of multimedia heritage contents. SUMAC ’19. Association for Computing Machinery, New York, NY, USA, p 61–68. https://doi.org/10.1145/3347317.3357240

Book   Google Scholar  

Bojanowski P, Grave E, Joulin A, Mikolov T (2017) Enriching word vectors with subword information. Trans Assoc Comput Linguist 5:135–146. https://doi.org/10.1162/tacl_a_00051

Bordwell D (1991) Making meaning: inference and rhetoric in the interpretation of cinema. Harvard University Press, Cambridge

Brasó G, Cetintas O, Leal-Taixé L (2022) Multi-object tracking and segmentation via neural message passing. Int J Comput Vis 130(12):3035–3053. https://doi.org/10.1007/s11263-022-01678-6

Brissman E, Johnander J, Danelljan M, and Felsberg M (2022) Recurrent graph neural networks for video instance segmentation. Int J Comput Vis. https://doi.org/10.1007/s11263-022-01703-8

Broadwell P, Tangherlini TR (2021) Comparative K-Pop choreography analysis through deep-learning pose estimation across a large video corpus. Digit Hum Q 15:1

Burges J, Armoskaite S, Fox T, Mueller D, Romphf J, Sherwood E, Ullrich M (2021) Audiovisualities out of annotation: three case studies in teaching digital annotation with mediate. Digit Hum Q 15:1

Burghardt M, Heftberger A, Pause J, Walkowski N-O, Zeppelzauer M (2020) Film and video analysis in the digital humanities—an interdisciplinary dialog. Digit Hum Q 14:4

Carrive J, Beloued A, Goetschel P, Heiden S, Laurent A, Lisena P, Mazuet F, et al. (2021) Transdisciplinary analysis of a corpus of French newsreels: the ANTRACT project. Digit Hum Q 15 (1). http://digitalhumanities.org/dhq/vol/15/1/000523/000523.html

Cassirer E (1927) Das symbolproblem und seine stellung im system der philosophie. Z für ÄEsthet Allg Kunstwiss 21:295–322

Chambers C (2018) The Irish Question: newsreels and national identity. In: Vande Winkel R, Chambers C, Jönsson M (eds.) Researching newsreels: local, national and transnational case studies. Palgrave Macmillan, Switzerland, p 265–283

Chambers Ciara, Jönsson Mats, Vande Winkel Roel (eds.) (2018) Researching newsreels: local, national and transnational case studies. Palgrave Macmillan, Switzerland

Chapman, J, Glancy M, and Harper S (2007) The new film history: sources, methods, approaches . Springer

Chávez Heras D (2024) Cinema and machine vision: artificial intelligence, aesthetics and spectatorship. Edinburgh University Press, Edinburgh

Clariana-Rodagut A, Hagener M (2023) Transnational networks of avant-garde film in the interwar period. In: Roig-Sanz D, Rotger N (eds.) Global literary studies: key concepts. De Gruyter, Berlin, p 253–277

Cooper A, Nascimento F, Francis D (2021) Exploring film language with a digital analysis tool: the case of Kinolab. Digit Hum Q 15:1

CUDAN Open Lab Seminar series 2020–2024. https://www.youtube.com/@CUDANLab/videos)

Cutting JE, Brunick KL, DeLong JE, Iricinschi C, Candan A (2011) Quicker, faster, darker: changes in Hollywood film over 75 years. I-Percept 2(6):569–576. https://doi.org/10.1068/2Fi0441aap

Dang S-M (2020) Unknowable facts and digital databases: reflections on the women film pioneers project and women in film history. Digit Hum Q 14:4

Deng J, Dong W, Socher R, Li L-J, Li K, Fei-Fei L (2009) ImageNet: a large-scale hierarchical image database. In Proceedings of the IEEE conference on computer vision and pattern recognition , 248–255. https://doi.org/10.1109/CVPR.2009.5206848

Elo K (2020) Big Data, Bad Metadata: a methodological note on the importance of good metadata in the age of digital history. In: Fridlund M, Oiva M, Paju P (eds.) Digital histories. Emergent approaches within the new digital history. Helsinki University Press, Helsinki, pp 103–111 https://doi.org/10.33134/HUP-5-6

Fickers A (2021) Authenticity: historical data integrity and the layered materiality of digital objects. In: Fickers A, Schafer V, Takats S, Zaagsma G (eds.) Digital roots. Historicizing media and communication concepts of the digital age. De Gruyter Oldenbourg, Berlin, pp 299–312 https://doi.org/10.1515/9783110740202-017

Fickers A, Tatarinov J, Heijden T (2022) Digital history and hermeneutics—between theory and practice: an introduction. In: Fickers A, Tatarinov J (eds.) Digital history and hermeneutics: between theory and practice. De Gruyter Oldenbourg, Berlin, pp 1–19 https://doi.org/10.1515/9783110723991

Fielding R (2009) Newsreels. Encyclopedia of journalism, 3 , SAGE Publications, Thousand Oaks, pp 992–994

Gadamer H-G (2013 (1960)) Truth and method . Bloomsbury Academic

Garrett Cooper Mark, Levavy SaraBeth, Melnick Ross, Williams Mark (eds.) (2018) Rediscovering U.S. newsfilm: cinema, television, and the archive. Routledge, London

Gentles SJ, Charles C, Nicholas DB, Ploeg J, McKibbon KA (2016) Reviewing the research methods literature: principles and strategies illustrated by a systematic overview of sampling in qualitative research. Syst Rev 5(1):172. https://doi.org/10.1186/s13643-016-0343-0

Article   PubMed   PubMed Central   Google Scholar  

Gilburd E (2013) The Revival of Soviet Internationalism in the Mid to Late 1950s. In: Gilburd E, Kozlov D (eds.) The Thaw: Soviet Society and Culture during the 1950s and 1960s. University of Toronto Press, Toronto, p 362–401

Grootendorst M (2022) BERTopic: neural topic modeling with a class-based TF-IDF procedure. arXiv https://doi.org/10.48550/arXiv.2203.05794

Grósz T, Kallioniemi N, Kiiskinen H, Laine K, Moisio A, Römpötti T, Virkkunen A, Salmi H, Kurimo M, Laaksonen J (2022) Tracing signs of urbanity in the finnish fiction film of the 1950s: toward a multimodal analysis of audiovisual data. In: Proceedings of the 6th digital humanities in the nordic and Baltic countries conference (DHNB 2022), 3232: 63–78. https://ceur-ws.org/Vol-3232/paper05.pdf

Gulshad S, Long T, van Noord N (2023) Hierarchical explanations for video action recognition. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, p 3703–3708. https://doi.org/10.48550/arXiv.2301.00436

Hanjalic A (2002) Shot-boundary detection: unraveled and resolved? IEEE Trans Circuits Syst Video Technol 12(2):90–105. https://doi.org/10.1109/76.988656

Hasegawa T, Kato S (2019) Dialogue mood estimation from speech sounds clusterized with speakers’ personality traits. In Proceedings of the IEEE 8th global conference on consumer electronics (GCCE) : 399–401. https://doi.org/10.1109/GCCE46687.2019.9015238

He K, Zhang X, Ren S, Sun J (2015) Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), p. 770–778. https://doi.org/10.48550/arXiv.1512.03385

Heftberger A (2018) Digital humanities and film studies. Visualizing Dziga Vertov’s Work. Quantitative methods in the humanities and social sciences. Springer International Publishing, Switzerland, https://doi.org/10.1007/978-3-030-02864-0_4

Hickethier K (2016) The creation of cultural identity through weekly newsreels in Germany in the 1950s: as illustrated by the NEUE DEUTSCHE WOCHENSCHAU and the UFA-WOCHENSCHAU (With a Side Glance at the DEFA Weekly Newsreel DER AUGENZEUGE). In: Imesch K, Schade S, Sieber S (eds.) Constructions of cultural identities in newsreel cinema and television after 1945. Transcript Verlag, Bielefeld, p 39–54. https://doi.org/10.14361/9783839429754-003

Hielscher E (2020) The phenomenon of interwar city symphonies: a combined methodology of digital tools and traditional film analysis methods to study visual motifs and structural patterns of experimental-documentary city films. Digit Hum Q 14:4

Hoffmann K (2018) The commentary makes the difference: an analysis of the Suez War in East and West German Newsreels, 1956. In: Vande Winkel R, Chambers C, Jönsson M (eds.) Researching newsreels: local, national and transnational case studies. Palgrave Macmillan, Switzerland, p 77–92

Howell MC, Prevenier W (2001) From reliable sources: an introduction to historical methods. Cornell University Press, Ithaca

