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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.

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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 ...

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84 Comments

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

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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.

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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

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

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i need a citation of your book.

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

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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 ?

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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

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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

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Clear explanation on qualitative and how about Case study

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

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

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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

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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 .

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Data Analysis for Qualitative Research: 6 Step Guide

Data analysis for qualitative research is not intuitive. This is because qualitative data stands in opposition to traditional data analysis methodologies: while data analysis is concerned with quantities, qualitative data is by definition unquantified . But there is an easy, methodical approach that anyone can take use to get reliable results when performing data analysis for qualitative research. The process consists of 6 steps that I’ll break down in this article:

• Perform interviews(if necessary )
• Gather all documents and transcribe any non-paper records
• Decide whether to either code analytical data, analyze word frequencies, or both
• Decide what interpretive angle you want to take: content analysis , narrative analysis, discourse analysis, framework analysis, and/or grounded theory
• Compile your data in a spreadsheet using document saving techniques (windows and mac)
• Identify trends in words, themes, metaphors, natural patterns, and more

To complete these steps, you will need:

• Microsoft word
• Microsoft excel
• Internet access

You can get the free Intro to Data Analysis eBook to cover the fundamentals and ensure strong progression in all your data endeavors.

What is qualitative research?

Qualitative research is not the same as quantitative research. In short, qualitative research is the interpretation of non-numeric data. It usually aims at drawing conclusions that explain why a phenomenon occurs, rather than that one does occur. Here’s a great quote from a nursing magazine about quantitative vs qualitative research:

“A traditional quantitative study… uses a predetermined (and auditable) set of steps to confirm or refute [a] hypothesis. “In contrast, qualitative research often takes the position that an interpretive understanding is only possible by way of uncovering or deconstructing the meanings of a phenomenon. Thus, a distinction between explaining how something operates (explanation) and why it operates in the manner that it does (interpretation) may be [an] effective way to distinguish quantitative from qualitative analytic processes involved in any particular study.” (bold added) (( EBN ))

Learn to Interpret Your Qualitative Data

This article explain what data analysis is and how to do it. To learn how to interpret the results, visualize, and write an insightful report, sign up for our handbook below.

Step 1a: Data collection methods and techniques in qualitative research: interviews and focus groups

Step 1 is collecting the data that you will need for the analysis. If you are not performing any interviews or focus groups to gather data, then you can skip this step. It’s for people who need to go into the field and collect raw information as part of their qualitative analysis.

Since the whole point of an interview and of qualitative analysis in general is to understand a research question better, you should start by making sure you have a specific, refined research question . Whether you’re a researcher by trade or a data analyst working on one-time project, you must know specifically what you want to understand in order to get results.

Good research questions are specific enough to guide action but open enough to leave room for insight and growth. Examples of good research questions include:

• Good : To what degree does living in a city impact the quality of a person’s life? (open-ended, complex)
• Bad : Does living in a city impact the quality of a person’s life? (closed, simple)

Once you understand the research question, you need to develop a list of interview questions. These questions should likewise be open-ended and provide liberty of expression to the responder. They should support the research question in an active way without prejudicing the response. Examples of good interview questions include:

• Good : Tell me what it’s like to live in a city versus in the country. (open, not leading)
• Bad : Don’t you prefer the city to the country because there are more people? (closed, leading)

Some additional helpful tips include:

• Begin each interview with a neutral question to get the person relaxed
• Limit each question to a single idea
• If you don’t understand, ask for clarity
• Do not pass any judgements
• Do not spend more than 15m on an interview, lest the quality of responses drop

Focus groups

The alternative to interviews is focus groups. Focus groups are a great way for you to get an idea for how people communicate their opinions in a group setting, rather than a one-on-one setting as in interviews.

In short, focus groups are gatherings of small groups of people from representative backgrounds who receive instruction, or “facilitation,” from a focus group leader. Typically, the leader will ask questions to stimulate conversation, reformulate questions to bring the discussion back to focus, and prevent the discussion from turning sour or giving way to bad faith.

Focus group questions should be open-ended like their interview neighbors, and they should stimulate some degree of disagreement. Disagreement often leads to valuable information about differing opinions, as people tend to say what they mean if contradicted.

However, focus group leaders must be careful not to let disagreements escalate, as anger can make people lie to be hurtful or simply to win an argument. And lies are not helpful in data analysis for qualitative research.

Step 1b: Tools for qualitative data collection

When it comes to data analysis for qualitative analysis, the tools you use to collect data should align to some degree with the tools you will use to analyze the data.

As mentioned in the intro, you will be focusing on analysis techniques that only require the traditional Microsoft suite programs: Microsoft Excel and Microsoft Word . At the same time, you can source supplementary tools from various websites, like Text Analyzer and WordCounter.

In short, the tools for qualitative data collection that you need are Excel and Word , as well as web-based free tools like Text Analyzer and WordCounter . These online tools are helpful in the quantitative part of your qualitative research.

Step 2: Gather all documents & transcribe non-written docs

Once you have your interviews and/or focus group transcripts, it’s time to decide if you need other documentation. If you do, you’ll need to gather it all into one place first, then develop a strategy for how to transcribe any non-written documents.

When do you need documentation other than interviews and focus groups? Two situations usually call for documentation. First , if you have little funding , then you can’t afford to run expensive interviews and focus groups.

Second , social science researchers typically focus on documents since their research questions are less concerned with subject-oriented data, while hard science and business researchers typically focus on interviews and focus groups because they want to know what people think, and they want to know today.

Non-written records

Other factors at play include the type of research, the field, and specific research goal. For those who need documentation and to describe non-written records, there are some steps to follow:

• Put all hard copy source documents into a sealed binder (I use plastic paper holders with elastic seals ).
• If you are sourcing directly from printed books or journals, then you will need to digitalize them by scanning them and making them text readable by the computer. To do so, turn all PDFs into Word documents using online tools such as PDF to Word Converter . This process is never full-proof, and it may be a source of error in the data collection, but it’s part of the process.
• If you are sourcing online documents, try as often as possible to get computer-readable PDF documents that you can easily copy/paste or convert. Locked PDFs are essentially a lost cause .
• Transcribe any audio files into written documents. There are free online tools available to help with this, such as 360converter . If you run a test through the system, you’ll see that the output is not 100%. The best way to use this tool is as a first draft generator. You can then correct and complete it with old fashioned, direct transcription.

Step 3: Decide on the type of qualitative research

Before step 3 you should have collected your data, transcribed it all into written-word documents, and compiled it in one place. Now comes the interesting part. You need to decide what you want to get out of your research by choosing an analytic angle, or type of qualitative research.

The available types of qualitative research are as follows. Each of them takes a unique angle that you must choose to get what information you want from the analysis . In addition, each of them has a different impact on the data analysis for qualitative research (coding vs word frequency) that we use.

Content analysis

Narrative analysis, discourse analysis.

• Framework analysis, and/or

Grounded theory

From a high level, content, narrative, and discourse analysis are actionable independent tactics, whereas framework analysis and grounded theory are ways of honing and applying the first three.

• Definition : Content analysis is identify and labelling themes of any kind within a text.
• Focus : Identifying any kind of pattern in written text, transcribed audio, or transcribed video. This could be thematic, word repetition, idea repetition. Most often, the patterns we find are idea that make up an argument.
• Goal : To simplify, standardize, and quickly reference ideas from any given text. Content analysis is a way to pull the main ideas from huge documents for comparison. In this way, it’s more a means to an end.
• Pros : The huge advantage of doing content analysis is that you can quickly process huge amounts of texts using simple coding and word frequency techniques we will look at below. To use a metaphore, it is to qualitative analysis documents what Spark notes are to books.
• Cons : The downside to content analysis is that it’s quite general. If you have a very specific, narrative research question, then tracing “any and all ideas” will not be very helpful to you.
• Definition : Narrative analysis is the reformulation and simplification of interview answers or documentation into small narrative components to identify story-like patterns.
• Focus : Understanding the text based on its narrative components as opposed to themes or other qualities.
• Goal : To reference the text from an angle closer to the nature of texts in order to obtain further insights.
• Pros : Narrative analysis is very useful for getting perspective on a topic in which you’re extremely limited. It can be easy to get tunnel vision when you’re digging for themes and ideas from a reason-centric perspective. Turning to a narrative approach will help you stay grounded. More importantly, it helps reveal different kinds of trends.
• Cons : Narrative analysis adds another layer of subjectivity to the instinctive nature of qualitative research. Many see it as too dependent on the researcher to hold any critical value.
• Definition : Discourse analysis is the textual analysis of naturally occurring speech. Any oral expression must be transcribed before undergoing legitimate discourse analysis.
• Focus : Understanding ideas and themes through language communicated orally rather than pre-processed on paper.
• Goal : To obtain insights from an angle outside the traditional content analysis on text.
• Pros : Provides a considerable advantage in some areas of study in order to understand how people communicate an idea, versus the idea itself. For example, discourse analysis is important in political campaigning. People rarely vote for the candidate who most closely corresponds to his/her beliefs, but rather for the person they like the most.
• Cons : As with narrative analysis, discourse analysis is more subjective in nature than content analysis, which focuses on ideas and patterns. Some do not consider it rigorous enough to be considered a legitimate subset of qualitative analysis, but these people are few.

Framework analysis

• Definition : Framework analysis is a kind of qualitative analysis that includes 5 ordered steps: coding, indexing, charting, mapping, and interpreting . In most ways, framework analysis is a synonym for qualitative analysis — the same thing. The significant difference is the importance it places on the perspective used in the analysis.
• Focus : Understanding patterns in themes and ideas.
• Goal : Creating one specific framework for looking at a text.
• Pros : Framework analysis is helpful when the researcher clearly understands what he/she wants from the project, as it’s a limitation approach. Since each of its step has defined parameters, framework analysis is very useful for teamwork.
• Cons : It can lead to tunnel vision.
• Definition : The use of content, narrative, and discourse analysis to examine a single case, in the hopes that discoveries from that case will lead to a foundational theory used to examine other like cases.
• Focus : A vast approach using multiple techniques in order to establish patterns.
• Goal : To develop a foundational theory.
• Pros : When successful, grounded theories can revolutionize entire fields of study.
• Cons : It’s very difficult to establish ground theories, and there’s an enormous amount of risk involved.

Step 4: Coding, word frequency, or both

Coding in data analysis for qualitative research is the process of writing 2-5 word codes that summarize at least 1 paragraphs of text (not writing computer code). This allows researchers to keep track of and analyze those codes. On the other hand, word frequency is the process of counting the presence and orientation of words within a text, which makes it the quantitative element in qualitative data analysis.

Video example of coding for data analysis in qualitative research

In short, coding in the context of data analysis for qualitative research follows 2 steps (video below):

• Reading through the text one time
• Adding 2-5 word summaries each time a significant theme or idea appears

Let’s look at a brief example of how to code for qualitative research in this video:

Click here for a link to the source text. 1

Example of word frequency processing

And word frequency is the process of finding a specific word or identifying the most common words through 3 steps:

• Decide if you want to find 1 word or identify the most common ones
• Use word’s “Replace” function to find a word or phrase
• Use Text Analyzer to find the most common terms

Here’s another look at word frequency processing and how you to do it. Let’s look at the same example above, but from a quantitative perspective.

Imagine we are already familiar with melanoma and KITs , and we want to analyze the text based on these keywords. One thing we can do is look for these words using the Replace function in word

• Locate the search bar
• Click replace
• Type in the word
• See the total results

Here’s a brief video example:

Another option is to use an online Text Analyzer. This methodology won’t help us find a specific word, but it will help us discover the top performing phrases and words. All you need to do it put in a link to a target page or paste a text. I pasted the abstract from our source text, and what turns up is as expected. Here’s a picture:

Step 5: Compile your data in a spreadsheet

After you have some coded data in the word document, you need to get it into excel for analysis. This process requires saving the word doc as an .htm extension, which makes it a website. Once you have the website, it’s as simple as opening that page, scrolling to the bottom, and copying/pasting the comments, or codes, into an excel document.

You will need to wrangle the data slightly in order to make it readable in excel. I’ve made a video to explain this process and places it below.

Step 6: Identify trends & analyze!

There are literally thousands of different ways to analyze qualitative data, and in most situations, the best technique depends on the information you want to get out of the research.

Nevertheless, there are a few go-to techniques. The most important of this is occurrences . In this short video, we finish the example from above by counting the number of times our codes appear. In this way, it’s very similar to word frequency (discussed above).

A few other options include:

• Ranking each code on a set of relevant criteria and clustering
• Pure cluster analysis
• Causal analysis

We cover different types of analysis like this on the website, so be sure to check out other articles on the home page .

How to analyze qualitative data from an interview

To analyze qualitative data from an interview , follow the same 6 steps for quantitative data analysis:

• Perform the interviews
• Transcribe the interviews onto paper
• Decide whether to either code analytical data (open, axial, selective), analyze word frequencies, or both
• Compile your data in a spreadsheet using document saving techniques (for windows and mac)
• Source text [ ↩ ]

About the Author

Noah is the founder & Editor-in-Chief at AnalystAnswers. He is a transatlantic professional and entrepreneur with 5+ years of corporate finance and data analytics experience, as well as 3+ years in consumer financial products and business software. He started AnalystAnswers to provide aspiring professionals with accessible explanations of otherwise dense finance and data concepts. Noah believes everyone can benefit from an analytical mindset in growing digital world. When he's not busy at work, Noah likes to explore new European cities, exercise, and spend time with friends and family.

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• v.68(3); May-Jun 2015

Qualitative Research: Data Collection, Analysis, and Management

Introduction.

In an earlier paper, 1 we presented an introduction to using qualitative research methods in pharmacy practice. In this article, we review some principles of the collection, analysis, and management of qualitative data to help pharmacists interested in doing research in their practice to continue their learning in this area. Qualitative research can help researchers to access the thoughts and feelings of research participants, which can enable development of an understanding of the meaning that people ascribe to their experiences. Whereas quantitative research methods can be used to determine how many people undertake particular behaviours, qualitative methods can help researchers to understand how and why such behaviours take place. Within the context of pharmacy practice research, qualitative approaches have been used to examine a diverse array of topics, including the perceptions of key stakeholders regarding prescribing by pharmacists and the postgraduation employment experiences of young pharmacists (see “Further Reading” section at the end of this article).

In the previous paper, 1 we outlined 3 commonly used methodologies: ethnography 2 , grounded theory 3 , and phenomenology. 4 Briefly, ethnography involves researchers using direct observation to study participants in their “real life” environment, sometimes over extended periods. Grounded theory and its later modified versions (e.g., Strauss and Corbin 5 ) use face-to-face interviews and interactions such as focus groups to explore a particular research phenomenon and may help in clarifying a less-well-understood problem, situation, or context. Phenomenology shares some features with grounded theory (such as an exploration of participants’ behaviour) and uses similar techniques to collect data, but it focuses on understanding how human beings experience their world. It gives researchers the opportunity to put themselves in another person’s shoes and to understand the subjective experiences of participants. 6 Some researchers use qualitative methodologies but adopt a different standpoint, and an example of this appears in the work of Thurston and others, 7 discussed later in this paper.

Qualitative work requires reflection on the part of researchers, both before and during the research process, as a way of providing context and understanding for readers. When being reflexive, researchers should not try to simply ignore or avoid their own biases (as this would likely be impossible); instead, reflexivity requires researchers to reflect upon and clearly articulate their position and subjectivities (world view, perspectives, biases), so that readers can better understand the filters through which questions were asked, data were gathered and analyzed, and findings were reported. From this perspective, bias and subjectivity are not inherently negative but they are unavoidable; as a result, it is best that they be articulated up-front in a manner that is clear and coherent for readers.

THE PARTICIPANT’S VIEWPOINT

What qualitative study seeks to convey is why people have thoughts and feelings that might affect the way they behave. Such study may occur in any number of contexts, but here, we focus on pharmacy practice and the way people behave with regard to medicines use (e.g., to understand patients’ reasons for nonadherence with medication therapy or to explore physicians’ resistance to pharmacists’ clinical suggestions). As we suggested in our earlier article, 1 an important point about qualitative research is that there is no attempt to generalize the findings to a wider population. Qualitative research is used to gain insights into people’s feelings and thoughts, which may provide the basis for a future stand-alone qualitative study or may help researchers to map out survey instruments for use in a quantitative study. It is also possible to use different types of research in the same study, an approach known as “mixed methods” research, and further reading on this topic may be found at the end of this paper.

The role of the researcher in qualitative research is to attempt to access the thoughts and feelings of study participants. This is not an easy task, as it involves asking people to talk about things that may be very personal to them. Sometimes the experiences being explored are fresh in the participant’s mind, whereas on other occasions reliving past experiences may be difficult. However the data are being collected, a primary responsibility of the researcher is to safeguard participants and their data. Mechanisms for such safeguarding must be clearly articulated to participants and must be approved by a relevant research ethics review board before the research begins. Researchers and practitioners new to qualitative research should seek advice from an experienced qualitative researcher before embarking on their project.