Hu X, Tang C, Chen H, Li X, Li J, Zhang Z (2022) Improving image segmentation with boundary patch refinement. Int J Comput Vis 130(11):2571–2589. https://doi.org/10.1007/s11263-022-01662-0

Imesch Kornelia, Schade Sigrid, Sieber Samuel (eds.) (2016) Constructions of cultural identities in newsreel cinema and television after 1945. Transcript Verlag, Bielefeld, 10.14361/9783839429754

Kandukuri RK, Achterhold J, Moeller M, Stueckler J (2022) Physical representation learning and parameter identification from video using differentiable physics. Int J Comput Vis 130(1):3–16. https://doi.org/10.1007/s11263-021-01493-5

Koivunen P (2016) Friends, ‘potential friends,’ and enemies: reimagining Soviet relations to the First, Second, and Third Worlds at the Moscow 1957 youth festival. In: Babiracki P, Jersild A (eds.) Socialist internationalism in the cold war. Springer International Publishing, New York, pp 219–47 https://doi.org/10.1007/978-3-319-32570-5_9

Kong Y, Fu Y (2022) Human action recognition and prediction: a survey. Int J Comput Vis 130(5):1366–1401. https://doi.org/10.1007/s11263-022-01594-9

Kordopatis-Zilos G, Tzelepis C, Papadopoulos S, Kompatsiaris I, Patras I (2022) DnS: distill-and-select for efficient and accurate video indexing and retrieval. Int J Comput Vis 130(10):2385–2407. https://doi.org/10.1007/s11263-022-01651-3

Lazer DMJ, Baum MA, Benkler Y, Berinsky AJ, Greenhill KM, Menczer F, Metzger MJ, Nyhan B, Pennycook G, Rothschild D (2018) The science of fake news. Science 359(6380):1094–1096

Article   ADS   CAS   PubMed   Google Scholar  

Levenshtein VI (1965) Binary codes capable of correcting spurious insertions and deletions of ones. Probl Inf Transm 1(1):8–17

Liu L, Ouyang W, Wang X, Fieguth P, Chen J, Liu X, Pietikäinen M (2020) Deep learning for generic object detection: a survey. Int J Comput Vis 128(2):261–318. https://doi.org/10.1007/s11263-019-01247-4

van der Maaten L, Hinton G (2008) Visualizing data using T-SNE. J Mach Learn Res 11:9

Maltby R, Biltereyst D, Meers P (eds.) (2011) Explorations in new cinema history: approaches and case studies . Wiley

Manovich L (2020) Cultural analytics. MIT Press, Cambridge, MA

Masson E, Olesen CG, Noord Nvan, Fossati G (2020) Exploring digitised moving image collections: the SEMIA project, visual analysis and the turn to abstraction. Digit Hum Q 14:4

McInnes L, Healy J, Melville J (2020) UMAP: uniform manifold approximation and projection for dimension reduction. ArXiv http://arxiv.org/abs/1802.03426

Morgan M (2008) Reality and media reality. The international encyclopedia of communication , John Wiley & Sons, New Jersey. https://doi.org/10.1002/9781405186407.wbiecr015

Mourchid Y, Renoust B, Roupin O, Văn L, Cherifi H, Hassouni ME (2019) Movienet: a movie multilayer network model using visual and textual semantic cues. Appl Netw Sci 4(1):121. https://doi.org/10.1007/s41109-019-0226-0

Navarro G (2001) A guided tour to approximate string matching. ACM Comput Surv 33(1):31–88. https://doi.org/10.1145/375360.375365

Navitski R, Poppe N (2017) Cosmopolitan film cultures in Latin America, 1896–1960. Indiana University Press, Indiana

Nazir S, Cagali T, Sadrzadeh M, Newell C (2020) Audiovisual, genre, neural and topical textual embeddings for TV programme content representation. In: Proceedings of the IEEE international symposium on multimedia (ISM) : p 197–200. https://doi.org/10.1109/ISM.2020.00041

van Noord N (2022) A survey of computational methods for iconic image analysis. Digit Scholarsh Hum 37(4):1316–1338. https://doi.org/10.1093/llc/fqac003

van Noord N, Olesen CG, Ordelman R, Noordegraaf J (2021) Automatic annotations and enrichments for audiovisual archives. ICAART 1:633–640

van Noord N, Wevers M, Blanke T, Noordegraaf J, and Worring M (2022) An analytics of culture: modeling subjectivity, scalability, contextuality, and temporality. arXiv https://doi.org/10.48550/arXiv.2211.07460

Oberbichler S, Boroş E, Doucet A, Marjanen J, Pfanzelter E, Rautiainen J, Toivonen H, Tolonen M (2022) Integrated interdisciplinary workflows for research on historical newspapers: perspectives from humanities scholars, computer scientists, and librarians. J Assoc Inf Sci Technol 73(2):225–239. https://doi.org/10.1002/asi.24565

Article   PubMed   Google Scholar  

Ohm T, Canet Solá M, Karjus A, and Schich M (2023) Collection space navigator: interactive visualization interface for multidimensional datasets. https://collection-space-navigator.github.io/

Olesen CG, Kisjes I (2018) From text mining to visual classification: rethinking computational new cinema history with Jean Desmet’s digitised business archive. TMG J Media Hist 21(2):127–145. https://doi.org/10.18146/2213-7653.2018.370

Olesen CG, Masson E, Van Gorp J, Fossati G, Noordegraaf J (2016) Data-driven research for film history: exploring the Jean Desmet collection. Mov Image: J Assoc Mov Image Arch 16(1):82–105. https://doi.org/10.5749/movingimage.16.1.0082

van Oort T, Jernudd Å, Lotze K, Pafort-Overduin C, Biltereyst D, Boter J, Dibeltulo S et al. (2020) Mapping film programming across post-war Europe (1952): arts and media. Res Data J Hum Soc Sci 5(2):109–125. https://doi.org/10.1163/24523666-00502009

OpenAI (2023) GPT-4 technical report. arXiv https://doi.org/10.48550/arXiv.2303.08774

Opoku A, Ahmed V, Akotia J (2016) Choosing an appropriate research methodology. In: Ahmed V, Opoku A, Aziz Z (eds.) Research methodology in the built environment: a selection of case studies. Routledge, London, p 32–50

Paju P, Oiva M, Fridlund M (2020) Digital and distant histories: introducing emergent approaches within the new digital history. In: Fridlund M, Oiva M, Paju P (eds.) Digital readings of history. history research in the digital era. Helsinki University Press, Helsinki, p 3–18. https://doi.org/10.33134/HUP-5

Park S, Bellur A, Han DK, Elhilali M (2021) Self-training for sound event detection in audio mixtures. In Proceedings of the ICASSP 2021–2021 IEEE international conference on acoustics, speech and signal processing (ICASSP) : 341–345. https://doi.org/10.1109/ICASSP39728.2021.9414450

Pérez AF, Sanguineti V, Morerio P, Murino V (2020) Audio-visual model distillation using acoustic images. In: Proceedings of the IEEE winter conference on applications of computer vision (WACV) : 2843–2852. https://doi.org/10.1109/WACV45572.2020.9093307

Pozdorovkin M (2012) Khronika: Soviet newsreel at the dawn of the information age . PhD thesis, Harvard University. https://dash.harvard.edu/handle/1/9823973

Pozharliev L, Gallo González D (2018) Martin Luther King’s assassination in Spain’s NO-DOs and in Bulgaria’s Kinopregledi. In: Vande Winkel R, Chambers C, Jönsson M (eds.) Researching newsreels: local, national and transnational case studies. Palgrave Macmillan, Switzerland, p 93–117

Pustu-Iren K, Sittel J, Mauer R, Bulgakowa O, Ewerth R (2020) Automated visual content analysis for film studies: current status and challenges. Digit Hum Q 14:4

Rasheed Z, Shah M (2003) Scene detection in Hollywood movies and TV shows. In: IEEE computer society conference on computer vision and pattern recognition, 2:II–343. https://doi.org/10.1109/CVPR.2003.1211489

Ravessoud C, Haver G (2016) Art exhibitions through newsreels: an avatar for identity politics (1945–1960). In: Imesch K, Schade S, Sieber S (eds.) Constructions of cultural identities in newsreel cinema and television after 1945. Transcript Verlag, Bielefeld, p 101–116. https://doi.org/10.14361/9783839429754-006