DATA COLLECTION

Whatever philosophical standpoint the researcher is taking and whatever the data collection method (e.g., focus group, one-to-one interviews), the process will involve the generation of large amounts of data. In addition to the variety of study methodologies available, there are also different ways of making a record of what is said and done during an interview or focus group, such as taking handwritten notes or video-recording. If the researcher is audio- or video-recording data collection, then the recordings must be transcribed verbatim before data analysis can begin. As a rough guide, it can take an experienced researcher/transcriber 8 hours to transcribe one 45-minute audio-recorded interview, a process than will generate 20–30 pages of written dialogue.

Many researchers will also maintain a folder of “field notes” to complement audio-taped interviews. Field notes allow the researcher to maintain and comment upon impressions, environmental contexts, behaviours, and nonverbal cues that may not be adequately captured through the audio-recording; they are typically handwritten in a small notebook at the same time the interview takes place. Field notes can provide important context to the interpretation of audio-taped data and can help remind the researcher of situational factors that may be important during data analysis. Such notes need not be formal, but they should be maintained and secured in a similar manner to audio tapes and transcripts, as they contain sensitive information and are relevant to the research. For more information about collecting qualitative data, please see the “Further Reading” section at the end of this paper.

DATA ANALYSIS AND MANAGEMENT

If, as suggested earlier, doing qualitative research is about putting oneself in another person’s shoes and seeing the world from that person’s perspective, the most important part of data analysis and management is to be true to the participants. It is their voices that the researcher is trying to hear, so that they can be interpreted and reported on for others to read and learn from. To illustrate this point, consider the anonymized transcript excerpt presented in Appendix 1 , which is taken from a research interview conducted by one of the authors (J.S.). We refer to this excerpt throughout the remainder of this paper to illustrate how data can be managed, analyzed, and presented.

Interpretation of Data

Interpretation of the data will depend on the theoretical standpoint taken by researchers. For example, the title of the research report by Thurston and others, 7 “Discordant indigenous and provider frames explain challenges in improving access to arthritis care: a qualitative study using constructivist grounded theory,” indicates at least 2 theoretical standpoints. The first is the culture of the indigenous population of Canada and the place of this population in society, and the second is the social constructivist theory used in the constructivist grounded theory method. With regard to the first standpoint, it can be surmised that, to have decided to conduct the research, the researchers must have felt that there was anecdotal evidence of differences in access to arthritis care for patients from indigenous and non-indigenous backgrounds. With regard to the second standpoint, it can be surmised that the researchers used social constructivist theory because it assumes that behaviour is socially constructed; in other words, people do things because of the expectations of those in their personal world or in the wider society in which they live. (Please see the “Further Reading” section for resources providing more information about social constructivist theory and reflexivity.) Thus, these 2 standpoints (and there may have been others relevant to the research of Thurston and others 7 ) will have affected the way in which these researchers interpreted the experiences of the indigenous population participants and those providing their care. Another standpoint is feminist standpoint theory which, among other things, focuses on marginalized groups in society. Such theories are helpful to researchers, as they enable us to think about things from a different perspective. Being aware of the standpoints you are taking in your own research is one of the foundations of qualitative work. Without such awareness, it is easy to slip into interpreting other people’s narratives from your own viewpoint, rather than that of the participants.

To analyze the example in Appendix 1 , we will adopt a phenomenological approach because we want to understand how the participant experienced the illness and we want to try to see the experience from that person’s perspective. It is important for the researcher to reflect upon and articulate his or her starting point for such analysis; for example, in the example, the coder could reflect upon her own experience as a female of a majority ethnocultural group who has lived within middle class and upper middle class settings. This personal history therefore forms the filter through which the data will be examined. This filter does not diminish the quality or significance of the analysis, since every researcher has his or her own filters; however, by explicitly stating and acknowledging what these filters are, the researcher makes it easer for readers to contextualize the work.

Transcribing and Checking

For the purposes of this paper it is assumed that interviews or focus groups have been audio-recorded. As mentioned above, transcribing is an arduous process, even for the most experienced transcribers, but it must be done to convert the spoken word to the written word to facilitate analysis. For anyone new to conducting qualitative research, it is beneficial to transcribe at least one interview and one focus group. It is only by doing this that researchers realize how difficult the task is, and this realization affects their expectations when asking others to transcribe. If the research project has sufficient funding, then a professional transcriber can be hired to do the work. If this is the case, then it is a good idea to sit down with the transcriber, if possible, and talk through the research and what the participants were talking about. This background knowledge for the transcriber is especially important in research in which people are using jargon or medical terms (as in pharmacy practice). Involving your transcriber in this way makes the work both easier and more rewarding, as he or she will feel part of the team. Transcription editing software is also available, but it is expensive. For example, ELAN (more formally known as EUDICO Linguistic Annotator, developed at the Technical University of Berlin) 8 is a tool that can help keep data organized by linking media and data files (particularly valuable if, for example, video-taping of interviews is complemented by transcriptions). It can also be helpful in searching complex data sets. Products such as ELAN do not actually automatically transcribe interviews or complete analyses, and they do require some time and effort to learn; nonetheless, for some research applications, it may be a valuable to consider such software tools.

All audio recordings should be transcribed verbatim, regardless of how intelligible the transcript may be when it is read back. Lines of text should be numbered. Once the transcription is complete, the researcher should read it while listening to the recording and do the following: correct any spelling or other errors; anonymize the transcript so that the participant cannot be identified from anything that is said (e.g., names, places, significant events); insert notations for pauses, laughter, looks of discomfort; insert any punctuation, such as commas and full stops (periods) (see Appendix 1 for examples of inserted punctuation), and include any other contextual information that might have affected the participant (e.g., temperature or comfort of the room).

Dealing with the transcription of a focus group is slightly more difficult, as multiple voices are involved. One way of transcribing such data is to “tag” each voice (e.g., Voice A, Voice B). In addition, the focus group will usually have 2 facilitators, whose respective roles will help in making sense of the data. While one facilitator guides participants through the topic, the other can make notes about context and group dynamics. More information about group dynamics and focus groups can be found in resources listed in the “Further Reading” section.

Reading between the Lines

During the process outlined above, the researcher can begin to get a feel for the participant’s experience of the phenomenon in question and can start to think about things that could be pursued in subsequent interviews or focus groups (if appropriate). In this way, one participant’s narrative informs the next, and the researcher can continue to interview until nothing new is being heard or, as it says in the text books, “saturation is reached”. While continuing with the processes of coding and theming (described in the next 2 sections), it is important to consider not just what the person is saying but also what they are not saying. For example, is a lengthy pause an indication that the participant is finding the subject difficult, or is the person simply deciding what to say? The aim of the whole process from data collection to presentation is to tell the participants’ stories using exemplars from their own narratives, thus grounding the research findings in the participants’ lived experiences.

Smith 9 suggested a qualitative research method known as interpretative phenomenological analysis, which has 2 basic tenets: first, that it is rooted in phenomenology, attempting to understand the meaning that individuals ascribe to their lived experiences, and second, that the researcher must attempt to interpret this meaning in the context of the research. That the researcher has some knowledge and expertise in the subject of the research means that he or she can have considerable scope in interpreting the participant’s experiences. Larkin and others 10 discussed the importance of not just providing a description of what participants say. Rather, interpretative phenomenological analysis is about getting underneath what a person is saying to try to truly understand the world from his or her perspective.

Once all of the research interviews have been transcribed and checked, it is time to begin coding. Field notes compiled during an interview can be a useful complementary source of information to facilitate this process, as the gap in time between an interview, transcribing, and coding can result in memory bias regarding nonverbal or environmental context issues that may affect interpretation of data.

Coding refers to the identification of topics, issues, similarities, and differences that are revealed through the participants’ narratives and interpreted by the researcher. This process enables the researcher to begin to understand the world from each participant’s perspective. Coding can be done by hand on a hard copy of the transcript, by making notes in the margin or by highlighting and naming sections of text. More commonly, researchers use qualitative research software (e.g., NVivo, QSR International Pty Ltd; www.qsrinternational.com/products_nvivo.aspx ) to help manage their transcriptions. It is advised that researchers undertake a formal course in the use of such software or seek supervision from a researcher experienced in these tools.

Returning to Appendix 1 and reading from lines 8–11, a code for this section might be “diagnosis of mental health condition”, but this would just be a description of what the participant is talking about at that point. If we read a little more deeply, we can ask ourselves how the participant might have come to feel that the doctor assumed he or she was aware of the diagnosis or indeed that they had only just been told the diagnosis. There are a number of pauses in the narrative that might suggest the participant is finding it difficult to recall that experience. Later in the text, the participant says “nobody asked me any questions about my life” (line 19). This could be coded simply as “health care professionals’ consultation skills”, but that would not reflect how the participant must have felt never to be asked anything about his or her personal life, about the participant as a human being. At the end of this excerpt, the participant just trails off, recalling that no-one showed any interest, which makes for very moving reading. For practitioners in pharmacy, it might also be pertinent to explore the participant’s experience of akathisia and why this was left untreated for 20 years.

One of the questions that arises about qualitative research relates to the reliability of the interpretation and representation of the participants’ narratives. There are no statistical tests that can be used to check reliability and validity as there are in quantitative research. However, work by Lincoln and Guba 11 suggests that there are other ways to “establish confidence in the ‘truth’ of the findings” (p. 218). They call this confidence “trustworthiness” and suggest that there are 4 criteria of trustworthiness: credibility (confidence in the “truth” of the findings), transferability (showing that the findings have applicability in other contexts), dependability (showing that the findings are consistent and could be repeated), and confirmability (the extent to which the findings of a study are shaped by the respondents and not researcher bias, motivation, or interest).

One way of establishing the “credibility” of the coding is to ask another researcher to code the same transcript and then to discuss any similarities and differences in the 2 resulting sets of codes. This simple act can result in revisions to the codes and can help to clarify and confirm the research findings.

Theming refers to the drawing together of codes from one or more transcripts to present the findings of qualitative research in a coherent and meaningful way. For example, there may be examples across participants’ narratives of the way in which they were treated in hospital, such as “not being listened to” or “lack of interest in personal experiences” (see Appendix 1 ). These may be drawn together as a theme running through the narratives that could be named “the patient’s experience of hospital care”. The importance of going through this process is that at its conclusion, it will be possible to present the data from the interviews using quotations from the individual transcripts to illustrate the source of the researchers’ interpretations. Thus, when the findings are organized for presentation, each theme can become the heading of a section in the report or presentation. Underneath each theme will be the codes, examples from the transcripts, and the researcher’s own interpretation of what the themes mean. Implications for real life (e.g., the treatment of people with chronic mental health problems) should also be given.

DATA SYNTHESIS

In this final section of this paper, we describe some ways of drawing together or “synthesizing” research findings to represent, as faithfully as possible, the meaning that participants ascribe to their life experiences. This synthesis is the aim of the final stage of qualitative research. For most readers, the synthesis of data presented by the researcher is of crucial significance—this is usually where “the story” of the participants can be distilled, summarized, and told in a manner that is both respectful to those participants and meaningful to readers. There are a number of ways in which researchers can synthesize and present their findings, but any conclusions drawn by the researchers must be supported by direct quotations from the participants. In this way, it is made clear to the reader that the themes under discussion have emerged from the participants’ interviews and not the mind of the researcher. The work of Latif and others 12 gives an example of how qualitative research findings might be presented.

Planning and Writing the Report

As has been suggested above, if researchers code and theme their material appropriately, they will naturally find the headings for sections of their report. Qualitative researchers tend to report “findings” rather than “results”, as the latter term typically implies that the data have come from a quantitative source. The final presentation of the research will usually be in the form of a report or a paper and so should follow accepted academic guidelines. In particular, the article should begin with an introduction, including a literature review and rationale for the research. There should be a section on the chosen methodology and a brief discussion about why qualitative methodology was most appropriate for the study question and why one particular methodology (e.g., interpretative phenomenological analysis rather than grounded theory) was selected to guide the research. The method itself should then be described, including ethics approval, choice of participants, mode of recruitment, and method of data collection (e.g., semistructured interviews or focus groups), followed by the research findings, which will be the main body of the report or paper. The findings should be written as if a story is being told; as such, it is not necessary to have a lengthy discussion section at the end. This is because much of the discussion will take place around the participants’ quotes, such that all that is needed to close the report or paper is a summary, limitations of the research, and the implications that the research has for practice. As stated earlier, it is not the intention of qualitative research to allow the findings to be generalized, and therefore this is not, in itself, a limitation.

Planning out the way that findings are to be presented is helpful. It is useful to insert the headings of the sections (the themes) and then make a note of the codes that exemplify the thoughts and feelings of your participants. It is generally advisable to put in the quotations that you want to use for each theme, using each quotation only once. After all this is done, the telling of the story can begin as you give your voice to the experiences of the participants, writing around their quotations. Do not be afraid to draw assumptions from the participants’ narratives, as this is necessary to give an in-depth account of the phenomena in question. Discuss these assumptions, drawing on your participants’ words to support you as you move from one code to another and from one theme to the next. Finally, as appropriate, it is possible to include examples from literature or policy documents that add support for your findings. As an exercise, you may wish to code and theme the sample excerpt in Appendix 1 and tell the participant’s story in your own way. Further reading about “doing” qualitative research can be found at the end of this paper.

CONCLUSIONS

Qualitative research can help researchers to access the thoughts and feelings of research participants, which can enable development of an understanding of the meaning that people ascribe to their experiences. It can be used in pharmacy practice research to explore how patients feel about their health and their treatment. Qualitative research has been used by pharmacists to explore a variety of questions and problems (see the “Further Reading” section for examples). An understanding of these issues can help pharmacists and other health care professionals to tailor health care to match the individual needs of patients and to develop a concordant relationship. Doing qualitative research is not easy and may require a complete rethink of how research is conducted, particularly for researchers who are more familiar with quantitative approaches. There are many ways of conducting qualitative research, and this paper has covered some of the practical issues regarding data collection, analysis, and management. Further reading around the subject will be essential to truly understand this method of accessing peoples’ thoughts and feelings to enable researchers to tell participants’ stories.

Appendix 1. Excerpt from a sample transcript

The participant (age late 50s) had suffered from a chronic mental health illness for 30 years. The participant had become a “revolving door patient,” someone who is frequently in and out of hospital. As the participant talked about past experiences, the researcher asked:

• What was treatment like 30 years ago?
• Umm—well it was pretty much they could do what they wanted with you because I was put into the er, the er kind of system er, I was just on
• endless section threes.
• Really…
• But what I didn’t realize until later was that if you haven’t actually posed a threat to someone or yourself they can’t really do that but I didn’t know
• that. So wh-when I first went into hospital they put me on the forensic ward ’cause they said, “We don’t think you’ll stay here we think you’ll just
• run-run away.” So they put me then onto the acute admissions ward and – er – I can remember one of the first things I recall when I got onto that
• ward was sitting down with a er a Dr XXX. He had a book this thick [gestures] and on each page it was like three questions and he went through
• all these questions and I answered all these questions. So we’re there for I don’t maybe two hours doing all that and he asked me he said “well
• when did somebody tell you then that you have schizophrenia” I said “well nobody’s told me that” so he seemed very surprised but nobody had
• actually [pause] whe-when I first went up there under police escort erm the senior kind of consultants people I’d been to where I was staying and
• ermm so er [pause] I . . . the, I can remember the very first night that I was there and given this injection in this muscle here [gestures] and just
• having dreadful side effects the next day I woke up [pause]
• . . . and I suffered that akathesia I swear to you, every minute of every day for about 20 years.
• Oh how awful.
• And that side of it just makes life impossible so the care on the wards [pause] umm I don’t know it’s kind of, it’s kind of hard to put into words
• [pause]. Because I’m not saying they were sort of like not friendly or interested but then nobody ever seemed to want to talk about your life [pause]
• nobody asked me any questions about my life. The only questions that came into was they asked me if I’d be a volunteer for these student exams
• and things and I said “yeah” so all the questions were like “oh what jobs have you done,” er about your relationships and things and er but
• nobody actually sat down and had a talk and showed some interest in you as a person you were just there basically [pause] um labelled and you
• know there was there was [pause] but umm [pause] yeah . . .

This article is the 10th in the CJHP Research Primer Series, an initiative of the CJHP Editorial Board and the CSHP Research Committee. The planned 2-year series is intended to appeal to relatively inexperienced researchers, with the goal of building research capacity among practising pharmacists. The articles, presenting simple but rigorous guidance to encourage and support novice researchers, are being solicited from authors with appropriate expertise.

Previous articles in this series:

Bond CM. The research jigsaw: how to get started. Can J Hosp Pharm . 2014;67(1):28–30.

Tully MP. Research: articulating questions, generating hypotheses, and choosing study designs. Can J Hosp Pharm . 2014;67(1):31–4.

Loewen P. Ethical issues in pharmacy practice research: an introductory guide. Can J Hosp Pharm. 2014;67(2):133–7.