Ren H, Renoust B, Viaud M-L, Melançon G, and Satoh S (2018) Generating ‘Visual Clouds’ from multiplex networks for TV news archive query visualization. In: Proceedings of the International Conference on Content-Based Multimedia Indexing (CBMI) : 1–6. https://doi.org/10.1109/CBMI.2018.8516482

Rodgers J (2014) From Perestroika to Putin: journalism in Russia. In: Media Independence , Routledge, London, p 237–256

Roth-Ey K (2011) Moscow prime time: how the Soviet Union built the media empire that lost the cultural cold war. Cornell University Press, Ithaca

Salmi H (2020) What is digital history? Polity, Medford

Salt B (1974) Statistical style analysis of motion pictures. Film Q 28(1):13–22. https://doi.org/10.2307/1211438

Sampaio S (2022) From propaganda to attraction: reassessing the role of the public in the Newsreel Jornal Português (1938–1951). Hist J Film Radio Telev 42(3):470–492. https://doi.org/10.1080/01439685.2021.1976918

Sanguineti V, Morerio P, Del Bue A, Murino V (2022) Unsupervised synthetic acoustic image generation for audio-visual scene understanding. IEEE Trans Image Process 31:7102–7115. https://doi.org/10.1109/TIP.2022.3219228

Article   ADS   PubMed   Google Scholar  

Schich M (2017) The hermeneutic hypercycle. In: Brockman J (ed.) Know this: today’s most interesting and important scientific ideas. Harper Perennial, New York, p 561–563

Schich M (2019) Cultural analysis situs. ART-Dok eprint https://doi.org/10.11588/artdok.00006347

Senocak A, Oh T-H, Kim J, Yang M-H, Kweon IS (2018) Learning to localize sound source in visual scenes. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition : 4358–4366. https://doi.org/10.1109/CVPR.2018.00458

Snow CP (2001) The two cultures. Cambridge University Press, London (1959)

Sommer NM, Velipasalar S, Hirshfield L, Lu Y, Kakillioglu B (2020) Simultaneous and spatiotemporal detection of different levels of activity in multidimensional data. IEEE Access 8:118205–118218. https://doi.org/10.1109/ACCESS.2020.3005633

Studer L, Alberti M, Pondenkandath V, Goktepe P, Kolonko T, Fischer A, Liwicki M, and Ingold R (2019) A comprehensive study of imagenet pre-training for historical document image analysis. In: Proceedings of the international conference on document analysis and recognition (ICDAR) : 720–725. https://doi.org/10.1109/ICDAR.2019.00120

Treveri Gennari D, Sedgwick J (2022) Five Italian cities: comparative analysis of cinema types, film circulation and relative popularity in the mid-1950s. In: Sedgwick J (ed.) Towards a comparative economic history of cinema, 1930–1970. Springer International Publishing, Cham, p 249–279. https://doi.org/10.1007/978-3-031-05770-0_9

Tsivian Y (2009) Cinemetrics, part of the humanities’ cyberinfrastructure. In: Ross M, Grauer M, Freisleben B (eds.) Digital tools in media studies. analysis and research. An overview. Transcript, Bielefeld, p 93–100

Turoma Sanna, Waldstein Maxim (eds.) (2013) Empire de/centered: new spatial histories of Russia and the Soviet Union. Ashgate, Farnham

Tworek HJS (2019) News from Germany: the competition to control world communications, 1900–1945. News from Germany. Harvard University Press, Harvard, https://doi.org/10.4159/9780674240728

Valverde, FR, Valeria Hurtado J, and Valada A (2021) There is more than meets the eye: self-supervised multi-object detection and tracking with sound by distilling multimodal knowledge. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (CVPR) : 11607–11616. https://doi.org/10.1109/CVPR46437.2021.01144

Vande Winkel R, Biltereyst D (2018) Newsreel production, distribution and exhibition in Belgium, 1908-1994. In: Vande Winkel R, Chambers C, Jönsson M (eds.) Researching newsreels: local, national and transnational case studies. Palgrave Macmillan, Switzerland, p 231–246

Veldi M, Bell S, Kuhlmann F (2019) Five-year plan in four: Kolkhoz propaganda in film and documentaries in Estonia. SHS Web Conf 63:10002. https://doi.org/10.1051/shsconf/20196310002

Verhoeven D, Coate B, Zemaityte V (2019) Re-distributing gender in the global film industry: beyond #metoo and #methree. Media Ind J 6(1):135–155. https://doi.org/10.3998/mij.15031809.0006.108

Verhoeven D, Musial K, Palmer S, Taylor S, Abidi S, Zemaityte V, Simpson L (2020) Controlling for openness in the male-dominated collaborative networks of the global film industry. PLOS One 15(6):e0234460. https://doi.org/10.1371/journal.pone.0234460

Article   CAS   PubMed   PubMed Central   Google Scholar  

Wan S, Xu X, Wang T, Gu Z (2021) An intelligent video analysis method for abnormal event detection in intelligent transportation systems. IEEE Trans Intell Transport Syst 22(7):4487–4495. https://doi.org/10.1109/TITS.2020.3017505

Wang D, Zhang S (2022) Unsupervised person re-identification via multi-label classification. Int J Comput Vis 130(12):2924–2939. https://doi.org/10.1007/s11263-022-01680-y

Werenskjold R (2018) Around the world: the first Norwegian newsreel, 1930–1941. In: Vande Winkel R, Chambers C, Jönsson M (eds.) Researching newsreels: local, national and transnational case studies. Palgrave Macmillan, Switzerland, p 51–75

Wevers M (2021) Scene detection in De Boer historical photo collection. In: Proceedings of the 13th international conference on agents and artificial intelligence (ICAART 2021) , 1:601–610. https://doi.org/10.5220/0010288206010610

Wevers M, Smits T (2020) The visual digital turn: using neural networks to study historical images. Digit Scholarsh Hum 35(1):194–207. https://doi.org/10.1093/llc/fqy085

Williams M, Bell J (2021) The Media Ecology Project: Collaborative DH synergies to produce new research in visual culture history. Digit Hum Q 15:1

Winston B (2018) Wofull News from Wales: details at 11. news, newsreels, bulletins and documentaries. In: Vande Winkel R, Chambers C, Jönsson M (eds.) Researching newsreels: local, national and transnational case studies. Palgrave Macmillan, Switzerland, p 15–33

Yang K, Russell B, Salamon J (2020) Telling left from right: learning spatial correspondence of sight and sound. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (CVPR) : 9929–9938. https://doi.org/10.1109/CVPR42600.2020.00995

Ye K, Kovashka A (2022) Weakly-Supervised Action Detection Guided By Audio Narration. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops (CVPRW) : 1527–1537. https://doi.org/10.1109/CVPRW56347.2022.00159

Zeiler M (1999) Modeling our world: the ESRI guide to geodatabase design . ESRI Press

Zhang Q, Chan AB (2022) 3D crowd counting via geometric attention-guided multi-view fusion. Int J Comput Vis 130(12):3123–3139. https://doi.org/10.1007/s11263-022-01685-7

Zhao B, Gong M, Li X (2021) AudioVisual video summarization. IEEE Transactions on Neural Networks and Learning Systems : 1–8. https://doi.org/10.1109/TNNLS.2021.3119969

Download references

Acknowledgements

European Union Horizon2020 research and innovation programme ERA Chair project for Cultural Data Analytics CUDAN (Project no. 810961); National programme of the Ministry of Education and Research of the Republic of Estonia for April 2022–February 2023 (Project no. EKKD77); Estonian Research Council Public Value of Open Cultural Data (Project no. PRG1641); European Union Horizon Europe research and innovation programme CresCine—Increasing the International Competitiveness of the Film Industry in Small European Markets (Project no. 101094988).

Author information

Authors and affiliations.

Tallinn University, Tallinn, Estonia

Mila Oiva, Ksenia Mukhina, Vejune Zemaityte, Andres Karjus, Mikhail Tamm, Tillmann Ohm, Mark Mets, Mar Canet Sola & Maximilian Schich

Estonian Business School, Tallinn, Estonia

Andres Karjus

King’s College London, London, UK

Daniel Chávez Heras

University of Tartu, Tartu, Estonia

Helena Hanna Juht

You can also search for this author in PubMed   Google Scholar

Contributions

MO, KM, VZ, TO, MT, MM, AK, DCH, MCS, and MS designed research, KM and MO acquired the data, MO, KM, VZ, MT, TO, and HHJ prepared the data, MO, KM, VZ, AK, MT, TO, MM, and MS performed the analysis, and MO, VZ, and MS wrote the manuscript. MO is the first author. MS is the supervising author.

Corresponding author

Correspondence to Mila Oiva .