Tsuyuki RT. Designing pharmacy practice research trials. Can J Hosp Pharm . 2014;67(3):226–9.

Bresee LC. An introduction to developing surveys for pharmacy practice research. Can J Hosp Pharm . 2014;67(4):286–91.

Gamble JM. An introduction to the fundamentals of cohort and case–control studies. Can J Hosp Pharm . 2014;67(5):366–72.

Austin Z, Sutton J. Qualitative research: getting started. C an J Hosp Pharm . 2014;67(6):436–40.

Houle S. An introduction to the fundamentals of randomized controlled trials in pharmacy research. Can J Hosp Pharm . 2014; 68(1):28–32.

Charrois TL. Systematic reviews: What do you need to know to get started? Can J Hosp Pharm . 2014;68(2):144–8.

Competing interests: None declared.

Further Reading

Examples of qualitative research in pharmacy practice.

• Farrell B, Pottie K, Woodend K, Yao V, Dolovich L, Kennie N, et al. Shifts in expectations: evaluating physicians’ perceptions as pharmacists integrated into family practice. J Interprof Care. 2010; 24 (1):80–9. [ PubMed ] [ Google Scholar ]
• Gregory P, Austin Z. Postgraduation employment experiences of new pharmacists in Ontario in 2012–2013. Can Pharm J. 2014; 147 (5):290–9. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Marks PZ, Jennnings B, Farrell B, Kennie-Kaulbach N, Jorgenson D, Pearson-Sharpe J, et al. “I gained a skill and a change in attitude”: a case study describing how an online continuing professional education course for pharmacists supported achievement of its transfer to practice outcomes. Can J Univ Contin Educ. 2014; 40 (2):1–18. [ Google Scholar ]
• Nair KM, Dolovich L, Brazil K, Raina P. It’s all about relationships: a qualitative study of health researchers’ perspectives on interdisciplinary research. BMC Health Serv Res. 2008; 8 :110. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Pojskic N, MacKeigan L, Boon H, Austin Z. Initial perceptions of key stakeholders in Ontario regarding independent prescriptive authority for pharmacists. Res Soc Adm Pharm. 2014; 10 (2):341–54. [ PubMed ] [ Google Scholar ]

Qualitative Research in General

• Breakwell GM, Hammond S, Fife-Schaw C. Research methods in psychology. Thousand Oaks (CA): Sage Publications; 1995. [ Google Scholar ]
• Given LM. 100 questions (and answers) about qualitative research. Thousand Oaks (CA): Sage Publications; 2015. [ Google Scholar ]
• Miles B, Huberman AM. Qualitative data analysis. Thousand Oaks (CA): Sage Publications; 2009. [ Google Scholar ]
• Patton M. Qualitative research and evaluation methods. Thousand Oaks (CA): Sage Publications; 2002. [ Google Scholar ]
• Willig C. Introducing qualitative research in psychology. Buckingham (UK): Open University Press; 2001. [ Google Scholar ]

Group Dynamics in Focus Groups

• Farnsworth J, Boon B. Analysing group dynamics within the focus group. Qual Res. 2010; 10 (5):605–24. [ Google Scholar ]

Social Constructivism

• Social constructivism. Berkeley (CA): University of California, Berkeley, Berkeley Graduate Division, Graduate Student Instruction Teaching & Resource Center; [cited 2015 June 4]. Available from: http://gsi.berkeley.edu/gsi-guide-contents/learning-theory-research/social-constructivism/ [ Google Scholar ]

Mixed Methods

• Creswell J. Research design: qualitative, quantitative, and mixed methods approaches. Thousand Oaks (CA): Sage Publications; 2009. [ Google Scholar ]

Collecting Qualitative Data

• Arksey H, Knight P. Interviewing for social scientists: an introductory resource with examples. Thousand Oaks (CA): Sage Publications; 1999. [ Google Scholar ]
• Guest G, Namey EE, Mitchel ML. Collecting qualitative data: a field manual for applied research. Thousand Oaks (CA): Sage Publications; 2013. [ Google Scholar ]

Constructivist Grounded Theory

• Charmaz K. Grounded theory: objectivist and constructivist methods. In: Denzin N, Lincoln Y, editors. Handbook of qualitative research. 2nd ed. Thousand Oaks (CA): Sage Publications; 2000. pp. 509–35. [ Google Scholar ]

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29 Qualitative Data Analysis Strategies

Johnny Saldaña, School of Theatre and Film, Arizona State University

• Published: 02 September 2020
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This chapter provides an overview of selected qualitative data analysis strategies with a particular focus on codes and coding. Preparatory strategies for a qualitative research study and data management are first outlined. Six coding methods are then profiled using comparable interview data: process coding, in vivo coding, descriptive coding, values coding, dramaturgical coding, and versus coding. Strategies for constructing themes and assertions from the data follow. Analytic memo writing is woven throughout as a method for generating additional analytic insight. Next, display and arts-based strategies are provided, followed by recommended qualitative data analytic software programs and a discussion on verifying the researcher’s analytic findings.

Qualitative Data Analysis Strategies

Anthropologist Clifford Geertz ( 1983 ) charmingly mused, “Life is just a bowl of strategies” (p. 25). Strategy , as I use it here, refers to a carefully considered plan or method to achieve a particular goal. The goal in this case is to develop a write-up of your analytic work with the qualitative data you have been given and collected as part of a study. The plans and methods you might employ to achieve that goal are what this article profiles.

Some may perceive strategy as an inappropriate, if not manipulative, word, suggesting formulaic or regimented approaches to inquiry. I assure you that is not my intent. My use of strategy is dramaturgical in nature: Strategies are actions that characters in plays take to overcome obstacles to achieve their objectives. Actors portraying these characters rely on action verbs to generate belief within themselves and to motivate them as they interpret their lines and move appropriately on stage.

What I offer is a qualitative researcher’s array of actions from which to draw to overcome the obstacles to thinking to achieve an analysis of your data. But unlike the prescripted text of a play in which the obstacles, strategies, and outcomes have been predetermined by the playwright, your work must be improvisational—acting, reacting, and interacting with data on a moment-by-moment basis to determine what obstacles stand in your way and thus what strategies you should take to reach your goals.

Another intriguing quote to keep in mind comes from research methodologist Robert E. Stake ( 1995 ), who posited, “Good research is not about good methods as much as it is about good thinking” (p. 19). In other words, strategies can take you only so far. You can have a box full of tools, but if you do not know how to use them well or use them creatively, the collection seems rather purposeless. One of the best ways we learn is by doing . So, pick up one or more of these strategies (in the form of verbs) and take analytic action with your data. Also keep in mind that these are discussed in the order in which they may typically occur, although humans think cyclically, iteratively, and reverberatively, and each research project has its unique contexts and needs. Be prepared for your mind to jump purposefully and/or idiosyncratically from one strategy to another throughout the study.

Qualitative Data Analysis Strategy: To Foresee

To foresee in qualitative data analysis (QDA) is to reflect beforehand on what forms of data you will most likely need and collect, which thus informs what types of data analytic strategies you anticipate using. Analysis, in a way, begins even before you collect data (Saldaña & Omasta, 2018 ). As you design your research study in your mind and on a text editing page, one strategy is to consider what types of data you may need to help inform and answer your central and related research questions. Interview transcripts, participant observation field notes, documents, artifacts, photographs, video recordings, and so on are not only forms of data but also foundations for how you may plan to analyze them. A participant interview, for example, suggests that you will transcribe all or relevant portions of the recording and use both the transcription and the recording itself as sources for data analysis. Any analytic memos (discussed later) you make about your impressions of the interview also become data to analyze. Even the computing software you plan to employ will be relevant to data analysis because it may help or hinder your efforts.

As your research design formulates, compose one to two paragraphs that outline how your QDA may proceed. This will necessitate that you have some background knowledge of the vast array of methods available to you. Thus, surveying the literature is vital preparatory work.

Qualitative Data Analysis Strategy: To Survey

To survey in QDA is to look for and consider the applicability of the QDA literature in your field that may provide useful guidance for your forthcoming data analytic work. General sources in QDA will provide a good starting point for acquainting you with the data analysis strategies available for the variety of methodologies or genres in qualitative inquiry (e.g., ethnography, phenomenology, case study, arts-based research, mixed methods). One of the most accessible (and humorous) is Galman’s ( 2013 ) The Good, the Bad, and the Data , and one of the most richly detailed is Frederick J. Wertz et al.’s ( 2011 ) Five Ways of Doing Qualitative Analysis . The author’s core texts for this chapter come from The Coding Manual for Qualitative Researchers (Saldaña, 2016 ) and Qualitative Research: Analyzing Life (Saldaña & Omasta, 2018 ).

If your study’s methodology or approach is grounded theory, for example, then a survey of methods works by authors such as Barney G. Glaser, Anselm L. Strauss, Juliet Corbin, and, in particular, the prolific Kathy Charmaz ( 2014 ) may be expected. But there has been a recent outpouring of additional book publications in grounded theory by Birks and Mills ( 2015 ), Bryant ( 2017 ), Bryant and Charmaz ( 2019 ), and Stern and Porr ( 2011 ), plus the legacy of thousands of articles and chapters across many disciplines that have addressed grounded theory in their studies.

Fields such as education, psychology, social work, healthcare, and others also have their own QDA methods literature in the form of texts and journals, as well as international conferences and workshops for members of the profession. It is important to have had some university coursework and/or mentorship in qualitative research to suitably prepare you for the intricacies of QDA, and you must acknowledge that the emergent nature of qualitative inquiry may require you to adopt analysis strategies that differ from what you originally planned.

Qualitative Data Analysis Strategy: To Collect

To collect in QDA is to receive the data given to you by participants and those data you actively gather to inform your study. Qualitative data analysis is concurrent with data collection and management. As interviews are transcribed, field notes are fleshed out, and documents are filed, the researcher uses opportunities to carefully read the corpus and make preliminary notations directly on the data documents by highlighting, bolding, italicizing, or noting in some way any particularly interesting or salient portions. As these data are initially reviewed, the researcher also composes supplemental analytic memos that include first impressions, reminders for follow-up, preliminary connections, and other thinking matters about the phenomena at work.

Some of the most common fieldwork tools you might use to collect data are notepads, pens and pencils; file folders for hard-copy documents; a laptop, tablet, or desktop with text editing software (Microsoft Word and Excel are most useful) and Internet access; and a digital camera and voice recorder (functions available on many electronic devices such as smartphones). Some fieldworkers may even employ a digital video camera to record social action, as long as participant permissions have been secured. But everything originates from the researcher. Your senses are immersed in the cultural milieu you study, taking in and holding onto relevant details, or significant trivia , as I call them. You become a human camera, zooming out to capture the broad landscape of your field site one day and then zooming in on a particularly interesting individual or phenomenon the next. Your analysis is only as good as the data you collect.

Fieldwork can be an overwhelming experience because so many details of social life are happening in front of you. Take a holistic approach to your entrée, but as you become more familiar with the setting and participants, actively focus on things that relate to your research topic and questions. Keep yourself open to the intriguing, surprising, and disturbing (Sunstein & Chiseri-Strater, 2012 , p. 115), because these facets enrich your study by making you aware of the unexpected.

Qualitative Data Analysis Strategy: To Feel

To feel in QDA is to gain deep emotional insight into the social worlds you study and what it means to be human. Virtually everything we do has an accompanying emotion(s), and feelings are both reactions and stimuli for action. Others’ emotions clue you to their motives, values, attitudes, beliefs, worldviews, identities, and other subjective perceptions and interpretations. Acknowledge that emotional detachment is not possible in field research. Attunement to the emotional experiences of your participants plus sympathetic and empathetic responses to the actions around you are necessary in qualitative endeavors. Your own emotional responses during fieldwork are also data because they document the tacit and visceral. It is important during such analytic reflection to assess why your emotional reactions were as they were. But it is equally important not to let emotions alone steer the course of your study. A proper balance must be found between feelings and facts.

Qualitative Data Analysis Strategy: To Organize

To organize in QDA is to maintain an orderly repository of data for easy access and analysis. Even in the smallest of qualitative studies, a large amount of data will be collected across time. Prepare both a hard drive and hard-copy folders for digital data and paperwork, and back up all materials for security from loss. I recommend that each data unit (e.g., one interview transcript, one document, one day’s worth of field notes) have its own file, with subfolders specifying the data forms and research study logistics (e.g., interviews, field notes, documents, institutional review board correspondence, calendar).

For small-scale qualitative studies, I have found it quite useful to maintain one large master file with all participant and field site data copied and combined with the literature review and accompanying researcher analytic memos. This master file is used to cut and paste related passages together, deleting what seems unnecessary as the study proceeds and eventually transforming the document into the final report itself. Cosmetic devices such as font style, font size, rich text (italicizing, bolding, underlining, etc.), and color can help you distinguish between different data forms and highlight significant passages. For example, descriptive, narrative passages of field notes are logged in regular font. “Quotations, things spoken by participants, are logged in bold font.”   Observer’s comments, such as the researcher’s subjective impressions or analytic jottings, are set in italics.

Qualitative Data Analysis Strategy: To Jot

To jot in QDA is to write occasional, brief notes about your thinking or reminders for follow-up. A jot is a phrase or brief sentence that will fit on a standard-size sticky note. As data are brought and documented together, take some initial time to review their contents and jot some notes about preliminary patterns, participant quotes that seem vivid, anomalies in the data, and so forth.

As you work on a project, keep something to write with or to voice record with you at all times to capture your fleeting thoughts. You will most likely find yourself thinking about your research when you are not working exclusively on the project, and a “mental jot” may occur to you as you ruminate on logistical or analytic matters. Document the thought in some way for later retrieval and elaboration as an analytic memo.

Qualitative Data Analysis Strategy: To Prioritize

To prioritize in QDA is to determine which data are most significant in your corpus and which tasks are most necessary. During fieldwork, massive amounts of data in various forms may be collected, and your mind can be easily overwhelmed by the magnitude of the quantity, its richness, and its management. Decisions will need to be made about the most pertinent data because they help answer your research questions or emerge as salient pieces of evidence. As a sweeping generalization, approximately one half to two thirds of what you collect may become unnecessary as you proceed toward the more formal stages of QDA.

To prioritize in QDA is also to determine what matters most in your assembly of codes, categories, patterns, themes, assertions, propositions, and concepts. Return to your research purpose and questions to keep you framed for what the focus should be.

Qualitative Data Analysis Strategy: To Analyze

To analyze in QDA is to observe and discern patterns within data and to construct meanings that seem to capture their essences and essentials. Just as there are a variety of genres, elements, and styles of qualitative research, so too are there a variety of methods available for QDA. Analytic choices are most often based on what methods will harmonize with your genre selection and conceptual framework, what will generate the most sufficient answers to your research questions, and what will best represent and present the project’s findings.

Analysis can range from the factual to the conceptual to the interpretive. Analysis can also range from a straightforward descriptive account to an emergently constructed grounded theory to an evocatively composed short story. A qualitative research project’s outcomes may range from rigorously achieved, insightful answers to open-ended, evocative questions; from rich descriptive detail to a bullet-point list of themes; and from third-person, objective reportage to first-person, emotion-laden poetry. Just as there are multiple destinations in qualitative research, there are multiple pathways and journeys along the way.

Analysis is accelerated as you take cognitive ownership of your data. By reading and rereading the corpus, you gain intimate familiarity with its contents and begin to notice significant details as well as make new connections and insights about their meanings. Patterns, categories, themes, and their interrelationships become more evident the more you know the subtleties of the database.

Since qualitative research’s design, fieldwork, and data collection are most often provisional, emergent, and evolutionary processes, you reflect on and analyze the data as you gather them and proceed through the project. If preplanned methods are not working, you change them to secure the data you need. There is generally a postfieldwork period when continued reflection and more systematic data analysis occur, concurrent with or followed by additional data collection, if needed, and the more formal write-up of the study, which is in itself an analytic act. Through field note writing, interview transcribing, analytic memo writing, and other documentation processes, you gain cognitive ownership of your data; and the intuitive, tacit, synthesizing capabilities of your brain begin sensing patterns, making connections, and seeing the bigger picture. The purpose and outcome of data analysis is to reveal to others through fresh insights what we have observed and discovered about the human condition. Fortunately, there are heuristics for reorganizing and reflecting on your qualitative data to help you achieve that goal.

Qualitative Data Analysis Strategy: To Pattern

To pattern in QDA is to detect similarities within and regularities among the data you have collected. The natural world is filled with patterns because we, as humans, have constructed them as such. Stars in the night sky are not just a random assembly; our ancestors pieced them together to form constellations like the Big Dipper. A collection of flowers growing wild in a field has a pattern, as does an individual flower’s patterns of leaves and petals. Look at the physical objects humans have created and notice how pattern oriented we are in our construction, organization, and decoration. Look around you in your environment and notice how many patterns are evident on your clothing, in a room, and on most objects themselves. Even our sometimes mundane daily and long-term human actions are reproduced patterns in the form of routines, rituals, rules, roles, and relationships (Saldaña & Omasta, 2018 ).