Ethics declarations

Ethical approval.

This article does not contain any studies with human participants performed by any of the authors.

Informed consent

Competing interests.

The authors declare no competing interests.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary material, supplementary table 1, rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Oiva, M., Mukhina, K., Zemaityte, V. et al. A framework for the analysis of historical newsreels. Humanit Soc Sci Commun 11 , 530 (2024). https://doi.org/10.1057/s41599-024-02886-w

Download citation

Received : 14 July 2023

Accepted : 27 February 2024

Published : 25 April 2024

DOI : https://doi.org/10.1057/s41599-024-02886-w

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

• Open access
• Published: 24 April 2024

Access to family planning services and associated factors among young people in Lira city northern Uganda

• Eustes Kigongo 1 ,
• Raymond Tumwesigye 1 ,
• Maxson Kenneth Anyolitho 1 ,
• Marvin Musinguzi 1 ,
• Gad Kwizera 3 ,
• Everlyne Achan 1 ,
• Caroline Kambugu Nabasirye 2 ,
• Samson Udho 2 ,
• Amir Kabunga 4 &
• Bernard Omech 1  

BMC Public Health volume  24 , Article number:  1146 ( 2024 ) Cite this article

Metrics details

Access to family planning services among young people is crucial for reproductive health. This study explores the access and associated factors among young people in Lira City, Northern Uganda.

Methods and materials

A mixed-methods study was conducted in March to April 2022. Quantitative data were collected using a structured questionnaire from 553 participants aged 15–24 years. Qualitative data were obtained through in-depth interviews and focus group discussions. Data analysis included univariate, bivariate, and multivariate analyses for quantitative data, while interpretative phenomenological analysis was used for qualitative data.

Overall, 31.7% of the respondents had a good perceived access to family planning services, with 64.6% reporting perceived availability of FP methods. Challenges included lack of privacy (57.7%), fear of mistreatment (77.2%), and decision-making difficulties (66.2%). Among females, good perceived access to FP services was less likely among urban residents (AOR: 0.22, 95% CI: 0.09–0.53), Christian respondents (AOR: 0.51, 95% CI: 0.01–0.36), Muslim respondents (AOR: 0.07, 95% CI: 0.01–0.55) and respondents with poor attitude to FP services (AOR: 0.39, 95% CI: 0.24–0.64), but more likely among respondents with a sexual a partner (AOR: 4.48, 95% CI: 2.60–7.75). Among males, good perceived access to FP services was less likely among respondents living with parents (AOR: 0.19, 95% CI: 0.05–0.67) but more likely among respondents with good knowledge of FP services (AOR: 2.28, 95% CI: 1.02–5.32). Qualitative findings showed that three themes emerged; knowledge of family planning methods, beliefs about youth contraception and, friendliness of family planning services.

The study revealed a substantial gap in perceived access to family planning services among young people in Lira City. Barriers include privacy concerns, fear of mistreatment, and decision-making difficulties. Tailored interventions addressing urban access, religious beliefs for females, and knowledge enhancement for males are essential. Positive aspects like diverse FP methods and physical accessibility provide a foundation for targeted interventions. Youth-friendly services, comprehensive sexual education, and further research are emphasized for a nuanced understanding and effective interventions in Northern Uganda.

Peer Review reports

Globally, approximately 16 million girls aged 15–19 give birth each year, with 95% of these births occurring in developing countries [ 1 ]. Additionally, annually, 14 million unsafe abortions take place among adolescents, who face various sexual and reproductive health challenges, including early pregnancy, unsafe abortions, sexually transmitted infections (STIs), and sexual abuse, particularly in Sub-Saharan Africa (SSA) [ 2 ]. Family planning (FP) is a critical aspect of global public health, recognized for its impact on maternal and child health, gender equality, and socioeconomic development [ 3 ]. The international community, as reflected in various global health initiatives and sustainable development goals, acknowledges the importance of ensuring universal access to FP services for all individuals, including young people [ 4 ]. This global perspective emphasizes the interconnectedness of reproductive health and broader efforts to achieve sustainable development [ 4 ]. Access to these services is particularly pertinent among young people, who constitute a significant demographic in many Low-and Middle-income Countries (LMICs).

Uganda, boasting one of the world’s youngest and fastest-growing populations, has nearly half (48%) of its estimated 46 million people under the age of 15, significantly surpassing the averages for SSA (43%) and the world (26%) [ 5 ]. Uganda, as a signatory to global health agendas, has made significant strides in promoting FP services [ 6 ]. The National Population Policy, coupled with the National Reproductive Health Policy, reflects the government’s commitment to ensuring access to FP for all citizens [ 7 ]. However, challenges persist, especially in urban areas. An examination of the national context provides insights into the policy circumstances, healthcare infrastructure, and societal norms that shape family planning services’ availability and utilization among young people in Northern Uganda.

According to the Uganda Demographic and Health Survey (UDHS) 2016, 25% of women aged 15–19 and 1% below 15 had initiated childbearing, with the incidence of unplanned pregnancies significantly rising following the shutdown of schools during the COVID-19 pandemic [ 8 , 9 ]. Reported underlying causes of teenage pregnancy include gender inequality, restricted freedom for girls to voice their concerns, school dropout, and limited access to contraception and knowledge [ 10 ]. Unintended teenage pregnancies can have severe adverse effects on well-being, leading to maternal morbidity and mortality related to childbirth and unsafe abortion [ 11 ]. Moreover, these pregnancies contribute to social consequences such as stigma and discrimination, accounting for 59% of school dropouts in Uganda in 2012, potentially hindering education and future employment opportunities [ 12 , 13 ]. Reports by the World Bank and the World Health Organization (WHO) emphasize the association of adolescents-childbearing with social stigma, lifelong poverty, and health risks, necessitating a comprehensive approach to address these issues [ 14 , 15 ].

Uganda’s current health sector strategy aims to expand youth-friendly health services (YFHS) and promote adolescent sexual and reproductive health and rights information in schools, ensuring access to FP information and services irrespective of age, marital status, or school status [ 16 ]. The country plans to increase access to modern contraceptive use and reduce unmet need for contraception in the coming years [ 17 ]. According to WHO guidelines, addressing the underlying factors, including the timing of first sex and marriage, effective contraceptive use, and the socio-cultural and economic environment, is crucial for delaying childbearing and expanding FP access to adolescents [ 18 ].

Notably, northern Uganda bears one of the highest burdens of adolescent pregnancies, with reports indicating a significant percentage of unintended pregnancies in Lira district in 2019 (33.3%) and a notable number of teenage girls visiting antenatal clinics in Lango sub-region in 2021 [ 19 ]. A recent study in Oyam district reported a high percentage of unintended pregnancies among adolescent girls [ 8 ]. In the context of Lira City in northern Uganda, unintended pregnancies represent a significant challenge affecting various aspects of young people’s lives, including education and economic prospects [ 20 ]. Despite the recognized importance of FP, there is a need for a comprehensive understanding of the factors hindering or facilitating youth access to FP services in Lira City. Existing literature primarily focuses on the prevalence of unintended pregnancies and associated outcomes, emphasizing challenges in accessing reliable information, contraceptives, and quality reproductive health services [ 20 ]. However, there is limited research examining the factors contributing to these challenges, such as cultural norms, stigma, and structural barriers specific to Lira City. Moreover, the evolving circumstances of youth perspectives, preferences, and behaviors related to FP require an updated understanding, considering rapid socio-cultural changes and advancements in technology [ 21 ]. To develop evidence-based intervention strategies, our assessment focused on the knowledge, perceptions, and factors influencing access to contraceptive services among young people in the specific context of northern Uganda.

Study design

This was an explanatory-sequential mixed methods study [ 22 ] conducted in Lira city, northern Uganda between March and April 2023. The mixed-methods approach was adopted so as to generate a more holistic understanding and a stronger inference with two approaches complementing each other [ 23 ].

Study setting

Lira City is among the newly created cities, located approximately 375 km by road north of the capital city of Kampala via Karuma-Kamdini. Lira City is the central business hub for Northern Uganda and comprises the west and east divisions. According to projections by the Uganda Bureau of Statistics (UBOS) in 2014, the population of 2020 for the Lira district was 474,200 people, and it is traditionally inhabited by the Lango tribe, who are farmers and cattle keepers. The urban centers of the district also have people engaged in many small-scale businesses, such as produce businesses and trading.

Study population

The study was among young people aged 15 to 24 years, residing in Lira city. Inclusion into the study was based on being a young person of 15 to 24 years of age who has lived in Lira city for at least six months. Additionally, being present at the selected household during data collection, and those who consented to participate were included in the study. In households where more than one persons were eligible, simple random sampling by lottery method was employed to select one. Exclusion was based on being critically ill to participate, or refusing to participate in the interviews.

Sample size determination

The sample size of the study was estimated using Kish Leslie (1965) as follows:

In the equation above, n is the sample size for the study, Z is the Z score at the 95% confidence interval (1.96), p is the proportion of perceived access to FP services (50%), d is the desired precision of the study (5%), and deff is the design effect due to multistage random sampling. A factor of 1.5 has been used to adjust the sample size based on what previous studies have used [ 24 , 25 ]. A design effect of 1.5 was employed to increase the homogeneity of the participants following the use of a multistage random sampling procedure. Therefore, the final sample size obtained was 577.

Interpretative phenomenological analysis (IPA) was employed for qualitative research to delve into individual experiences, progressing towards an examination of shared and contrasting aspects within a limited sample [ 26 ]. This approach facilitated the identification of thematic connections. Adhering to IPA guidelines advocating for a compact and homogenous sample, purposive sampling was used to recruit 5 participants. This sample size aligns with the recommended number for an IPA study [ 27 ], and was considered sufficient to capture a distinct range of experiences related to the phenomenon under investigation.

Sampling technique

A multistage sampling procedure was employed to select the 577 study participants. The study was conducted in both divisions of Lira City, East and West. From each of the divisions, five wards were selected, making a total of ten wards. This was done by simple random sampling using the lottery method, where the names of wards were written on small papers, folded, mixed in a container, and shaken well, and then five were picked at random without replacement. From each of the wards, two cells were selected using the same procedure, which generated a total of 20 cells. From each of the cells, Village Health Teams (VHTs) were used to obtain lists of households with young people aged 15 to 24 years, and these were used as sampling frames per cell. The number of participants to be selected from each cell was determined by the sample size proportionate to the cell size. In each of the cells, participants were selected through simple random sampling using computer-generated random numbers. Purposive sampling was used to select participants for qualitative interviews [ 28 ]. While purposive sampling guided our selection process, we also sought to include a diverse range of perspectives by engaging with individuals from various backgrounds, including community health workers, educators, and youth leaders. Our rationale for selecting community peer educators stems from their unique position as trusted intermediaries within their communities, often serving as frontline advocates for reproductive health education and services. Similarly, the inclusion of university leaders was motivated by their influence and role in shaping policies and programs related to youth reproductive health within academic settings.

Study variables

Dependent variable.

The dependent variable for the study is perceived access to FP services. Access to healthcare means “the timely use of personal health services to achieve the best health outcomes” [ 29 ]. Many frameworks have been proposed to measure access to family planning services but have all proved not sufficient [ 30 ]. This study adopted one of the common frameworks, Penchansky and Thomas (1981) framework that reflects the fit between characteristics and expectations of the providers and the clients. These characteristics (5As of access) are availability, accessibility, acceptability, accommodation, and affordability [ 31 ]. This conceptualization of access has been adopted because it describes the broad dimensions and determinants that integrate demand and supply-side factors [ 32 ]. According to the model, the five As of access form a chain that is no stronger than its weakest link. For example, improving affordability by providing health insurance will not significantly improve access and utilization if the other four dimensions have not also been addressed. The perception of access to FP services index composed of five questions of yes or no response. For all the questions “yes” was coded 2 and “no” coded 1. The percentage of respondents that perceived access to be good on all five variables had good perceived access to FP services.

Availability: Are the family planning commodities available when you need them, and meet your FP needs?

Accessibility: Is the location of the facilities that provide family planning services convenient for you?

Acceptability: Are the characteristics of the FP service providers (including attitudes and attributes such as age, sex and religion) comfortable for you?

Accommodation: Do health providers organize FP services in ways (including appointment system, hours of operation and facility environment) that suit your needs and preferences?

Affordability: Do you have to pay for family planning services?

All the access questions were asked as yes and no questions and coded 1 and 2, respectively. To measure the index of perceived access, only participants who answered Yes to all the access questions were labeled as having good perceived access to FP services.

Independent variables

The independent variables included sociodemographic characteristics (age, sex, education, religion, marital status, living with parents), sexual-related characteristics (having a child, sexually active, sexual partners), knowledge, and attitudes. The knowledge of the participants was assessed based on a total of nine questions about family planning. Each of the questions was binary coded as 1(Yes) and 0(No). Overall knowledge was therefore measured as a composite score ranging from 0 to 9. The mean score was taken as a cut-off with individuals above the mean score categorized as having good knowledge and those below the mean as having poor knowledge. This measurement was adopted from a recent study [ 33 ]. The overall attitudes of the young people regarding actual use of family planning commodities, which includes the misconceptions, fears, cultural and religious beliefs about family planning commodities such as condoms were assessed based on a total of eight questions with a favorable response coded as Yes (0) and unfavorable response coded as No [ 1 ]. The responses were computed into an overall attitude to FP services score with a total of eight. Similarly, the cut-off was set as the mean with individuals above the mean classified as having a poor perception and those below the mean with a good perception, as from a recent related study [ 33 ]. The knowledge items had a scale reliability coefficient of 0.78 whereas the perception items had 0.70, all these are within the acceptable limits [ 34 ].

Participant recruitment and informed consent processes

After obtaining ethical approval and clearance, five research assistants from the city were recruited and trained on the study protocol and data collection procedures. A pretest of the questionnaire was carried out among 58 youths from Lira district to refine the questions for simplicity and comprehension and to assess validity and reliability using the Statistical Package for Social Sciences (SPSS) software. Lists of households with young people aged 15 to 24 years were obtained by Village Health Teams (VHTs). Sampling was then conducted, and eligible participants were approached for data collection after providing informed consent and, for minors, informed assent. During this process, the study objectives, procedures, benefits, risks, and voluntarism were explained. Interviews took place in a private space within the participants’ homes. In cases where the parent or guardian was absent during data collection, the household was skipped.

Data collection instruments

Quantitative data was collected using a pretested interviewer-administered questionnaire developed by the researcher (Supplementary file 1 ). The questionnaire consisted of four sections: sociodemographic characteristics (age, sex, education, religion, marital status, residence, and parent’s education), sexually related information (ever had a child, engaged in sexual relationships, number of sexual partners, sexual risks encountered), access questions (availability, accessibility, acceptability, accommodation, and affordability), knowledge of family planning services, and attitudes regarding family planning services. This was administered in approximately 15 min. Qualitative data was collected through in-depth interviews and focus group discussions using guides (Supplementary file 2 ). This was done after obtaining insights from quantitative data. Interviews with participants were done at proposed times and places deemed convenient to the participants themselves. During collection, audio recordings were made together with extended field notes to complement the audios. Data collection was done in Lango, verbatim transcribed, and then translated to English for analysis. Data collection was conducted through five in-depth interviews and four focus group discussions all from young people aged 15 to 24 years. A sample of 10 were from the University and 30 were from the community with equal proportions of males and females. These participants were community adolescent peer-educators and University reproductive health leaders. Some were picked after quantitative interviews while others based on their roles regarding reproductive health for the young people.

Statistical analysis

Quantitative data analysis.

The collected data was entered into SPSS software, where it was cleaned and coded, then exported to STATA version 17 software for final analysis. The analysis was conducted at three levels. At the univariate level, data was summarized as frequencies and proportions, means and standard deviations, or median with interquartile range, and presented in frequency tables. In bivariate analysis, perception of access to SRHR services was cross-tabulated with the independent variables one at a time to assess relationships. A crude odd ratio (COR) and a 95% confidence interval were reported. At this level, associations were considered at p  < 0.25 in order to consider all possible predictors [ 35 ], and all those associated factors were taken into multivariate analysis. In multivariate analysis, binary logistic regression was used to estimate the predictors of the primary outcome. The backward elimination method was used to build a predictive model. Results were reported as adjusted odds ratios and 95% confidence intervals. A p -value of < 0.05 was considered statistically significant for variables.

Qualitative data analysis

The data analysis adhered to the seven-stage IPA process outline, derived from Smith and colleagues, as outlined by Brown and colleagues [ 36 ]. Each interview underwent verbatim transcription and was entered into a customized IPA analysis framework. Multiple re-readings of the interviews were conducted, applying in-method triangulation by integrating field notes with observations and commentary from the fieldwork [ 37 ]. This triangulation process enhanced confidence in the outcomes post data analysis. Following the verbatim transcription of the audio data and thorough review of the text, initial notes were made, leading to the development of emerging themes. Connections across these emergent themes were sought to identify subordinate themes. Subsequently, a search for patterns across the cases was conducted to reveal the major themes.