This human propensity for pattern-making follows us into QDA. From the vast array of interview transcripts, field notes, documents, and other forms of data, there is this instinctive, hardwired need to bring order to the collection—not just to reorganize it but to look for and construct patterns out of it. The discernment of patterns is one of the first steps in the data analytic process, and the methods described next are recommended ways to construct them.

Qualitative Data Analysis Strategy: To Code

To code in QDA is to assign a truncated, symbolic meaning to each datum for purposes of qualitative analysis—primarily patterning and categorizing. Coding is a heuristic—a method of discovery—to the meanings of individual sections of data. These codes function as a way of patterning, classifying, and later reorganizing them into emergent categories for further analysis. Different types of codes exist for different types of research genres and qualitative data analytic approaches, but this chapter will focus on only a few selected methods. First, a code can be defined as follows:

A code in qualitative data analysis is most often 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. The data can consist of interview transcripts, participant observation field notes, journals, documents, open-ended survey responses, drawings, artifacts, photographs, video, Internet sites, e-mail correspondence, academic and fictional literature, and so on. The portion of data coded … can range in magnitude from a single word to a full paragraph, an entire page of text or a stream of moving images.… Just as a title represents and captures a book or film or poem’s primary content and essence, so does a code represent and capture a datum’s primary content and essence. (Saldaña, 2016 , p. 4)

One helpful precoding task is to divide or parse long selections of field note or interview transcript data into shorter stanzas . Stanza division unitizes or “chunks” the corpus into more manageable paragraph-like units for coding assignments and analysis. The transcript sample that follows illustrates one possible way of inserting line breaks between self-standing passages of interview text for easier readability.

Process Coding

As a first coding example, the following interview excerpt about an employed, single, lower middle-class adult male’s spending habits during a difficult economic period in the United States is coded in the right-hand margin in capital letters. The superscript numbers match the beginning of the datum unit with its corresponding code. This method is called process coding (Charmaz, 2014 ), and it uses gerunds (“-ing” words) exclusively to represent action suggested by the data. Processes can consist of observable human actions (e.g., BUYING BARGAINS), mental or internal processes (e.g., THINKING TWICE), and more conceptual ideas (e.g., APPRECIATING WHAT YOU’VE GOT). Notice that the interviewer’s (I) portions are not coded, just the participant’s (P). A code is applied each time the subtopic of the interview shifts—even within a stanza—and the same codes can (and should) be used more than once if the subtopics are similar. The central research question driving this qualitative study is, “In what ways are middle-class Americans influenced and affected by an economic recession?”

Different researchers analyzing this same piece of data may develop completely different codes, depending on their personal lenses, filters, and angles. The previous codes are only one person’s interpretation of what is happening in the data, not a definitive list. The process codes have transformed the raw data units into new symbolic representations for analysis. A listing of the codes applied to this interview transcript, in the order they appear, reads:

BUYING BARGAINS

QUESTIONING A PURCHASE

THINKING TWICE

STOCKING UP

REFUSING SACRIFICE

PRIORITIZING

FINDING ALTERNATIVES

LIVING CHEAPLY

NOTICING CHANGES

STAYING INFORMED

MAINTAINING HEALTH

PICKING UP THE TAB

APPRECIATING WHAT YOU’VE GOT

Coding the data is the first step in this approach to QDA, and categorization is just one of the next possible steps.

Qualitative Data Analysis Strategy: To Categorize

To categorize in QDA is to cluster similar or comparable codes into groups for pattern construction and further analysis. Humans categorize things in innumerable ways. Think of an average apartment or house’s layout. The rooms of a dwelling have been constructed or categorized by their builders and occupants according to function. A kitchen is designated as an area to store and prepare food and to store the cooking and dining materials, such as pots, pans, and utensils. A bedroom is designated for sleeping, a closet for clothing storage, a bathroom for bodily functions and hygiene, and so on. Each room is like a category in which related and relevant patterns of human action occur. There are exceptions now and then, such as eating breakfast in bed rather than in a dining area or living in a small studio apartment in which most possessions are contained within one large room (but nonetheless are most often organized and clustered into subcategories according to function and optimal use of space).

The point is that the patterns of social action we designate into categories during QDA are not perfectly bounded. Category construction is our best attempt to cluster the most seemingly alike things into the most seemingly appropriate groups. Categorizing is reorganizing and reordering the vast array of data from a study because it is from these smaller, larger, and meaning-rich units that we can better grasp the particular features of each one and the categories’ possible interrelationships with one another.

One analytic strategy with a list of codes is to classify them into similar clusters. The same codes share the same category, but it is also possible that a single code can merit its own group if you feel it is unique enough. After the codes have been classified, a category label is applied to each grouping. Sometimes a code can also double as a category name if you feel it best summarizes the totality of the cluster. Like coding, categorizing is an interpretive act, because there can be different ways of separating and collecting codes that seem to belong together. The cut-and-paste functions of text editing software are most useful for exploring which codes share something in common.

Below is my categorization of the 15 codes generated from the interview transcript presented earlier. Like the gerunds for process codes, the categories have also been labeled as “-ing” words to connote action. And there was no particular reason why 15 codes resulted in three categories—there could have been less or even more, but this is how the array came together after my reflections on which codes seemed to belong together. The category labels are ways of answering why they belong together. For at-a-glance differentiation, I place codes in CAPITAL LETTERS and categories in upper- and lowercase Bold Font :

Category 1: Thinking Strategically

Category 2: Spending Strategically

Category 3: Living Strategically

Notice that the three category labels share a common word: strategically . Where did this word come from? It came from analytic reflection on the original data, the codes, and the process of categorizing the codes and generating their category labels. It was the analyst’s choice based on the interpretation of what primary action was happening. Your categories generated from your coded data do not need to share a common word or phrase, but I find that this technique, when appropriate, helps build a sense of unity to the initial analytic scheme.

The three categories— Thinking Strategically, Spending Strategically , and Living Strategically —are then reflected on for how they might interact and interplay. This is where the next major facet of data analysis, analytic memos, enters the scheme. But a necessary section on the basic principles of interrelationship and analytic reasoning must precede that discussion.

Qualitative Data Analysis Strategy: To Interrelate

To interrelate in QDA is to propose connections within, between, and among the constituent elements of analyzed data. One task of QDA is to explore the ways our patterns and categories interact and interplay. I use these terms to suggest the qualitative equivalent of statistical correlation, but interaction and interplay are much more than a simple relationship. They imply interrelationship . Interaction refers to reverberative connections—for example, how one or more categories might influence and affect the others, how categories operate concurrently, or whether there is some kind of domino effect to them. Interplay refers to the structural and processual nature of categories—for example, whether some type of sequential order, hierarchy, or taxonomy exists; whether any overlaps occur; whether there is superordinate and subordinate arrangement; and what types of organizational frameworks or networks might exist among them. The positivist construct of cause and effect becomes influences and affects in QDA.

There can even be patterns of patterns and categories of categories if your mind thinks conceptually and abstractly enough. Our minds can intricately connect multiple phenomena, but only if the data and their analyses support the constructions. We can speculate about interaction and interplay all we want, but it is only through a more systematic investigation of the data—in other words, good thinking—that we can plausibly establish any possible interrelationships.

Qualitative Data Analysis Strategy: To Reason

To reason in QDA is to think in ways that lead to summative findings, causal probabilities, and evaluative conclusions. Unlike quantitative research, with its statistical formulas and established hypothesis-testing protocols, qualitative research has no standardized methods of data analysis. Rest assured, there are recommended guidelines from the field’s scholars and a legacy of analysis strategies from which to draw. But the primary heuristics (or methods of discovery) you apply during a study are retroductive, inductive, substructive, abductive , and deductive reasoning.

Retroduction is historic reconstruction, working backward to figure out how the current conditions came to exist. Induction is what we experientially explore and infer to be transferable from the particular to the general, based on an examination of the evidence and an accumulation of knowledge. Substruction takes things apart to more carefully examine the constituent elements of the whole. Abduction is surmising from a range of possibilities that which is most likely, those explanatory hunches of plausibility based on clues. Deduction is what we generally draw and conclude from established facts and evidence.

It is not always necessary to know the names of these five ways of reasoning as you proceed through analysis. In fact, you will more than likely reverberate quickly from one to another depending on the task at hand. But what is important to remember about reasoning is:

to examine the evidence carefully and make reasonable inferences;

to base your conclusions primarily on the participants’ experiences, not just your own;

not to take the obvious for granted, because sometimes the expected will not happen;

your hunches can be right and, at other times, quite wrong; and

to logically yet imaginatively think about what is going on and how it all comes together.

Futurists and inventors propose three questions when they think about creating new visions for the world: What is possible (induction)? What is plausible (abduction)? What is preferable (deduction)? These same three questions might be posed as you proceed through QDA and particularly through analytic memo writing, which is substructive and retroductive reflection on your analytic work thus far.

Qualitative Data Analysis Strategy: To Memo

To memo in QDA is to reflect in writing on the nuances, inferences, meanings, and transfer of coded and categorized data plus your analytic processes. Like field note writing, perspectives vary among practitioners as to the methods for documenting the researcher’s analytic insights and subjective experiences. Some advise that such reflections should be included in field notes as relevant to the data. Others advise that a separate researcher’s journal should be maintained for recording these impressions. And still others advise that these thoughts be documented as separate analytic memos. I prescribe the latter as a method because it is generated by and directly connected to the data themselves.

An analytic memo is a “think piece” of reflective free writing, a narrative that sets in words your interpretations of the data. Coding and categorizing are heuristics to detect some of the possible patterns and interrelationships at work within the corpus, and an analytic memo further articulates your retroductive, inductive, substructive, abductive, and deductive thinking processes on what things may mean. Though the metaphor is a bit flawed and limiting, think of codes and their consequent categories as separate jigsaw puzzle pieces and their integration into an analytic memo as the trial assembly of the complete picture.

What follows is an example of an analytic memo based on the earlier process coded and categorized interview transcript. It is intended not as the final write-up for a publication, but as an open-ended reflection on the phenomena and processes suggested by the data and their analysis thus far. As the study proceeds, however, initial and substantive analytic memos can be revisited and revised for eventual integration into the final report. Note how the memo is dated and given a title for future and further categorization, how participant quotes are occasionally included for evidentiary support, and how the category names are bolded and the codes kept in capital letters to show how they integrate or weave into the thinking:

April 14, 2017 EMERGENT CATEGORIES: A STRATEGIC AMALGAM There’s a popular saying: “Smart is the new rich.” This participant is Thinking Strategically about his spending through such tactics as THINKING TWICE and QUESTIONING A PURCHASE before he decides to invest in a product. There’s a heightened awareness of both immediate trends and forthcoming economic bad news that positively affects his Spending Strategically . However, he seems unaware that there are even more ways of LIVING CHEAPLY by FINDING ALTERNATIVES. He dines at all-you-can-eat restaurants as a way of STOCKING UP on meals, but doesn’t state that he could bring lunch from home to work, possibly saving even more money. One of his “bad habits” is cigarettes, which he refuses to give up; but he doesn’t seem to realize that by quitting smoking he could save even more money, not to mention possible health care costs. He balks at the idea of paying $2.00 for a soft drink, but doesn’t mind paying $6.00–$7.00 for a pack of cigarettes. Penny-wise and pound-foolish. Addictions skew priorities. Living Strategically , for this participant during “scary times,” appears to be a combination of PRIORITIZING those things which cannot be helped, such as pet care and personal dental care; REFUSING SACRIFICE for maintaining personal creature-comforts; and FINDING ALTERNATIVES to high costs and excessive spending. Living Strategically is an amalgam of thinking and action-oriented strategies.

There are several recommended topics for analytic memo writing throughout the qualitative study. Memos are opportunities to reflect on and write about:

A descriptive summary of the data;

How the researcher personally relates to the participants and/or the phenomenon;

The participants’ actions, reactions, and interactions;

The participants’ routines, rituals, rules, roles, and relationships;

What is surprising, intriguing, or disturbing (Sunstein & Chiseri-Strater, 2012 , p. 115);

Code choices and their operational definitions;

Emergent patterns, categories, themes, concepts, assertions, and propositions;

The possible networks and processes (links, connections, overlaps, flows) among the codes, patterns, categories, themes, concepts, assertions, and propositions;

An emergent or related existent theory;

Any problems with the study;

Any personal or ethical dilemmas with the study;

Future directions for the study;

The analytic memos generated thus far (i.e., metamemos);

Tentative answers to the study’s research questions; and

The final report for the study. (adapted from Saldaña & Omasta, 2018 , p. 54)

Since writing is analysis, analytic memos expand on the inferential meanings of the truncated codes, categories, and patterns as a transitional stage into a more coherent narrative with hopefully rich social insight.

Qualitative Data Analysis Strategy: To Code—A Different Way

The first example of coding illustrated process coding, a way of exploring general social action among humans. But sometimes a researcher works with an individual case study in which the language is unique or with someone the researcher wishes to honor by maintaining the authenticity of his or her speech in the analysis. These reasons suggest that a more participant-centered form of coding may be more appropriate.

In Vivo Coding

A second frequently applied method of coding is called in vivo coding. The root meaning of in vivo is “in that which is alive”; it refers to a code based on the actual language used by the participant (Strauss, 1987 ). The words or phrases in the data record you select as codes are those that seem to stand out as significant or summative of what is being said.

Using the same transcript of the male participant living in difficult economic times, in vivo codes are listed in the right-hand column. I recommend that in vivo codes be placed in quotation marks as a way of designating that the code is extracted directly from the data record. Note that instead of 15 codes generated from process coding, the total number of in vivo codes is 30. This is not to suggest that there should be specific numbers or ranges of codes used for particular methods. In vivo codes, however, tend to be applied more frequently to data. Again, the interviewer’s questions and prompts are not coded, just the participant’s responses:

The 30 in vivo codes are then extracted from the transcript and could be listed in the order they appear, but this time they are placed in alphabetical order as a heuristic to prepare them for analytic action and reflection:

“ALL-YOU-CAN-EAT”

“ANOTHER DING IN MY WALLET”

“BAD HABITS”

“CHEAP AND FILLING”

“COUPLE OF THOUSAND”

“DON’T REALLY NEED”

“HAVEN’T CHANGED MY HABITS”

“HIGH MAINTENANCE”

“INSURANCE IS JUST WORTHLESS”

“IT ALL ADDS UP”

“LIVED KIND OF CHEAP”

“NOT A BIG SPENDER”

“NOT AS BAD OFF”

“NOT PUTTING AS MUCH INTO SAVINGS”

“PICK UP THE TAB”

“SCARY TIMES”

“SKYROCKETED”

“SPENDING MORE”

“THE LITTLE THINGS”

“THINK TWICE”

“TWO-FOR-ONE”

Even though no systematic categorization has been conducted with the codes thus far, an analytic memo of first impressions can still be composed:

March 19, 2017 CODE CHOICES: THE EVERYDAY LANGUAGE OF ECONOMICS After eyeballing the in vivo codes list, I noticed that variants of “CHEAP” appear most often. I recall a running joke between me and a friend of mine when we were shopping for sales. We’d say, “We’re not ‘cheap,’ we’re frugal .” There’s no formal economic or business language in this transcript—no terms such as “recession” or “downsizing”—just the everyday language of one person trying to cope during “SCARY TIMES” with “ANOTHER DING IN MY WALLET.” The participant notes that he’s always “LIVED KIND OF CHEAP” and is “NOT A BIG SPENDER” and, due to his employment, “NOT AS BAD OFF” as others in the country. Yet even with his middle class status, he’s still feeling the monetary pinch, dining at inexpensive “ALL-YOU-CAN-EAT” restaurants and worried about the rising price of peanut butter, observing that he’s “NOT PUTTING AS MUCH INTO SAVINGS” as he used to. Of all the codes, “ANOTHER DING IN MY WALLET” stands out to me, particularly because on the audio recording he sounded bitter and frustrated. It seems that he’s so concerned about “THE LITTLE THINGS” because of high veterinary and dental charges. The only way to cope with a “COUPLE OF THOUSAND” dollars worth of medical expenses is to find ways of trimming the excess in everyday facets of living: “IT ALL ADDS UP.”

Like process coding, in vivo codes could be clustered into similar categories, but another simple data analytic strategy is also possible.

Qualitative Data Analysis Strategy: To Outline

To outline in QDA is to hierarchically, processually, and/or temporally assemble such things as codes, categories, themes, assertions, propositions, and concepts into a coherent, text-based display. Traditional outlining formats and content provide not only templates for writing a report but also templates for analytic organization. This principle can be found in several computer-assisted qualitative data analysis software (CAQDAS) programs through their use of such functions as “hierarchies,” “trees,” and “nodes,” for example. Basic outlining is simply a way of arranging primary, secondary, and subsecondary items into a patterned display. For example, an organized listing of things in a home might consist of the following:

Large appliances

Refrigerator

Stove-top oven

Microwave oven

Small appliances

Coffee maker

Dining room

In QDA, outlining may include descriptive nouns or topics but, depending on the study, it may also involve processes or phenomena in extended passages, such as in vivo codes or themes.