We employed research assistants who are social scientists trained in qualitative study and interview techniques to assure the validity of our study. Data from diverse sources, including field notes and audio recordings, were independently analyzed by two researchers. The newly emerging themes were routinely compared to the original transcribed text, and the writers frequently convened for debriefings to make sure that the subjects were at the center of the data analysis and interpretation. The results of the data analysis were examined and discussed until a consensus was achieved in order to increase the dependability and accuracy of the results. To demonstrate confirmability (the degree to which the findings are shaped by participants and the context rather than the perspectives of the research), the researchers used participants’ narratives and words as noted in the transcripts. Additionally, the researchers dwelled on their previous experiences to reduce their influence on the findings. To ensure that the processes of data collecting and analysis could be traced back to the initial interviews, we have preserved all audit trails from data collection to analysis.

Quantitative findings

Sociodemographic characteristics.

Recruitment into the study was between March and April 2022. Out of a total of 577 participants, 553 were included generating a response rate of 95.8%. Table  1 shows that the majority of the respondents, 65.3% were female, with a mean age of 17 (± 2.1) years and 90.8% aged between 15 and 19 years. Most of the youth, 45.2% were in secondary school, 40.7% were Anglican, and 71.4% were living with their parents. The majority of the youths, 46.8% were sexually active and had had sex in the past 4 months.

Perceived access to family planning services

The mean score for perception of access to family planning services was 1.91 with a standard deviation of ± 0.29. Figure  1 shows that the percentage of respondents that perceived access to be good for all the five variables was 31.7% (95% CI: 28%, 36%). The majority of the young people, 64.6% reported that different FP methods were available at the health facilities. Most of the young people, 79.3%% also reported that the health facilities were within their reach, and 61.3% reported that attitudes and personal characteristics of FP service providers were comfortable for them. The majority of the young people, 66.7% also reported that the manner in which FP services are organized, including facility’s operating and environment, suited their needs and preferences. Additionally, females had overall favorable responses compared to their male counterparts.

Percentage of young people reporting a good response to variables on the perceived access index in Lira district, Northern Uganda

Knowledge and attitudes regarding perceived access to family planning services

Table  2 presents questions used to assess both knowledge and perceptions regarding family planning services. questions 1 to 9 were designed to measure knowledge and 10 to 17 were aimed at capturing perceptions regarding use of family planning commodities. The majority of the young people, 69.4% were aware that FP, 75.6% knew the facility that offers FP services, 89.3% knew how to prevent pregnancy and 75.8% knew about sexual rights. Table  3 also shows that the majority of the young people, 58.1% perceived that FP services were not for young people, 80.5% could access FP whenever they wanted and 90.4% knew that information at the health facility was always kept confidential. However, the majority of the young people, 66.2% cannot decide on using a FP method, 57.7% also reported that there is not enough privacy at the health facilities, and 77.2% fear being mistreated by the staff at the health facilities.

Factors associated with perceived access to family planning services among young people

The bivariate analysis was performed stratified by sex to prevent introduction of bias arising from differencing sample sizes because males were close to a third of the entire sample. Table  3 indicates that among the females, being aged 20–24 years, having a child, being sexually active and having a sexual partner was associated with a higher perceived access to FP services at p  value less than 0.25. On the other hand, having primary and secondary education, urban residence, Christians, Muslims, not in a marital relationship, secondary education of a mother, primary, secondary and tertiary education of father, and good attitude towards FP services were associated with a lower perceived access to FP services at p  value less than 0.25. Table  3 also shows that among males, living with parents, mother’s secondary education level, and good attitude towards FP services had a lower perceived access to FP, where as being sexually active and good overall knowledge of FP services had higher perceived access to FP services with p  value of less than 0.25.

Multivariate analysis was performed to assess the predictors of perceived access to family planning services for males and females, presented as two separate models for females and males. Table  4 show among females, residence, religion, sexual partners and perception regarding use of family planning methods had significant associations. Females respondents who were less likely to consider access to family planning services as good were urban residents (AOR: 0.22, 95% CI: 0.09–0.53, p  = 0.001), those who were Christian (AOR: 0.51, 95% CI: 0.01–0.36, p  = 0.003) and Muslim (AOR: 0.07, 95% CI: 0.01–0.55, p  = 0.012), and those who had a poor attitude towards family planning services (AOR: 0.39, 95% CI: 0.24–0.64, p  < 0.001). Female respondents who had a sexual partner were more likely to consider access to family planning services as good (AOR: 4.48, 95% CI: 2.60–7.75, p  < 0.001). Table  4 also shows that among males, living with parents and overall knowledge about family planning services were significantly associated with perceive access to family planning services. Male respondents that were less likely to consider access to family planning as good were those who lived with their parents (AOR: 0.19, 95% CI: 0.05–0.67, p  = 0.010), and those that were more likely to consider access to family planning as good were those that had good overall knowledge about family planning methods (AOR: 2.28, 95% CI: 1.02–5.32, p  = 0.050).

Qualitative findings

Characteristics of participants.

A total of 5 in-depth interviews and 4 FGDs were conducted with a total of 30 young people; 10 were university students, whereas 20 were from the community. The focus groups were homogeneous in nature, for males and females separated. Two focus groups of 10 participants for males and females were conducted in the community, and two groups of five males and females were conducted from the University youths. The participants were young people aged 15 to 24 years. Themes were obtained through finding similar texts, patterns, and insights. We generated 8 different codes, 7 subthemes, and 3 themes.

Theme 1: knowledge of family planning methods

Majority of the young people did not have adequate knowledge regarding access and use of FP services. This was evidenced as most participants from the community reported that many young people used off label benefits of paracetamol and traditional herbal medicines for contraception. Additionally, many reported their source of information to be friends who seemed not to have adequate knowledge as well. Here are some of the verbatim comments to support the results:

“After having sex today, you can take 4 Panadol tablets immediately after having sex or 6 tablets, though it depends, you can also take it a day after having the sexual intercourse, taking on the third day will be late for it to work well in preventing the pregnancy”. (Female, 22 years, Lira Town, Feb 2023) “When I was in Primary six class. I was living with my sister and she had maids who told me about Panadol use”. (Female, 21 years, Junior quarters, Feb 2023) “Some girls use paw paw leaves, others mixing diclofenac drug with herbs which can also cause abortion. But these procedures can also either lead to incomplete abortion, death or even over bleeding” (Male, 24 years, Barapwo, Feb 2023) . “There is no proper sexual information. In the past, parents called children to prepare for education but today nowhere it’s practiced. Now it is only in schools to ensure that people know that sex is good but has challenges” (Female, 23 years, LU, Feb 2023) .

Theme 2: beliefs about youth contraception

The majority of the participants also reported negative perceptions regarding family planning services. However, this appears to stem from the common narrative that frames sexual health for young people as taboo. To continue, many young people and the community reported distancing themselves from reproductive health programs, citing that their motives are not entirely transparent. Here are some of the quotes that were recorded to emphasize the narrative:

“I see no meaning in engaging in such because they are just avenues for disseminating homosexuality and encouraging the youths to abort. They come in the sense of advocating for rights but instead teach that abortion and homosexuality is okay and a human right” (Female, 24 years, LU, Feb 2023) . “Family planning services are for big people. But there is need for a comprehensive guidance in matters of Sexual health for adolescents and adults about hygiene and opposite sex interaction” (Male, 16 years, Lira town, Feb 2023) .

Theme 3: friendliness of family planning services

Most of the participants reported that reproductive health services for young people are not friendly. The services are provided in environments that do not guarantee privacy and confidentiality, as well as during inflexible hours. To emphasize the narrative, here are a few verbatim comments:

It’s very difficult to go and access family planning services like pills from the teaching hospital…, can you imagine being served by your own lecturer who discourages having sex before marriage……Hmmm it’s funny! (Female, 24 years, LU, Feb 2023) A friend can help buy contraceptives if the user is known to the health worker who is selling. The seller might inform the buyer’s parents when one goes to buy condoms. (Male, 18 years, Amuca, Feb 2023)

The study aimed to assess perceived access to FP services and associated factors among young people in Lira City, Northern Uganda. Though many models have been suggested to measure access, they have all showed deficiencies in measuring actual access to family planning methods [ 30 ]. This study adopted the Penchansky and Thomas (1981) framework that measures perception of access through a 5-item index to explore the level of perceived access in this study. Findings of the current study showed that good perceived access to FP services was among 31.7% of respondents, with 64.6% reporting availability, 76.5% accessibility, 61.3% acceptability, 66.7% accommodative and 87.9% affordability of FP methods at health facilities. Our study indicates a low perceived access to FP services. Among the various components, availability, acceptability and accommodation pose significant obstacles to contraceptive access. A similar study in South Africa also reported the accommodation component as the greatest obstacle for accessing FP services due to integrated care, long waiting hours, and limited operational hours [ 38 ]. Additionally, the study reported that community were less concerned about the availability of trained service providers and a variety of contraceptive methods [ 38 ]. These possibly explain the low perceived access in the current study. In line with the current study, a recent study on utilization of sexual and reproductive services including family planning among young people in Lira city also reported a low level of 42% [ 39 ].