The complexity of what we learn in the field can be overwhelming, and outlining is a way of organizing and ordering that complexity so that it does not become complicated. The cut-and-paste and tab functions of a text editing page enable you to arrange and rearrange the salient items from your preliminary coded analytic work into a more streamlined flow. By no means do I suggest that the intricate messiness of life can always be organized into neatly formatted arrangements, but outlining is an analytic act that stimulates deep reflection on both the interconnectedness and the interrelationships of what we study. As an example, here are the 30 in vivo codes generated from the initial transcript analysis, arranged in such a way as to construct five major categories:

Now that the codes have been rearranged into an outline format, an analytic memo is composed to expand on the rationale and constructed meanings in progress:

March 19, 2017 NETWORKS: EMERGENT CATEGORIES The five major categories I constructed from the in vivo codes are: “SCARY TIMES,” “PRIORTY,” “ANOTHER DING IN MY WALLET,” “THE LITTLE THINGS,” and “LIVED KIND OF CHEAP.” One of the things that hit me today was that the reason he may be pinching pennies on smaller purchases is that he cannot control the larger ones he has to deal with. Perhaps the only way we can cope with or seem to have some sense of agency over major expenses is to cut back on the smaller ones that we can control. $1,000 for a dental bill? Skip lunch for a few days a week. Insulin medication to buy for a pet? Don’t buy a soft drink from a vending machine. Using this reasoning, let me try to interrelate and weave the categories together as they relate to this particular participant: During these scary economic times, he prioritizes his spending because there seems to be just one ding after another to his wallet. A general lifestyle of living cheaply and keeping an eye out for how to save money on the little things compensates for those major expenses beyond his control.

Qualitative Data Analysis Strategy: To Code—In Even More Ways

The process and in vivo coding examples thus far have demonstrated only two specific methods of 33 documented approaches (Saldaña, 2016 ). Which one(s) you choose for your analysis depends on such factors as your conceptual framework, the genre of qualitative research for your project, the types of data you collect, and so on. The following sections present four additional approaches available for coding qualitative data that you may find useful as starting points.

Descriptive Coding

Descriptive codes are primarily nouns that simply summarize the topic of a datum. This coding approach is particularly useful when you have different types of data gathered for one study, such as interview transcripts, field notes, open-ended survey responses, documents, and visual materials such as photographs. Descriptive codes not only help categorize but also index the data corpus’s basic contents for further analytic work. An example of an interview portion coded descriptively, taken from the participant living in tough economic times, follows to illustrate how the same data can be coded in multiple ways:

For initial analysis, descriptive codes are clustered into similar categories to detect such patterns as frequency (i.e., categories with the largest number of codes) and interrelationship (i.e., categories that seem to connect in some way). Keep in mind that descriptive coding should be used sparingly with interview transcript data because other coding methods will reveal richer participant dynamics.

Values Coding

Values coding identifies the values, attitudes, and beliefs of a participant, as shared by the individual and/or interpreted by the analyst. This coding method infers the “heart and mind” of an individual or group’s worldview as to what is important, perceived as true, maintained as opinion, and felt strongly. The three constructs are coded separately but are part of a complex interconnected system.

Briefly, a value (V) is what we attribute as important, be it a person, thing, or idea. An attitude (A) is the evaluative way we think and feel about ourselves, others, things, or ideas. A belief (B) is what we think and feel as true or necessary, formed from our “personal knowledge, experiences, opinions, prejudices, morals, and other interpretive perceptions of the social world” (Saldaña, 2016 , p. 132). Values coding explores intrapersonal, interpersonal, and cultural constructs, or ethos . It is an admittedly slippery task to code this way because it is sometimes difficult to discern what is a value, attitude, or belief since they are intricately interrelated. But the depth you can potentially obtain is rich. An example of values coding follows:

For analysis, categorize the codes for each of the three different constructs together (i.e., all values in one group, attitudes in a second group, and beliefs in a third group). Analytic memo writing about the patterns and possible interrelationships may reveal a more detailed and intricate worldview of the participant.

Dramaturgical Coding

Dramaturgical coding perceives life as performance and its participants as characters in a social drama. Codes are assigned to the data (i.e., a “play script”) that analyze the characters in action, reaction, and interaction. Dramaturgical coding of participants examines their objectives (OBJ) or wants, needs, and motives; the conflicts (CON) or obstacles they face as they try to achieve their objectives; the tactics (TAC) or strategies they employ to reach their objectives; their attitudes (ATT) toward others and their given circumstances; the particular emotions (EMO) they experience throughout; and their subtexts (SUB), or underlying and unspoken thoughts. The following is an example of dramaturgically coded data:

Not included in this particular interview excerpt are the emotions the participant may have experienced or talked about. His later line, “that’s another ding in my wallet,” would have been coded EMO: BITTER. A reader may not have inferred that specific emotion from seeing the line in print. But the interviewer, present during the event and listening carefully to the audio recording during transcription, noted that feeling in his tone of voice.

For analysis, group similar codes together (e.g., all objectives in one group, all conflicts in another group, all tactics in a third group) or string together chains of how participants deal with their circumstances to overcome their obstacles through tactics:

OBJ: SAVING MEAL MONEY → TAC: SKIPPING MEALS + COUPONS

Dramaturgical coding is particularly useful as preliminary work for narrative inquiry story development or arts-based research representations such as performance ethnography. The method explores how the individuals or groups manage problem solving in their daily lives.

Versus Coding

Versus (VS) coding identifies the conflicts, struggles, and power issues observed in social action, reaction, and interaction as an X VS Y code, such as MEN VS WOMEN, CONSERVATIVES VS LIBERALS, FAITH VS LOGIC, and so on. Conflicts are rarely this dichotomous; they are typically nuanced and much more complex. But humans tend to perceive these struggles with an US VS THEM mindset. The codes can range from the observable to the conceptual and can be applied to data that show humans in tension with others, themselves, or ideologies.

What follows are examples of versus codes applied to the case study participant’s descriptions of his major medical expenses:

As an initial analytic tactic, group the versus codes into one of three categories: the Stakeholders , their Perceptions and/or Actions , and the Issues at stake. Examine how the three interrelate and identify the central ideological conflict at work as an X VS Y category. Analytic memos and the final write-up can detail the nuances of the issues.

Remember that what has been profiled in this section is a broad brushstroke description of just a few basic coding processes, several of which can be compatibly mixed and matched within a single analysis (see Saldaña’s 2016   The Coding Manual for Qualitative Researchers for a complete discussion). Certainly with additional data, more in-depth analysis can occur, but coding is only one approach to extracting and constructing preliminary meanings from the data corpus. What now follows are additional methods for qualitative analysis.

Qualitative Data Analysis Strategy: To Theme

To theme in QDA is to construct summative, phenomenological meanings from data through extended passages of text. Unlike codes, which are most often single words or short phrases that symbolically represent a datum, themes are extended phrases or sentences that summarize the manifest (apparent) and latent (underlying) meanings of data (Auerbach & Silverstein, 2003 ; Boyatzis, 1998 ). Themes, intended to represent the essences and essentials of humans’ lived experiences, can also be categorized or listed in superordinate and subordinate outline formats as an analytic tactic.

Below is the interview transcript example used in the previous coding sections. (Hopefully you are not too fatigued at this point with the transcript, but it is important to know how inquiry with the same data set can be approached in several different ways.) During the investigation of the ways middle-class Americans are influenced and affected by an economic recession, the researcher noticed that participants’ stories exhibited facets of what he labeled economic intelligence , or EI (based on the formerly developed theories of Howard Gardner’s multiple intelligences and Daniel Goleman’s emotional intelligence). Notice how theming interprets what is happening through the use of two distinct phrases—ECONOMIC INTELLIGENCE IS (i.e., manifest or apparent meanings) and ECONOMIC INTELLIGENCE MEANS (i.e., latent or underlying meanings):

Unlike the 15 process codes and 30 in vivo codes in the previous examples, there are now 14 themes to work with. They could be listed in the order they appear, but one initial heuristic is to group them separately by “is” and “means” statements to detect any possible patterns (discussed later):

EI IS TAKING ADVANTAGE OF UNEXPECTED OPPORTUNITY

EI IS BUYING CHEAP

EI IS SAVING A FEW DOLLARS NOW AND THEN

EI IS SETTING PRIORITIES

EI IS FINDING CHEAPER FORMS OF ENTERTAINMENT

EI IS NOTICING PERSONAL AND NATIONAL ECONOMIC TRENDS

EI IS TAKING CARE OF ONE’S OWN HEALTH

EI MEANS THINKING BEFORE YOU ACT

EI MEANS SACRIFICE

EI MEANS KNOWING YOUR FLAWS

EI MEANS LIVING AN INEXPENSIVE LIFESTYLE

EI MEANS YOU CANNOT CONTROL EVERYTHING

EI MEANS KNOWING YOUR LUCK

There are several ways to categorize the themes as preparation for analytic memo writing. The first is to arrange them in outline format with superordinate and subordinate levels, based on how the themes seem to take organizational shape and structure. Simply cutting and pasting the themes in multiple arrangements on a text editing page eventually develops a sense of order to them. For example:

A second approach is to categorize the themes into similar clusters and to develop different category labels or theoretical constructs . A theoretical construct is an abstraction that transforms the central phenomenon’s themes into broader applications but can still use “is” and “means” as prompts to capture the bigger picture at work:

Theoretical Construct 1: EI Means Knowing the Unfortunate Present

Supporting Themes:

Theoretical Construct 2: EI Is Cultivating a Small Fortune

Theoretical Construct 3: EI Means a Fortunate Future

What follows is an analytic memo generated from the cut-and-paste arrangement of themes into “is” and “means” statements, into an outline, and into theoretical constructs:

March 19, 2017 EMERGENT THEMES: FORTUNE/FORTUNATELY/UNFORTUNATELY I first reorganized the themes by listing them in two groups: “is” and “means.” The “is” statements seemed to contain positive actions and constructive strategies for economic intelligence. The “means” statements held primarily a sense of caution and restriction with a touch of negativity thrown in. The first outline with two major themes, LIVING AN INEXPENSIVE LIFESTYLE and YOU CANNOT CONTROL EVERYTHING also had this same tone. This reminded me of the old children’s picture book, Fortunately/Unfortunately , and the themes of “fortune” as a motif for the three theoretical constructs came to mind. Knowing the Unfortunate Present means knowing what’s (most) important and what’s (mostly) uncontrollable in one’s personal economic life. Cultivating a Small Fortune consists of those small money-saving actions that, over time, become part of one’s lifestyle. A Fortunate Future consists of heightened awareness of trends and opportunities at micro and macro levels, with the understanding that health matters can idiosyncratically affect one’s fortune. These three constructs comprise this particular individual’s EI—economic intelligence.

Again, keep in mind that the examples for coding and theming were from one small interview transcript excerpt. The number of codes and their categorization would increase, given a longer interview and/or multiple interviews to analyze. But the same basic principles apply: codes and themes relegated into patterned and categorized forms are heuristics—stimuli for good thinking through the analytic memo-writing process on how everything plausibly interrelates. Methodologists vary in the number of recommended final categories that result from analysis, ranging anywhere from three to seven, with traditional grounded theorists prescribing one central or core category from coded work.

Qualitative Data Analysis Strategy: To Assert

To assert in QDA is to put forward statements that summarize particular fieldwork and analytic observations that the researcher believes credibly represent and transcend the experiences. Educational anthropologist Frederick Erickson ( 1986 ) wrote a significant and influential chapter on qualitative methods that outlined heuristics for assertion development . Assertions are declarative statements of summative synthesis, supported by confirming evidence from the data and revised when disconfirming evidence or discrepant cases require modification of the assertions. These summative statements are generated from an interpretive review of the data corpus and then supported and illustrated through narrative vignettes—reconstructed stories from field notes, interview transcripts, or other data sources that provide a vivid profile as part of the evidentiary warrant.

Coding or theming data can certainly precede assertion development as a way of gaining intimate familiarity with the data, but Erickson’s ( 1986 ) methods are a more admittedly intuitive yet systematic heuristic for analysis. Erickson promotes analytic induction and exploration of and inferences about the data, based on an examination of the evidence and an accumulation of knowledge. The goal is not to look for “proof” to support the assertions, but to look for plausibility of inference-laden observations about the local and particular social world under investigation.

Assertion development is the writing of general statements, plus subordinate yet related ones called subassertions and a major statement called a key assertion that represents the totality of the data. One also looks for key linkages between them, meaning that the key assertion links to its related assertions, which then link to their respective subassertions. Subassertions can include particulars about any discrepant related cases or specify components of their parent assertions.

Excerpts from the interview transcript of our case study will be used to illustrate assertion development at work. By now, you should be quite familiar with the contents, so I will proceed directly to the analytic example. First, there is a series of thematically related statements the participant makes:

“Buy one package of chicken, get the second one free. Now that was a bargain. And I got some.”

“With Sweet Tomatoes I get those coupons for a few bucks off for lunch, so that really helps.”

“I don’t go to movies anymore. I rent DVDs from Netflix or Redbox or watch movies online—so much cheaper than paying over ten or twelve bucks for a movie ticket.”

Assertions can be categorized into low-level and high-level inferences . Low-level inferences address and summarize what is happening within the particulars of the case or field site—the micro . High-level inferences extend beyond the particulars to speculate on what it means in the more general social scheme of things—the meso or macro . A reasonable low-level assertion about the three statements above collectively might read, The participant finds several small ways to save money during a difficult economic period . A high-level inference that transcends the case to the meso level might read, Selected businesses provide alternatives and opportunities to buy products and services at reduced rates during a recession to maintain consumer spending.

Assertions are instantiated (i.e., supported) by concrete instances of action or participant testimony, whose patterns lead to more general description outside the specific field site. The author’s interpretive commentary can be interspersed throughout the report, but the assertions should be supported with the evidentiary warrant . A few assertions and subassertions based on the case interview transcript might read as follows (and notice how high-level assertions serve as the paragraphs’ topic sentences):

Selected businesses provide alternatives and opportunities to buy products and services at reduced rates during a recession to maintain consumer spending. Restaurants, for example, need to find ways during difficult economic periods when potential customers may be opting to eat inexpensively at home rather than spending more money by dining out. Special offers can motivate cash-strapped clientele to patronize restaurants more frequently. An adult male dealing with such major expenses as underinsured dental care offers: “With Sweet Tomatoes I get those coupons for a few bucks off for lunch, so that really helps.” The film and video industries also seem to be suffering from a double-whammy during the current recession: less consumer spending on higher-priced entertainment, resulting in a reduced rate of movie theater attendance (recently 39 percent of the American population, according to a CNN report); coupled with a media technology and business revolution that provides consumers less costly alternatives through video rentals and Internet viewing: “I don’t go to movies anymore. I rent DVDs from Netflix or Redbox or watch movies online—so much cheaper than paying over ten or twelve bucks for a movie ticket.”

To clarify terminology, any assertion that has an if–then or predictive structure to it is called a proposition since it proposes a conditional event. For example, this assertion is also a proposition: “Special offers can motivate cash-strapped clientele to patronize restaurants more frequently.” Propositions are the building blocks of hypothesis testing in the field and for later theory construction. Research not only documents human action but also can sometimes formulate statements that predict it. This provides a transferable and generalizable quality in our findings to other comparable settings and contexts. And to clarify terminology further, all propositions are assertions, but not all assertions are propositions.

Particularizability —the search for specific and unique dimensions of action at a site and/or the specific and unique perspectives of an individual participant—is not intended to filter out trivial excess but to magnify the salient characteristics of local meaning. Although generalizable knowledge is difficult to formulate in qualitative inquiry since each naturalistic setting will contain its own unique set of social and cultural conditions, there will be some aspects of social action that are plausibly universal or “generic” across settings and perhaps even across time.

To work toward this, Erickson advocates that the interpretive researcher look for “concrete universals” by studying actions at a particular site in detail and then comparing those actions to actions at other sites that have also been studied in detail. The exhibit or display of these generalizable features is to provide a synoptic representation, or a view of the whole. What the researcher attempts to uncover is what is both particular and general at the site of interest, preferably from the perspective of the participants. It is from the detailed analysis of actions at a specific site that these universals can be concretely discerned, rather than abstractly constructed as in grounded theory.

In sum, assertion development is a qualitative data analytic strategy that relies on the researcher’s intense review of interview transcripts, field notes, documents, and other data to inductively formulate, with reasonable certainty, composite statements that credibly summarize and interpret participant actions and meanings and their possible representation of and transfer into broader social contexts and issues.