The overall perceived access to FP services at 31.7% suggests a substantial gap in service availability, indicating the need for targeted interventions to enhance accessibility. The presence of different FP methods at health facilities (64.6%) is a positive aspect, but the study unveils underlying challenges that contribute to the overall low perceived access. One of the key positive findings is the proximity of health facilities for 79.3% of participants, emphasizing the importance of physical accessibility. Additionally, positive perceptions towards use of family planning commodities, such as acceptability of FP use by the young people (61.3%) and a conducive environment at health facilities (66.7%), indicate a foundation upon which interventions can build. However, challenges identified, particularly for females, including a lack of privacy (57.7%), fear of mistreatment by staff (77.2%), and difficulties in decision-making regarding FP use (66.2%), highlight the nature of barriers to access. These challenges align with existing literature on the importance of privacy [ 40 ], quality of service [ 41 ], and decision-making autonomy in shaping individuals’ willingness to utilize FP services [ 42 ].

Quantitative findings revealed significant associations between perceived access to FP services and various sociodemographic factors, emphasizing the complexity of the issue. For females, urban residence, religion, having sexual partners, and perception were identified as influencing factors, while for males, living with parents and overall knowledge played a significant role. These associations underline the necessity for tailored interventions that consider the specific challenges faced by each gender. Qualitative findings highlighted insufficient knowledge, negative perceptions, and unfriendly FP services. These findings provide a deeper understanding of the barriers, emphasizing inadequate knowledge of FP methods, negative cultural and societal perceptions about youth contraception, and unfriendly service environments. These findings are consistent with existing literature, highlighting the role of cultural perceptions, knowledge gaps, and service quality in shaping young people’s access to FP services [ 43 ]. In agreement with previous studies, the study underscores the importance of comprehensive sexual education programs and youth-friendly service initiatives [ 44 ]. Our study shows a notable link between Islam and Catholicism and perceived access to FP services, aligning with previous research on religious influences that notes that the use of contraception is not promoted by any of the two religions [ 45 ]. Further exploration and comparative analysis with other studies may help elucidate these discrepancies and provide a more nuanced understanding of the factors influencing access to FP services among young people in Northern Uganda.

Strength and limitations

The study benefits from a mixed-methods approach, which integrates both qualitative and quantitative data to offer a comprehensive understanding of the factors influencing young people’s perceived access to family planning services. However, the cross-sectional design presents a limitation as it hinders the establishment of causality, providing only a snapshot of the situation at a specific moment and limiting exploration of temporal relationships over time. Acknowledging the small sample size and the potential bias introduced by selecting individuals with extensive knowledge on the topic, we recognize the limitation on the generalizability of our findings only to Lira City. The selection of individuals for IDIs may have inadvertently limited the diversity of perspectives represented in our study. Furthermore, participants may exhibit social desirability bias, particularly in studies addressing sensitive topics like sexual and reproductive health. Recall bias among participants, particularly when recalling past experiences related to sensitive topics or events that occurred some time ago, is also a possibility. Lastly, the quantitative sample was skewed towards females and those aged 15–19 years, potentially affecting the representativeness of the findings.

Our study reveals a substantial gap in perceived access to family planning services among young people. Despite high awareness, barriers like privacy concerns and fear of mistreatment contribute to low access. Tailored interventions are needed, focusing on urban service access, religious beliefs for females, and knowledge enhancement for males. Positive aspects, such as diverse FP methods and physical accessibility, form a foundation for interventions. The study emphasizes the importance of youth-friendly services, comprehensive sexual education, and further research for a nuanced understanding and targeted interventions in Northern Uganda.

Data availability

The data for the study is not publicly available due to restrictions from the Research Ethics Committee (REC) for posting of public data. However, can be accessed from the principal investigator on a reasonable request ([email protected]).

Abbreviations

Sexual Reproductive Health and Rights

Sub-Saharan Africa

Village Health Team

World Health Organization

Stanburry POHCHR. 2021 [cited 2022 May 3]. Young people need their sexual and reproductive health and rights. https://www.ohchr.org/en/stories/2021/05/young-people-need-their-sexual-and-reproductive-health-and-rights

UNICEF. Adolescent development [Internet]. 2022 [cited 2022 May 3]. https://www.unicef.org/uganda/what-we-do/adolescent-development

Bongaarts J, Hardee K. The role of public-sector family planning programs in meeting the demand for contraception in Sub-saharan Africa. Int Perspect Sex Reprod Health. 2017;43(2):41–50.

Article   PubMed   Google Scholar  

Organization WH. A vision for primary health care in the 21st century: towards universal health coverage and the Sustainable Development Goals. World Health Organization; 2018.

09_2021Uganda-National-Survey-Report-2019-2020.pdf [Internet]. [cited 2023 Sep 9]. https://www.ubos.org/wp-content/uploads/publications/09_2021Uganda-National-Survey-Report-2019-2020.pdf

Nabwowe Kasule A, Mncwabe N. Reclaiming public health services in Uganda. 2021.

Boydell V, Neema S, Wright K, Hardee K. Closing the gap between people and programs: lessons from implementation of social accountability for family planning and reproductive health in Uganda. Afr J Reprod Health. 2018;22(1):73–84.

PubMed   Google Scholar  

Okalo P, Arach AA, Apili B, Oyat J, Halima N, Kabunga A. Predictors of Unintended Pregnancy Among Adolescent Girls During the Second Wave of COVID-19 Pandemic in Oyam District in Northern Uganda. Open Access J Contracept [Internet]. 2023 Dec 31 [cited 2023 Mar 27];14:15–21. https://www.tandfonline.com/doi/abs/ https://doi.org/10.2147/OAJC.S399973

Musinguzi M, Kumakech E, Auma AG, Akello RA, Kigongo E, Tumwesigye R et al. Prevalence and correlates of teenage pregnancy among in-school teenagers during the COVID-19 pandemic in Hoima district western Uganda–A cross sectional community-based study. PLOS ONE [Internet]. 2022 Dec 16 [cited 2023 Mar 27];17(12):e0278772. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0278772

Ochen AM, Chi PC, Lawoko S. Predictors of teenage pregnancy among girls aged 13–19 years in Uganda: a community based case-control study. BMC Pregnancy Childbirth [Internet]. 2019 Jun 24 [cited 2023 Mar 28];19(1):211. https://doi.org/10.1186/s12884-019-2347-y

Yaya S, Zegeye B, Ahinkorah BO, Oladimeji KE, Shibre G. Inequality in fertility rate among adolescents: evidence from Timor-Leste demographic and health surveys 2009–2016. Arch Public Health [Internet]. 2020 Oct 14 [cited 2023 Mar 27];78:98. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7557032/

Leerlooijer JN, Bos AE, Ruiter RA, van Reeuwijk MA, Rijsdijk LE, Nshakira N et al. Qualitative evaluation of the Teenage Mothers Project in Uganda: a community-based empowerment intervention for unmarried teenage mothers. BMC Public Health [Internet]. 2013 Sep 8 [cited 2023 Mar 27];13(1):816. https://doi.org/10.1186/1471-2458-13-816

MoES. Fact Booklet [Internet]. Ministry of Education And Sports. 2012 [cited 2023 Mar 27]. https://www.education.go.ug/fact-booklet/

World Health Organization. Adolescent pregnancy [Internet]. [cited 2023 Dec 18]. https://www.who.int/news-room/fact-sheets/detail/adolescent-pregnancy

World Bank. World Bank Gender Data Portal. 2022 [cited 2023 Dec 18]. The Social and Educational Consequences of Adolescent Childbearing. https://genderdata.worldbank.org/data-stories/adolescent-fertility/

The National. Adolescent Health-Strategy.

MoH. Uganda [Internet]. 2022 [cited 2023 May 8]. https://fp2030.org/uganda

Bhurtyal A. Policy brief Policy brief.