Qualitative Data Analysis Strategy: To Display

To display in QDA is to visually present the processes and dynamics of human or conceptual action represented in the data. Qualitative researchers use not only language but also illustrations to both analyze and display the phenomena and processes at work in the data. Tables, charts, matrices, flow diagrams, and other models and graphics help both you and your readers cognitively and conceptually grasp the essence and essentials of your findings. As you have seen thus far, even simple outlining of codes, categories, and themes is one visual tactic for organizing the scope of the data. Rich text, font, and format features such as italicizing, bolding, capitalizing, indenting, and bullet pointing provide simple emphasis to selected words and phrases within the longer narrative.

Think display was a phrase coined by methodologists Miles and Huberman ( 1994 ) to encourage the researcher to think visually as data were collected and analyzed. The magnitude of text can be essentialized into graphics for at-a-glance review. Bins in various shapes and lines of various thicknesses, along with arrows suggesting pathways and direction, render the study a portrait of action. Bins can include the names of codes, categories, concepts, processes, key participants, and/or groups.

As a simple example, Figure 29.1 illustrates the three categories’ interrelationship derived from process coding. It displays what could be the apex of this interaction, LIVING STRATEGICALLY, and its connections to THINKING STRATEGICALLY, which influences and affects SPENDING STRATEGICALLY.

Three categories’ interrelationship derived from process coding.

Figure 29.2 represents a slightly more complex (if not playful) model, based on the five major in vivo codes/categories generated from analysis. The graphic is used as a way of initially exploring the interrelationship and flow from one category to another. The use of different font styles, font sizes, and line and arrow thicknesses is intended to suggest the visual qualities of the participant’s language and his dilemmas—a way of heightening in vivo coding even further.

In vivo categories in rich text display

Accompanying graphics are not always necessary for a qualitative report. They can be very helpful for the researcher during the analytic stage as a heuristic for exploring how major ideas interrelate, but illustrations are generally included in published work when they will help supplement and clarify complex processes for readers. Photographs of the field setting or the participants (and only with their written permission) also provide evidentiary reality to the write-up and help your readers get a sense of being there.

Qualitative Data Analysis Strategy: To Narrate

To narrate in QDA is to create an evocative literary representation and presentation of the data in the form of creative nonfiction. All research reports are stories of one kind or another. But there is yet another approach to QDA that intentionally documents the research experience as story, in its traditional literary sense. Narrative inquiry serves to plot and story-line the participant’s experiences into what might be initially perceived as a fictional short story or novel. But the story is carefully crafted and creatively written to provide readers with an almost omniscient perspective about the participants’ worldview. The transformation of the corpus from database to creative nonfiction ranges from systematic transcript analysis to open-ended literary composition. The narrative, however, should be solidly grounded in and emerge from the data as a plausible rendering of social life.

The following is a narrative vignette based on interview transcript selections from the participant living through tough economic times:

Jack stood in front of the soft drink vending machine at work and looked almost worriedly at the selections. With both hands in his pants pockets, his fingers jingled the few coins he had inside them as he contemplated whether he could afford the purchase. Two dollars for a twenty-ounce bottle of Diet Coke. Two dollars. “I can practically get a two-liter bottle for that same price at the grocery store,” he thought. Then Jack remembered the upcoming dental surgery he needed—that would cost one thousand dollars—and the bottle of insulin and syringes he needed to buy for his diabetic, high maintenance cat—almost two hundred dollars. He sighed, took his hands out of his pockets, and walked away from the vending machine. He was skipping lunch that day anyway so he could stock up on dinner later at the cheap-but-filling all-you-can-eat Chinese buffet. He could get his Diet Coke there.

Narrative inquiry representations, like literature, vary in tone, style, and point of view. The common goal, however, is to create an evocative portrait of participants through the aesthetic power of literary form. A story does not always have to have a moral explicitly stated by its author. The reader reflects on personal meanings derived from the piece and how the specific tale relates to one’s self and the social world.

Qualitative Data Analysis Strategy: To Poeticize

To poeticize in QDA is to create an evocative literary representation and presentation of the data in poetic form. One approach to analyzing or documenting analytic findings is to strategically truncate interview transcripts, field notes, and other pertinent data into poetic structures. Like coding, poetic constructions capture the essence and essentials of data in a creative, evocative way. The elegance of the format attests to the power of carefully chosen language to represent and convey complex human experience.

In vivo codes (codes based on the actual words used by participants themselves) can provide imagery, symbols, and metaphors for rich category, theme, concept, and assertion development, in addition to evocative content for arts-based interpretations of the data. Poetic inquiry takes note of what words and phrases seem to stand out from the data corpus as rich material for reinterpretation. Using some of the participant’s own language from the interview transcript illustrated previously, a poetic reconstruction or “found poetry” might read as follows:

Scary Times Scary times … spending more   (another ding in my wallet) a couple of thousand   (another ding in my wallet) insurance is just worthless   (another ding in my wallet) pick up the tab   (another ding in my wallet) not putting as much into savings   (another ding in my wallet) It all adds up. Think twice:   don’t really need    skip Think twice, think cheap:   coupons   bargains   two-for-one    free Think twice, think cheaper:   stock up   all-you-can-eat    (cheap—and filling) It all adds up.

Anna Deavere Smith, a verbatim theatre performer, attests that people speak in forms of “organic poetry” in everyday life. Thus, in vivo codes can provide core material for poetic representation and presentation of lived experiences, potentially transforming the routine and mundane into the epic. Some researchers also find the genre of poetry to be the most effective way to compose original work that reflects their own fieldwork experiences and autoethnographic stories.

Qualitative Data Analysis Strategy: To Compute

To compute in QDA is to employ specialized software programs for qualitative data management and analysis. The acronym for computer-assisted qualitative data analysis software is CAQDAS. There are diverse opinions among practitioners in the field about the utility of such specialized programs for qualitative data management and analysis. The software, unlike statistical computation, does not actually analyze data for you at higher conceptual levels. These CAQDAS software packages serve primarily as a repository for your data (both textual and visual) that enables you to code them, and they can perform such functions as calculating the number of times a particular word or phrase appears in the data corpus (a particularly useful function for content analysis) and can display selected facets after coding, such as possible interrelationships. Basic software such as Microsoft Word and Excel provides utilities that can store and, with some preformatting and strategic entry, organize qualitative data to enable the researcher’s analytic review. The following Internet addresses are listed to help in exploring selected CAQDAS packages and obtaining demonstration/trial software; video tutorials are available on the companies’ websites and on YouTube:

ATLAS.ti: http://www.atlasti.com

Dedoose: http://www.dedoose.com

HyperRESEARCH: http://www.researchware.com

MAXQDA: http://www.maxqda.com

NVivo: http://www.qsrinternational.com

QDA Miner: http://www.provalisresearch.com

Quirkos: http://www.quirkos.com

Transana: http://www.transana.com

V-Note: http://www.v-note.org

Some qualitative researchers attest that the software is indispensable for qualitative data management, especially for large-scale studies. Others feel that the learning curve of most CAQDAS programs is too lengthy to be of pragmatic value, especially for small-scale studies. From my own experience, if you have an aptitude for picking up quickly on the scripts and syntax of software programs, explore one or more of the packages listed. If you are a novice to qualitative research, though, I recommend working manually or “by hand” for your first project so you can focus exclusively on the data and not on the software.

Qualitative Data Analysis Strategy: To Verify

To verify in QDA is to administer an audit of “quality control” to your analysis. After your data analysis and the development of key findings, you may be thinking to yourself, “Did I get it right?” “Did I learn anything new?” Reliability and validity are terms and constructs of the positivist quantitative paradigm that refer to the replicability and accuracy of measures. But in the qualitative paradigm, other constructs are more appropriate.

Credibility and trustworthiness (Lincoln & Guba, 1985 ) are two factors to consider when collecting and analyzing the data and presenting your findings. In our qualitative research projects, we must present a convincing story to our audiences that we “got it right” methodologically. In other words, the amount of time we spent in the field, the number of participants we interviewed, the analytic methods we used, the thinking processes evident to reach our conclusions, and so on should be “just right” to assure the reader that we have conducted our jobs soundly. But remember that we can never conclusively prove something; we can only, at best, convincingly suggest. Research is an act of persuasion.

Credibility in a qualitative research report can be established in several ways. First, citing the key writers of related works in your literature review is essential. Seasoned researchers will sometimes assess whether a novice has “done her homework” by reviewing the bibliography or references. You need not list everything that seminal writers have published about a topic, but their names should appear at least once as evidence that you know the field’s key figures and their work.

Credibility can also be established by specifying the particular data analysis methods you employed (e.g., “Interview transcripts were taken through two cycles of process coding, resulting in three primary categories”), through corroboration of data analysis with the participants themselves (e.g., “I asked my participants to read and respond to a draft of this report for their confirmation of accuracy and recommendations for revision”), or through your description of how data and findings were substantiated (e.g., “Data sources included interview transcripts, participant observation field notes, and participant response journals to gather multiple perspectives about the phenomenon”).

Data scientist W. Edwards Deming is attributed with offering this cautionary advice about making a convincing argument: “Without data, you’re just another person with an opinion.” Thus, researchers can also support their findings with relevant, specific evidence by quoting participants directly and/or including field note excerpts from the data corpus. These serve both as illustrative examples for readers and to present more credible testimony of what happened in the field.

Trustworthiness, or providing credibility to the writing, is when we inform the reader of our research processes. Some make the case by stating the duration of fieldwork (e.g., “Forty-five clock hours were spent in the field”; “The study extended over a 10-month period”). Others put forth the amounts of data they gathered (e.g., “Sixteen individuals were interviewed”; “My field notes totaled 157 pages”). Sometimes trustworthiness is established when we are up front or confessional with the analytic or ethical dilemmas we encountered (e.g., “It was difficult to watch the participant’s teaching effectiveness erode during fieldwork”; “Analysis was stalled until I recoded the entire data corpus with a new perspective”).

The bottom line is that credibility and trustworthiness are matters of researcher honesty and integrity . Anyone can write that he worked ethically, rigorously, and reflexively, but only the writer will ever know the truth. There is no shame if something goes wrong with your research. In fact, it is more than likely the rule, not the exception. Work and write transparently to achieve credibility and trustworthiness with your readers.

The length of this chapter does not enable me to expand on other QDA strategies such as to conceptualize, theorize, and write. Yet there are even more subtle thinking strategies to employ throughout the research enterprise, such as to synthesize, problematize, and create. Each researcher has his or her own ways of working, and deep reflexivity (another strategy) on your own methodology and methods as a qualitative inquirer throughout fieldwork and writing provides you with metacognitive awareness of data analysis processes and possibilities.

Data analysis is one of the most elusive practices in qualitative research, perhaps because it is a backstage, behind-the-scenes, in-your-head enterprise. It is not that there are no models to follow. It is just that each project is contextual and case specific. The unique data you collect from your unique research design must be approached with your unique analytic signature. It truly is a learning-by-doing process, so accept that and leave yourself open to discovery and insight as you carefully scrutinize the data corpus for patterns, categories, themes, concepts, assertions, propositions, and possibly new theories through strategic analysis.

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Home » Qualitative Data – Types, Methods and Examples

Qualitative Data – Types, Methods and Examples

Table of Contents

Qualitative Data

Definition:

Qualitative data is a type of data that is collected and analyzed in a non-numerical form, such as words, images, or observations. It is generally used to gain an in-depth understanding of complex phenomena, such as human behavior, attitudes, and beliefs.

Types of Qualitative Data

There are various types of qualitative data that can be collected and analyzed, including:

• Interviews : These involve in-depth, face-to-face conversations with individuals or groups to gather their perspectives, experiences, and opinions on a particular topic.
• Focus Groups: These are group discussions where a facilitator leads a discussion on a specific topic, allowing participants to share their views and experiences.
• Observations : These involve observing and recording the behavior and interactions of individuals or groups in a particular setting.
• Case Studies: These involve in-depth analysis of a particular individual, group, or organization, usually over an extended period.
• Document Analysis : This involves examining written or recorded materials, such as newspaper articles, diaries, or public records, to gain insight into a particular topic.
• Visual Data : This involves analyzing images or videos to understand people’s experiences or perspectives on a particular topic.
• Online Data: This involves analyzing data collected from social media platforms, forums, or online communities to understand people’s views and opinions on a particular topic.

Qualitative Data Formats

Qualitative data can be collected and presented in various formats. Some common formats include:

• Textual data: This includes written or transcribed data from interviews, focus groups, or observations. It can be analyzed using various techniques such as thematic analysis or content analysis.
• Audio data: This includes recordings of interviews or focus groups, which can be transcribed and analyzed using software such as NVivo.
• Visual data: This includes photographs, videos, or drawings, which can be analyzed using techniques such as visual analysis or semiotics.
• Mixed media data : This includes data collected in different formats, such as audio and text. This can be analyzed using mixed methods research, which combines both qualitative and quantitative research methods.
• Field notes: These are notes taken by researchers during observations, which can include descriptions of the setting, behaviors, and interactions of participants.

Qualitative Data Analysis Methods

Qualitative data analysis refers to the process of systematically analyzing and interpreting qualitative data to identify patterns, themes, and relationships. Here are some common methods of analyzing qualitative data:

• Thematic analysis: This involves identifying and analyzing patterns or themes within the data. It involves coding the data into themes and subthemes and organizing them into a coherent narrative.
• Content analysis: This involves analyzing the content of the data, such as the words, phrases, or images used. It involves identifying patterns and themes in the data and examining the relationships between them.
• Discourse analysis: This involves analyzing the language and communication used in the data, such as the meaning behind certain words or phrases. It involves examining how the language constructs and shapes social reality.
• Grounded theory: This involves developing a theory or framework based on the data. It involves identifying patterns and themes in the data and using them to develop a theory that explains the phenomenon being studied.
• Narrative analysis : This involves analyzing the stories and narratives present in the data. It involves examining how the stories are constructed and how they contribute to the overall understanding of the phenomenon being studied.
• Ethnographic analysis : This involves analyzing the culture and social practices present in the data. It involves examining how the cultural and social practices contribute to the phenomenon being studied.

Qualitative Data Collection Guide

Here are some steps to guide the collection of qualitative data:

• Define the research question : Start by clearly defining the research question that you want to answer. This will guide the selection of data collection methods and help to ensure that the data collected is relevant to the research question.
• Choose data collection methods : Select the most appropriate data collection methods based on the research question, the research design, and the resources available. Common methods include interviews, focus groups, observations, document analysis, and participatory research.
• Develop a data collection plan : Develop a plan for data collection that outlines the specific procedures, timelines, and resources needed for each data collection method. This plan should include details such as how to recruit participants, how to conduct interviews or focus groups, and how to record and store data.
• Obtain ethical approval : Obtain ethical approval from an institutional review board or ethics committee before beginning data collection. This is particularly important when working with human participants to ensure that their rights and interests are protected.
• Recruit participants: Recruit participants based on the research question and the data collection methods chosen. This may involve purposive sampling, snowball sampling, or random sampling.
• Collect data: Collect data using the chosen data collection methods. This may involve conducting interviews, facilitating focus groups, observing participants, or analyzing documents.
• Transcribe and store data : Transcribe and store the data in a secure location. This may involve transcribing audio or video recordings, organizing field notes, or scanning documents.
• Analyze data: Analyze the data using appropriate qualitative data analysis methods, such as thematic analysis or content analysis.
• I nterpret findings : Interpret the findings of the data analysis in the context of the research question and the relevant literature. This may involve developing new theories or frameworks, or validating existing ones.
• Communicate results: Communicate the results of the research in a clear and concise manner, using appropriate language and visual aids where necessary. This may involve writing a report, presenting at a conference, or publishing in a peer-reviewed journal.

Qualitative Data Examples

Some examples of qualitative data in different fields are as follows:

• Sociology : In sociology, qualitative data is used to study social phenomena such as culture, norms, and social relationships. For example, a researcher might conduct interviews with members of a community to understand their beliefs and practices.
• Psychology : In psychology, qualitative data is used to study human behavior, emotions, and attitudes. For example, a researcher might conduct a focus group to explore how individuals with anxiety cope with their symptoms.
• Education : In education, qualitative data is used to study learning processes and educational outcomes. For example, a researcher might conduct observations in a classroom to understand how students interact with each other and with their teacher.
• Marketing : In marketing, qualitative data is used to understand consumer behavior and preferences. For example, a researcher might conduct in-depth interviews with customers to understand their purchasing decisions.
• Anthropology : In anthropology, qualitative data is used to study human cultures and societies. For example, a researcher might conduct participant observation in a remote community to understand their customs and traditions.
• Health Sciences: In health sciences, qualitative data is used to study patient experiences, beliefs, and preferences. For example, a researcher might conduct interviews with cancer patients to understand how they cope with their illness.