CDFU. Monitor. 2021 [cited 2023 Mar 28]. 23,000 girls impregnated in Lango in one year - NGO. https://www.monitor.co.ug/uganda/news/national/23-000-girls-impregnated-in-lango-in-one-year-ngo-3573252

Napyo A, Nankabirwa V, Mukunya D, Tumuhamye J, Ndeezi G, Arach AAO, et al. Prevalence and predictors for unintended pregnancy among HIV-infected pregnant women in Lira, Northern Uganda: a cross-sectional study. Sci Rep. 2020;10(1):1–8.

Article   Google Scholar  

Abdel-Tawab NG, Attia S, Bader N, Roushdy R, El-Nakib S, Oraby D. Fertility preferences and behaviors among younger cohorts in Egypt: Recent trends, correlates, and prospects for change. 2020.

Draucker CB, Rawl SM, Vode E, Carter-Harris L. Integration through connecting in explanatory sequential mixed method studies. West J Nurs Res. 2020;42(12):1137–47.

Article   PubMed   PubMed Central   Google Scholar  

Regnault A, Willgoss T, Barbic S, On behalf of the International Society for Quality of Life Research (ISOQOL) Mixed Methods Special Interest Group (SIG). Towards the use of mixed methods inquiry as best practice in health outcomes research. J Patient-Rep Outcomes [Internet]. 2018 Apr 11 [cited 2023 Mar 28];2(1):19. https://doi.org/10.1186/s41687-018-0043-8

Simegn A, Azale T, Addis A, Dile M, Ayalew Y, Minuye B. Youth friendly sexual and reproductive health service utilization among high and preparatory school students in Debre Tabor town, Northwest Ethiopia: a cross sectional study. PLoS ONE. 2020;15(9):e0240033.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Rade BK, Tamiru AT, Aynalem GL, Taye EB, Melkie M, Abera A, et al. Prevalence and factors associated with sexual and reproductive health services use among reproductive age women with disabilities: a community based cross-sectional study. BMC Womens Health. 2023;23(1):215.

Smith JA, Fieldsend M. Interpretative phenomenological analysis. American Psychological Association; 2021.

Eatough V, Smith JA. Interpretative phenomenological analysis. Sage Handb Qual Res Psychol. 2017;193–209.

Campbell S, Greenwood M, Prior S, Shearer T, Walkem K, Young S, et al. Purposive sampling: complex or simple? Research case examples. J Res Nurs JRN. 2020;25(8):652–61.

Institute of Medicine (US) Committee on Monitoring Access to Personal Health Care Services. Access to Health Care in America [Internet]. Millman M, editor. Washington (DC): National Academies Press (US). 1993 [cited 2023 Dec 18]. http://www.ncbi.nlm.nih.gov/books/NBK235882/

Choi Y, Fabic MS, Adetunji J. Measuring Access to Family Planning: conceptual frameworks and DHS Data. Stud Fam Plann. 2016;47(2):145–61.

Article   CAS   PubMed   Google Scholar  

Penchansky R, Thomas JW. The concept of access: definition and relationship to consumer satisfaction. Med Care. 1981;19(2):127–40.

Levesque JF, Harris MF, Russell G. Patient-centred access to health care: conceptualising access at the interface of health systems and populations. Int J Equity Health. 2013;12(1):18.

Bekele D, Surur F, Nigatu B, Teklu A, Getinet T, Kassa M, et al. Knowledge and attitude towards Family Planning among women of Reproductive Age in emerging regions of Ethiopia. J Multidiscip Healthc. 2020;13:1463–74.

Taber KS. The Use of Cronbach’s alpha when developing and Reporting Research Instruments in Science Education. Res Sci Educ. 2018;48(6):1273–96.

Collett D. Modelling Binary Data. 2nd ed. New York: Chapman and Hall/CRC; 2014. p. 408.

Google Scholar  

Brown HM, Stahmer AC, Dwyer P, Rivera S. Changing the story: How diagnosticians can support a neurodiversity perspective from the start. Vol. 25, Autism. SAGE Publications Sage UK: London, England; 2021. pp. 1171–4.

Flick U. Triangulation in data collection. SAGE Handb Qual Data Collect. 2018;527–44.

Kriel Y, Milford C, Cordero JP, Suleman F, Steyn PS, Smit JA. Access to public sector family planning services and modern contraceptive methods in South Africa: a qualitative evaluation from community and health care provider perspectives. PLoS ONE. 2023;18(3):e0282996.

Murungi T, Benyumiza D, Apio J, Nekesa C, Nalubuuka A, Misuk I, et al. Factors Associated with Utilization of Sexual and Reproductive Health Services among the Youth in Lira City West, Northern Uganda: a cross-sectional study. BioMed Res Int. 2023;2023:e9649792.

Brittain AW, Williams JR, Zapata LB, Pazol K, Romero LM, Weik TS. Youth-Friendly Family Planning services for Young people. Am J Prev Med. 2015;49(2 Suppl 1):S73–84.

Abera L, Ejigu E, Hailu M, Tadesse D, Omer A. Quality of family planning services and associated factors among reproductive age women attending family planning unit at public health facilities in dire Dawa, Eastern Ethiopia, 2021. Contracept Reprod Med. 2023;8(1):33.

Osamor PE, Grady C. Women’s autonomy in health care decision-making in developing countries: a synthesis of the literature. Int J Womens Health. 2016;8:191–202.

Namasivayam A, Schluter PJ, Namutamba S, Lovell S. Understanding the contextual and cultural influences on women’s modern contraceptive use in East Uganda: a qualitative study. PLOS Glob Public Health. 2022;2(8):e0000545.

Asingwire N, Muhangi D, Kyomuhendo S, Leight J. Impact evaluation of youth-friendly family planning services in Uganda. 2019.

Schenker JG, Rabenou V. Family planning: cultural and religious perspectives. Hum Reprod. 1993;8(6):969–76.

Download references

Acknowledgements

Pre-Publication Support Service (PREPSS) supported the development of this manuscript by providing author training, as well as pre-publication peer-review and copy editing.

The authors want to acknowledge all the young people who took part in the study. In a special way, we also want to thank Dr. Marc Sam Opollo of Faculty of Public Health Lira University for reviewing and guiding our results presentation for the annual sexual and reproductive symposium presentation in 2023.

This research work was supported by a seed grant from the Center for International Reproductive Health Training at the University of Michigan (CIRHT-UM). The content is solely the responsibility of the authors and does not necessarily represent the official views of CIRHT-UM. The funders had no role in study design, data collection and analysis, the decision to publish, or the preparation of the manuscript.

Author information

Authors and affiliations.

Faculty of Public Health, Lira University, Lira City, Northern, P. O Box 1035, Uganda

Eustes Kigongo, Raymond Tumwesigye, Maxson Kenneth Anyolitho, Marvin Musinguzi, Everlyne Achan & Bernard Omech

Faculty of Nursing , Mbarara University of Science and Technology, Mbarara, Uganda

Caroline Kambugu Nabasirye & Samson Udho

Faculty of Education, Lira University, Lira, Uganda

Gad Kwizera

Faculty of Medicine, Lira University, Lira, Uganda

Amir Kabunga

You can also search for this author in PubMed   Google Scholar

Contributions

All authors made significant contributions to the conceptualization, design, data collection, curation, manuscript writing, and editing. EK, MKA and RT conceptualized and designed the study. EA, MM, GK, CKN, and SU designed the data collection tools and conducted the study. AK and BO gave overall guidance for the study. All the authors gave final approval to the manuscript for journal submission and are responsible for the content of the manuscript.

Corresponding author

Correspondence to Eustes Kigongo .

Ethics declarations

Ethical approval and consent to participate.

Informed consent was obtained from all the participants and their legal guardians. All ethical approvals were obtained from the Gulu University Research Ethics Committee.

Consent for publication

Not applicable.

Competing interests

Authors declare no conflict of interest.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Supplementary material 2, rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Cite this article.

Kigongo, E., Tumwesigye, R., Anyolitho, M.K. et al. Access to family planning services and associated factors among young people in Lira city northern Uganda. BMC Public Health 24 , 1146 (2024). https://doi.org/10.1186/s12889-024-18605-8

Download citation

Received : 11 September 2023

Accepted : 15 April 2024

Published : 24 April 2024

DOI : https://doi.org/10.1186/s12889-024-18605-8

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Contraception
• Family planning
• Young people

BMC Public Health

ISSN: 1471-2458

• Submission enquiries: [email protected]
• General enquiries: [email protected]