Application of Qualitative Data

Qualitative data is used in a variety of fields and has numerous applications. Here are some common applications of qualitative data:

• Exploratory research: Qualitative data is often used in exploratory research to understand a new or unfamiliar topic. Researchers use qualitative data to generate hypotheses and develop a deeper understanding of the research question.
• Evaluation: Qualitative data is often used to evaluate programs or interventions. Researchers use qualitative data to understand the impact of a program or intervention on the people who participate in it.
• Needs assessment: Qualitative data is often used in needs assessments to understand the needs of a specific population. Researchers use qualitative data to identify the most pressing needs of the population and develop strategies to address those needs.
• Case studies: Qualitative data is often used in case studies to understand a particular case in detail. Researchers use qualitative data to understand the context, experiences, and perspectives of the people involved in the case.
• Market research: Qualitative data is often used in market research to understand consumer behavior and preferences. Researchers use qualitative data to gain insights into consumer attitudes, opinions, and motivations.
• Social and cultural research : Qualitative data is often used in social and cultural research to understand social phenomena such as culture, norms, and social relationships. Researchers use qualitative data to understand the experiences, beliefs, and practices of individuals and communities.

Purpose of Qualitative Data

The purpose of qualitative data is to gain a deeper understanding of social phenomena that cannot be captured by numerical or quantitative data. Qualitative data is collected through methods such as observation, interviews, and focus groups, and it provides descriptive information that can shed light on people’s experiences, beliefs, attitudes, and behaviors.

Qualitative data serves several purposes, including:

• Generating hypotheses: Qualitative data can be used to generate hypotheses about social phenomena that can be further tested with quantitative data.
• Providing context : Qualitative data provides a rich and detailed context for understanding social phenomena that cannot be captured by numerical data alone.
• Exploring complex phenomena : Qualitative data can be used to explore complex phenomena such as culture, social relationships, and the experiences of marginalized groups.
• Evaluating programs and intervention s: Qualitative data can be used to evaluate the impact of programs and interventions on the people who participate in them.
• Enhancing understanding: Qualitative data can be used to enhance understanding of the experiences, beliefs, and attitudes of individuals and communities, which can inform policy and practice.

When to use Qualitative Data

Qualitative data is appropriate when the research question requires an in-depth understanding of complex social phenomena that cannot be captured by numerical or quantitative data.

Here are some situations when qualitative data is appropriate:

• Exploratory research : Qualitative data is often used in exploratory research to generate hypotheses and develop a deeper understanding of a research question.
• Understanding social phenomena : Qualitative data is appropriate when the research question requires an in-depth understanding of social phenomena such as culture, social relationships, and experiences of marginalized groups.
• Program evaluation: Qualitative data is often used in program evaluation to understand the impact of a program on the people who participate in it.
• Needs assessment: Qualitative data is often used in needs assessments to understand the needs of a specific population.
• Market research: Qualitative data is often used in market research to understand consumer behavior and preferences.
• Case studies: Qualitative data is often used in case studies to understand a particular case in detail.

Characteristics of Qualitative Data

Here are some characteristics of qualitative data:

• Descriptive : Qualitative data provides a rich and detailed description of the social phenomena under investigation.
• Contextual : Qualitative data is collected in the context in which the social phenomena occur, which allows for a deeper understanding of the phenomena.
• Subjective : Qualitative data reflects the subjective experiences, beliefs, attitudes, and behaviors of the individuals and communities under investigation.
• Flexible : Qualitative data collection methods are flexible and can be adapted to the specific needs of the research question.
• Emergent : Qualitative data analysis is often an iterative process, where new themes and patterns emerge as the data is analyzed.
• Interpretive : Qualitative data analysis involves interpretation of the data, which requires the researcher to be reflexive and aware of their own biases and assumptions.
• Non-standardized: Qualitative data collection methods are often non-standardized, which means that the data is not collected in a standardized or uniform way.

Advantages of Qualitative Data

Some advantages of qualitative data are as follows:

• Richness : Qualitative data provides a rich and detailed description of the social phenomena under investigation, allowing for a deeper understanding of the phenomena.
• Flexibility : Qualitative data collection methods are flexible and can be adapted to the specific needs of the research question, allowing for a more nuanced exploration of social phenomena.
• Contextualization : Qualitative data is collected in the context in which the social phenomena occur, which allows for a deeper understanding of the phenomena and their cultural and social context.
• Subjectivity : Qualitative data reflects the subjective experiences, beliefs, attitudes, and behaviors of the individuals and communities under investigation, allowing for a more holistic understanding of the phenomena.
• New insights : Qualitative data can generate new insights and hypotheses that can be further tested with quantitative data.
• Participant voice : Qualitative data collection methods often involve direct participation by the individuals and communities under investigation, allowing for their voices to be heard.
• Ethical considerations: Qualitative data collection methods often prioritize ethical considerations such as informed consent, confidentiality, and respect for the autonomy of the participants.

Limitations of Qualitative Data

Here are some limitations of qualitative data:

• Subjectivity : Qualitative data is subjective, and the interpretation of the data depends on the researcher’s own biases, assumptions, and perspectives.
• Small sample size: Qualitative data collection methods often involve a small sample size, which limits the generalizability of the findings.
• Time-consuming: Qualitative data collection and analysis can be time-consuming, as it requires in-depth engagement with the data and often involves iterative processes.
• Limited statistical analysis: Qualitative data is often not suitable for statistical analysis, which limits the ability to draw quantitative conclusions from the data.
• Limited comparability: Qualitative data collection methods are often non-standardized, which makes it difficult to compare findings across different studies or contexts.
• Social desirability bias : Qualitative data collection methods often rely on self-reporting by the participants, which can be influenced by social desirability bias.
• Researcher bias: The researcher’s own biases, assumptions, and perspectives can influence the data collection and analysis, which can limit the objectivity of the findings.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Qualitative Data Analysis

Qualitative data refers to non-numeric information such as interview transcripts, notes, video and audio recordings, images and text documents. Qualitative data analysis can be divided into the following five categories:

1. Content analysis . This refers to the process of categorizing verbal or behavioural data to classify, summarize and tabulate the data.

2. Narrative analysis . This method involves the reformulation of stories presented by respondents taking into account context of each case and different experiences of each respondent. In other words, narrative analysis is the revision of primary qualitative data by researcher.

3. Discourse analysis . A method of analysis of naturally occurring talk and all types of written text.

4. Framework analysis . This is more advanced method that consists of several stages such as familiarization, identifying a thematic framework, coding, charting, mapping and interpretation.

5. Grounded theory . This method of qualitative data analysis starts with an analysis of a single case to formulate a theory. Then, additional cases are examined to see if they contribute to the theory.

Qualitative data analysis can be conducted through the following three steps:

Step 1: Developing and Applying Codes . Coding can be explained as categorization of data. A ‘code’ can be a word or a short phrase that represents a theme or an idea. All codes need to be assigned meaningful titles. A wide range of non-quantifiable elements such as events, behaviours, activities, meanings etc. can be coded.

There are three types of coding:

• Open coding . The initial organization of raw data to try to make sense of it.
• Axial coding . Interconnecting and linking the categories of codes.
• Selective coding . Formulating the story through connecting the categories.

Coding can be done manually or using qualitative data analysis software such as

 NVivo,  Atlas ti 6.0,  HyperRESEARCH 2.8,  Max QDA and others.

When using manual coding you can use folders, filing cabinets, wallets etc. to gather together materials that are examples of similar themes or analytic ideas. Manual method of coding in qualitative data analysis is rightly considered as labour-intensive, time-consuming and outdated.

In computer-based coding, on the other hand, physical files and cabinets are replaced with computer based directories and files. When choosing software for qualitative data analysis you need to consider a wide range of factors such as the type and amount of data you need to analyse, time required to master the software and cost considerations.

Moreover, it is important to get confirmation from your dissertation supervisor prior to application of any specific qualitative data analysis software.

The following table contains examples of research titles, elements to be coded and identification of relevant codes:

 Qualitative data coding

Step 2: Identifying themes, patterns and relationships . Unlike quantitative methods , in qualitative data analysis there are no universally applicable techniques that can be applied to generate findings. Analytical and critical thinking skills of researcher plays significant role in data analysis in qualitative studies. Therefore, no qualitative study can be repeated to generate the same results.

Nevertheless, there is a set of techniques that you can use to identify common themes, patterns and relationships within responses of sample group members in relation to codes that have been specified in the previous stage.

Specifically, the most popular and effective methods of qualitative data interpretation include the following:

• Word and phrase repetitions – scanning primary data for words and phrases most commonly used by respondents, as well as, words and phrases used with unusual emotions;
• Primary and secondary data comparisons – comparing the findings of interview/focus group/observation/any other qualitative data collection method with the findings of literature review and discussing differences between them;
• Search for missing information – discussions about which aspects of the issue was not mentioned by respondents, although you expected them to be mentioned;
• Metaphors and analogues – comparing primary research findings to phenomena from a different area and discussing similarities and differences.

Step 3: Summarizing the data . At this last stage you need to link research findings to hypotheses or research aim and objectives. When writing data analysis chapter, you can use noteworthy quotations from the transcript in order to highlight major themes within findings and possible contradictions.

It is important to note that the process of qualitative data analysis described above is general and different types of qualitative studies may require slightly different methods of data analysis.

My  e-book,  The Ultimate Guide to Writing a Dissertation in Business Studies: a step by step approach  contains a detailed, yet simple explanation of qualitative data analysis methods . The e-book explains all stages of the research process starting from the selection of the research area to writing personal reflection. Important elements of dissertations such as research philosophy, research approach, research design, methods of data collection and data analysis are explained in simple words. John Dudovskiy

Want to create or adapt books like this? Learn more about how Pressbooks supports open publishing practices.

Chapter 10: Qualitative Data Collection & Analysis Methods

10.5 Analysis of Qualitative Interview Data

Analysis of qualitative interview data typically begins with a set of transcripts of the interviews conducted. Obtaining said transcripts requires either having taken exceptionally good notes during an interview or, preferably, recorded the interview and then transcribed it. To transcribe an interview means to create a complete, written copy of the recorded interview by playing the recording back and typing in each word that is spoken on the recording, noting who spoke which words. In general, it is best to aim for a verbatim transcription, i.e., one that reports word for word exactly what was said in the recorded interview. If possible, it is also best to include nonverbal responses in the written transcription of an interview (if the interview is completed face-to-face, or some other form of visual contact is maintained, such as with Skype). Gestures made by respondents should be noted, as should the tone of voice and notes about when, where, and how spoken words may have been emphasized by respondents.

If you have the time, it is best to transcribe your interviews yourself. If the researcher who conducted the interviews transcribes them herself, that person will also be able to record associated nonverbal behaviors and interactions that may be relevant to analysis but that could not be picked up by audio recording. Interviewees may roll their eyes, wipe tears from their face, and even make obscene gestures that speak volumes about their feelings; however, such non-verbal gestures cannot be recorded, and being able to remember and record in writing these details as it relates to the transcribing of interviews is invaluable.

Overall, the goal of analysis is to reach some inferences, lessons, or conclusions by condensing large amounts of data into relatively smaller, more manageable bits of understandable information. Analysis of qualitative interview data often works inductively (Glaser & Strauss, 1967; Patton, 2001). To move from the specific observations an interviewer collects to identifying patterns across those observations, qualitative interviewers will often begin by reading through transcripts of their interviews and trying to identify codes. A code is a shorthand representation of some more complex set of issues or ideas. The process of identifying codes in one’s qualitative data is often referred to as coding . Coding involves identifying themes across interview data by reading and re-reading (and re-reading again) interview transcripts, until the researcher has a clear idea about what sorts of themes come up across the interviews. Coding helps to achieve the goal of data management and data reduction (Palys & Atchison, 2014, p. 304).

Coding can be inductive or deductive. Deductive coding is the approach used by research analysts who have a well-specified or pre-defined set of interests (Palys & Atchison, 2014, P. 304). The process of deductive coding begins with the analyst utilizing those specific or pre-defined interests to identify “relevant” passages, quotes, images, scenes, etc., to develop a set of preliminary codes (often referred to as descriptive coding ). From there, the analyst elaborates on these preliminary codes, making finer distinctions within each coding category (known as interpretative coding ). Pattern coding is another step an analyst might take as different associations become apparent. For example, if you are studying at-risk behaviours in youth, and you discover that the various behaviours have different characteristics and meanings depending upon the social context (e.g., school, family, work) in which the various behaviours occur, you have identified a pattern (Palys & Atchison, 2014, p. 304).

In contrast, inductive coding begins with the identification of general themes and ideas that emerge as the researcher reads through the data. This process is also referred to as open coding (Palys & Atchison, 2014, p. 305), because it will probably require multiple analyses. As you read through your transcripts, it is likely that you will begin to see some commonalities across the categories or themes that you’ve jotted down (Saylor Academy, 2012). The open coding process can go one of two ways: either the researcher elaborates on a category by making finer, and then even finer distinctions, or the researcher starts with a very specific descriptive category that is subsequently collapsed into another category (Palys & Atchison, 2014, p. 305). In other words, the development and elaboration of codes arise out of the material that is being examined.

The next step for the research analyst is to begin more specific coding, which is known as focused or axial coding . Focused coding involves collapsing or narrowing themes and categories identified in open coding by reading through the notes you made while conducting open coding, identifying themes or categories that seem to be related, and perhaps merging some. Then give each collapsed/merged theme or category a name (or code) and identify passages of data that fit each named category or theme. To identify passages of data that represent your emerging codes, you will need to read through your transcripts several times. You might also write up brief definitions or descriptions of each code. Defining codes is a way of giving meaning to your data, and developing a way to talk about your findings and what your data means (Saylor Academy, 2012).

As tedious and laborious as it might seem to read through hundreds of pages of transcripts multiple times, sometimes getting started with the coding process is actually the hardest part. If you find yourself struggling to identify themes at the open coding stage, ask yourself some questions about your data. The answers should give you a clue about what sorts of themes or categories you are reading (Saylor Academy, 2012). (Lofland and Lofland,1995, p. 2001) identify a set of questions that are useful when coding qualitative data. They suggest asking the following:

• Of what topic, unit, or aspect is this an instance?
• What question about a topic does this item of data suggest?
• What sort of answer to a question about a topic does this item of data suggest (i.e., what proposition is suggested)?

Asking yourself these questions about the passages of data that you are reading can help you begin to identify and name potential themes and categories.

Table 10.3 “ Interview coding” example is drawn from research undertaken by Saylor Academy (Saylor Academy, 2012) where she presents two codes that emerged from her inductive analysis of transcripts from her interviews with child-free adults. Table 10.3 also includes a brief description of each code and a few (of many) interview excerpts from which each code was developed.

Table 10.3 Interview coding

Just as quantitative researchers rely on the assistance of special computer programs designed to help sort through and analyze their data, so, do qualitative researchers. Where quantitative researchers have SPSS and MicroCase (and many others), qualitative researchers have programs such as NVivo ( http://www.qsrinternational.com ) and Atlasti ( http://www.atlasti.com ). These are programs specifically designed to assist qualitative researchers to organize, manage, sort, and analyze large amounts of qualitative data. The programs allow researchers to import interview transcripts contained in an electronic file and then label or code passages, cut and paste passages, search for various words or phrases, and organize complex interrelationships among passages and codes

Research Methods for the Social Sciences: An Introduction Copyright © 2020 by Valerie Sheppard is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Qualitative Study

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• 1 University of Nebraska Medical Center
• 2 GDB Research and Statistical Consulting
• 3 GDB Research and Statistical Consulting/McLaren Macomb Hospital
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• Bookshelf ID: NBK470395

Qualitative research is a type of research that explores and provides deeper insights into real-world problems. 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’. 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. One of the strengths of qualitative research is its ability to explain processes and patterns of human behavior that can be difficult to quantify. 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. 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. 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. 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.” 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. 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”. 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. 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. 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”.

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. 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. 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. 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. 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. 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”. 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.”

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. 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:

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.

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. 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. The Standards for Reporting Qualitative Research (SRQR) is a checklist covering a wider range of qualitative research.

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.

Copyright © 2024, StatPearls Publishing LLC.

• Introduction
• Issues of Concern
• Clinical Significance
• Enhancing Healthcare Team Outcomes
• Review Questions

Publication types

• Study Guide

• Open access
• Published: 09 May 2024

Overlooked by the obstetric gaze – how women with persistent health problems due to severe perineal trauma experience encounters with healthcare services: a qualitative study

• Katharina Tjernström 1 ,
• Inger Lindberg 1 ,
• Maria Wiklund 2 &
• Margareta Persson 1  

BMC Health Services Research volume  24 , Article number:  610 ( 2024 ) Cite this article

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Metrics details

During the first year postpartum, about 25 per cent of Swedish women with severe perineal trauma (SPT), i.e., a third- or fourth-degree perineal laceration at childbirth, are unsatisfied with their healthcare contacts. Further, there is a lack of research on the more long-term experiences of healthcare encounters among women with persistent SPT-related health problems. This study explores how women with self-reported persistent SPT-related health problems experience their contact with healthcare services 18 months to five years after childbirth when the SPT occurred.

In this descriptive qualitative study, a purposive sample of twelve women with self-reported persistent health problems after SPT were individually interviewed from November 2020 – February 2022. The data was analysed using inductive qualitative content analysis.

Our results showed a paradoxical situation for women with persistent health problems due to SPT. They struggled with their traumatised body, but healthcare professionals rejected their health problems as postpartum normalities. This paradox highlighted the women’s difficulties in accessing postpartum healthcare, rehabilitation, and sick leave, which left them with neglected healthcare needs, diminished emotional well-being, and loss of financial and social status. Our results indicated that these health problems did not diminish over time. Consequently, the women had to search relentlessly for a ‘key person’ in healthcare who acknowledged their persistent problems as legitimate to access needed care, rehabilitation, and sick leave, thus feeling empowered.

Conclusions

Our study revealed that women with persistent SPT-related health problems experienced complex health challenges. Additionally, their needs for medical care, rehabilitation, and sick leave were largely neglected. Thus, the study highlights an inequitable provision of SPT-related healthcare services in Sweden, including regional disparities in access to care. Hence, the authors suggest that Swedish national guidelines for SPT-related care need to be developed and implemented, applying a woman-centered approach, to ensure equitable, effective, and accessible healthcare.

Peer Review reports

Intrapartum and postpartum healthcare should ideally be high-quality, evidence-based, and a positive experience stemming from woman-centred care with a holistic approach based on human rights [ 1 ]. This approach acknowledges each woman’s articulated needs and expectations in her social, emotional, physical, spiritual, and cultural context [ 2 ]. Nevertheless, during the first year postpartum, about one in four Swedish women with severe perineal trauma (SPT) [ 3 ], i.e., a third- or fourth-degree perineal laceration involving the anal sphincter muscle and anorectal mucosa at vaginal childbirth [ 4 ], are dissatisfied with their care and one in three women report ongoing health problems related to their SPT. Women with SPT may suffer from various physiological and psychological consequences such as pain [ 5 , 6 ] , incontinence [ 7 ], defecation problems [ 8 ], vaginal prolapse [ 5 ], sexual dysfunction [ 9 ] or depression and anxiety [ 10 , 11 , 12 ].

Reducing physical symptoms is essential to support emotional and social recovery after any perineal trauma [ 13 , 14 ]. Women with SPT emphasise that professional, competent, and respectful attitudes from healthcare professionals (HCPs), including individual and adapted information, facilitate, and promote their postpartum recovery. Thus, the HCPs’ competence and knowledge of treatment options is a prerequisite for women to access needed care [ 15 ]. An additional problem in the Swedish context is the lack of national recommendations or guidelines, which enables each of the 21 regions to develop own regional and local guidelines. An audit of the existing regional and local guidelines for prevention and care of SPT shows an unexpected diversity or lack of evidence-based recommendations [ 16 ]. However, dissatisfaction with access to healthcare has been expressed by women with persistent, i.e., beyond one year postpartum, SPT-related health problems [ 6 , 17 ]. Furthermore, women criticise inadequate or absent support [ 6 , 18 ], poor information and education [ 6 , 10 , 18 ], and lack of follow-up care regarding SPT and its potential psychological and social consequences [ 6 , 10 ]. Postpartum care focuses more on the baby than the mother’s well-being [ 18 , 19 ]. Also, the available treatment options are perceived as limited and outdated by those with access to needed care [ 17 , 18 ]. Moreover, women with SPT describe that some HCPs tend to normalise their SPT-related problems [ 10 , 17 , 18 , 19 , 20 , 21 , 22 ], and women are met in unprofessional and disrespectful ways [ 17 , 23 ], where HCPs are perceived as ignorant, nonchalant, and questioning women’s symptoms [ 10 , 17 ]. Previous research [ 24 ] indicates an institutional objectification of women with SPT by Swedish healthcare providers hindering access to healthcare, sick leave, and occupational rehabilitation after SPT. In contrast, women also report being acknowledged and liberated when HCPs have a professional and empathic approach and provide continuity of care that enables access to care for persistent SPT-related health problems [ 17 , 18 , 19 , 25 ]. Thus, several women who sustained an SPT during childbirth do not experience access to needed and necessary care, a fact that needs further exploration.

Globally, sexual and reproductive health and rights (SRHR) are crucial for individual health and gender equality [ 26 ]. Current issues within SRHR and midwifery are controlled by the institutional power in health institutions, i.e., medical power [ 27 ], connected to the still-existing economic and educational disadvantages of women globally, which are also feminist issues [ 26 , 28 ]. As midwife stands for ‘with woman’ [ 28 ], gender or feminist approaches are used in advancing midwifery theory [ 27 , 29 ] and various aspects of SRHR topics such as breastfeeding promotion [ 30 ], birth plans [ 31 ] and attitudes towards contraceptives [ 32 ]. In midwifery and feminist approaches, the biological material body and the socially constructed gendered body are viewed as intertwined [ 33 ]. Moreover, midwifery care is recommended to be woman-centred [ 1 , 2 ], focusing on the individual woman’s needs and transferring control from the institution to the woman herself. However, despite the different organisations of sexual and reproductive healthcare between countries, international research shows similar results regarding women’s diverging experiences with postpartum SPT-related healthcare [ 6 , 15 , 17 , 18 ].

In sum, there is growing evidence showing that many women with persisting health problems caused by SPT are often, but not always, met with mistrust and ignorance when seeking care for their problems. Even though there may be national, regional, or local protocols or guidelines for care after SPT, women with persistent SPT-related problems still raise their voices about the difficulty of getting access to competent quality care. This indicates a potential gender bias [ 34 ] and a need for gender theoretical perspectives in midwifery [ 28 ], as utilized in this study. Additionally, few studies explore the care-seeking experience among this group of women in a longer time perspective after childbirth when the SPT occurred.

The aim of this study is to explore how women with self-reported persistent SPT-related health problems experience their contact with healthcare services 18 months to five years after childbirth when the SPT occurred.

Study design and context

The present study is part of a larger research project investigating the long-term consequences of SPT on quality of life, working life, and healthcare contacts. This study had an inductive qualitative interview study design applying qualitative content analysis to analyse data [ 35 , 36 , 37 ]. This method searches for patterns, e.g., by identifying similarities and differences in the data. The researchers obtain an in-depth understanding of the studied phenomenon through abstraction and interpretation [ 36 ]; thus, an appropriate method to apply to capture women’s experiences of their healthcare encounters when seeking medical help and support. Throughout the research process, the recommendations for qualitative research according to ‘Consolidated criteria for reporting qualitative research’ (COREQ) were followed [ 38 ].

Sweden has 21 partly independent regions primarily responsible for providing healthcare services to the population. Healthcare services are tax-funded, and the regions have extensive autonomy to decide upon the healthcare services within each region based on the frameworks of the Health and Medical Service Act [ 39 ]. Additionally, within the Swedish social security system, 480 days of paid parental leave are allocated to each child in Sweden and can be utilised by their legal guardian(s) until the child is twelve years old. Of these 480 days, 60 days are specifically assigned to each parent, and the remaining days are split between parents as desired. The financial compensation is based on the parent’s income and is financed by taxes [ 40 ].

In Sweden, midwives are the primary care providers to women with normal pregnancies, births, and postpartum care. In case of complications to pregnancy and childbirth, midwives collaborate with other medical professionals, especially obstetricians. For example, midwives suture first- and second-degree perineal lacerations, while obstetricians are responsible for all SPT repairs [ 41 ]. Generally, in Sweden, women who sustain an SPT during childbirth are offered a check-up with the obstetrician responsible for the repair before discharge and should also have a follow-up with an obstetrician or sometimes a physiotherapist within the postnatal period. Thereafter, women with no mayor initial healing problems are advised to contact relevant healthcare services if any health issues related to the SPT should arise in the future. Women presenting with complicated healing are treated accordingly. Additionally, women with second- to fourth-degree perineal lacerations are assessed with questionnaires three times during the first year postpartum by the National Perineal Laceration Register. However, there are no recommendations in Sweden for prolonged check-ups for women with SPT after the postnatal period and no guidelines on organised check-ups for women with prolonged symptoms due to SPT exist [ 42 , 43 ].

Women with persistent SPT-related health problems and characteristics were purposively recruited to achieve a heterogeneous sample reflecting multiple experiences. An overview of inclusion and exclusion criteria can be found in Table 1 .

The closed Swedish Facebook community ‘Förlossningsskadad? Du är inte ensam!’ [‘Injured at childbirth? You are not alone!’] functioned as a recruitment platform for a national sample of women reporting persistent SPT-related health problems. The Facebook community is secluded to women with SPT and started in 2014. During the data collection period (Nov 2020 – Feb 2022), the group had over 7,600 members; today, the community has grown to include over 9,500 members [ 44 ].

In late November 2020, the administrators of the Facebook community pinned a digital poster with study information and a link to the study homepage in the group feed. The study homepage contained written information on the research project and contact details for the research group if any women wanted additional information about the study. Interested potential participants contacted the research group via a contact form on the homepage, and the first author (KT) confirmed that the potential participants met the inclusion criteria via telephone. Thirteen participants from different parts of Sweden showed interest in participating and left their contact information. One woman never responded to our efforts to reach her. The remaining twelve women fulfilled the inclusion criteria and were invited to an interview. Before the interview, the women answered a digital survey on background data (such as demographic data, education, employment, sick leave, and childbirth history) distributed via REDCap ® , a web-based application to create secure online questionnaires and research databases [ 45 ]. The interviews were finalised in February 2022.

Data collection

We collected data via individual open-ended interviews [ 46 ], supported by a semi-structured interview guide [see Additional file 1 ]. The interview guide, developed by KT and MP with input from MW and IL, was based on literature reviews, our awareness of gender as a social construct [ 33 ], and the clinical pre-understanding within the research group. After a pilot interview conducted by KT (not included in the data), minor adjustments were made to the interview guide. The final interview guide covered the topics of everyday life experiences, work, and general functioning. However, despite the mentioned interview topics, the emergent study design and the ability to speak freely about what was perceived as important for their daily functioning, the contacts with healthcare services was brought up in vivid and extensive narratives by all participants as part of their descriptions of their challenges in everyday life and their ability to function at work. Hence, the experiences the women made of the healthcare services played an important role for the women in their daily management of SPT-related health problems.

As data collection occurred during the COVID-19 pandemic, all participants were interviewed digitally via Zoom ® [ 47 , 48 ]. With the participant’s consent, the interviews were audio-recorded via Zoom ® and a separate digital recorder (as backup). Any Zoom video files automatically generated were deleted directly after the termination of the interview to protect participants’ identities. The first author interviewed all women; in two interviews, co-authors (IL or MW) also attended. The authors had no professional or personal affiliation with the enrolled participants . Detailed interviews ranging from 29 – 112 minutes (median: 61.5 minutes) gave extensive data. All interviews were performed in Swedish and transcribed verbatim. After that, the first author validated the transcripts for accuracy by reviewing the text while listening to the recordings.

Authors’ pre-understanding and theoretical positionality

The research group comprises three midwives (KT, IL, MP) and one physiotherapist (MW). We all have extensive professional experiences from clinical practice in primary and in-patient care, where three authors (KT, IL, MP) have specific professional experiences of caring for women with SPT. Additionally, we are women, feminists, and mothers with various birth experiences. Further, the group holds expertise in gender studies and qualitative research within midwifery science, such as perineal trauma and medical sociology. Hence, we stem from a social constructivist research standpoint and utilise ourselves as co-constructors in the analysis process. As feminist researchers, we apply a gender theoretical lens to the data.

Data analysis

The interviews were analysed using qualitative content analysis with an inductive and stepwise approach focusing on the manifest and latent content [ 35 , 36 , 37 ]. The interviews, transcripts, and analysis steps were performed in Swedish.

The analytical procedure started with reading the transcripts multiple times while highlighting text, meaning units, with content relevant to the aim of this study. Then, identified meaning units were condensed, focusing on preserving their core meaning and labelled with manifest codes [ 35 , 36 , 37 ]. Initially, KT coded one interview and triangulated those codes with the principal investigator (MP). KT then coded the rest of the interviews. In the next step, similar codes were clustered, forming subcategories based on the manifest content. Moving towards an interpretation of the content, categories were created by the abstraction of subcategories. This was done by KT and MP separately and then triangulated to identify significant concepts. Next, the preliminary categories and subcategories were triangulated with the whole research group until a consensus was obtained. To answer the question of ‘what?’ and ‘how?’ within the data, the latent content and thread of meaning were identified by clustering and abstracting the emerging findings to form subthemes and a theme [ 36 , 37 ]. The emerging findings were also peer-reviewed and discussed at a research seminar. The finalisation of the analysis resulted in an overarching theme and four subthemes. The translation of categories, subthemes, theme and inserted citations from Swedish into English was performed as a last step. The translation and choice of words were discussed between authors (all knowledgeable in English) to reach a consensus and minimise translation bias.

During the coding process, the researchers used MAXQDA ® [ 49 ], a software for organising, transcribing, analysing, and visualising qualitative research data, and Microsoft Excel ® [ 50 ] as aids to organise the codes.

Demographics of included participants

The background characteristics of the twelve participants in the final sample are presented in Table 2 .

The participants identified themselves as cis women, i.e., their gender identity matched their sex assigned at birth [ 51 ], and are thus referred to as ‘women’ in this paper. All women were in a partner relationship. The women reported a broad spectrum of physical and phycological health problems following the SPT at childbirth, e.g., urine or anal incontinence, pain in the lower abdomen, sexual dysfunction, and depression. Thirty per cent of the women had full-time employment, and the proportion of parental leave varied from 12% to 100% (three women had an ongoing parental leave with subsequent children at the interview). Further, 60% of the women had a sedentary occupation. Five women had been on sick leave after reconstructive surgery, and five reported sick leave for other reasons than their SPT.

The analysis resulted in one theme, ‘Overlooked by the obstetric gaze – living the paradox of a normalised but traumatised postpartum body’, with related subthemes ‘Questioning whether it’s all in my head’, ‘Fighting persistently for access and legitimacy in no (wo)man's-land’, ‘Facing multidimensional losses when no help in sight’, and ‘Depending on other’s advocacy to navigate an arbitrary system’. An overview of the findings is presented in Table 3 . The findings are presented as an overarching theme and thereafter, the related subthemes and categories. Citations from the participants illustrate the findings. All women have been allocated pseudonyms in the result presentation.

Overlooked by the obstetric gaze – living the paradox of a normalised but traumatised postpartum body

The latent theme ‘Overlooked by the obstetric gaze – living the paradox of a normalised but traumatised postpartum body’ represented the women’s experiences of healthcare encounters covering HCPs’ diminishing attitudes towards women’s persistent SPT-related health problems and the women’s difficulties accessing healthcare and sick leave. We interpreted that the women were assessed by the HCPs’ ‘obstetric gaze’, i.e., a medical gaze in postpartum healthcare normalising their persistent health problems and judging the women’s lower abdomen as ‘fine’ by their looks. The obstetric gaze put the women in a paradoxical situation where HCPs normalised tangible symptoms to be a natural part of childbirth. With no medical legitimacy of the health problems, the women also felt labelled as ‘hysterical’ (exaggerating health problems) by the HCPs. As a result, on the one hand, they had to continue facing persistent and tangible health problems such as incontinence, pain or prolapses. On the other hand, no acknowledgement by HCPs of their health problems led them to question whether their problems were merely a product of their imagination and, thus, only existed ‘in their head’. The theme also comprised women’s struggle for legitimacy in a gendered healthcare system - a no-(wo)man's land. They experienced that healthcare services and social insurance systems were challenging to access and demanded a tenacious and extensive fight to obtain legitimacy for their health problems. Consequently, the women had to put up with neglected healthcare needs, negatively impacting their physical and emotional well-being, and financial and social status when no medical help or rehabilitation was available. However, some women had encountered an HCP who was empathic and understanding, hence not guided by the obstetric gaze. Such encounters legitimised persistent problems and were crucial for accessing needed care, sick leave, and rehabilitation.

Questioning whether it’s all in my head

The subtheme ‘Questioning whether it’s all in my head’ focused on the women’s experiences of facing ignoration and no confirmation of perceived health problems and thus being labelled as a hysterical woman. The related categories referred to a normalisation process that the women experienced in their encounters with HCPs, which made them question their bodily perceptions. Furthermore, the women felt accused of exaggerating symptoms because their persistent SPT-related health problems did not match HPCs’ views of acceptable postpartum symptoms. Thus, it could be understood that the women found themselves in a paradox of suffering from tangible physical consequences after SPT, which were normalised by HCPs and their ‘obstetric gaze’.

Facing HCP's ignoration of perceived problems

The women experienced the HCPs defining their persistent health problems after the SPT as ‘normal’. The HCPs assured the women that their problems would disappear with time or that transient motherhood-related aspects, such as breastfeeding or fragile vaginal mucosa, were the cause of the problem. One woman expressed:

“Then I felt, ‘It should not feel like this; this is something wrong’, and I sought medical attention and was seen by multiple physicians […] They thought my vaginal mucous membrane was not ready for intercourse. I was still breastfeeding, so they thought I should stop breastfeeding. Then maybe the mucous membrane would be restored, which was causing me the pain. I was not listened to at all. I was treated very poorly by one physician in particular, and despite second opinions and so on, nobody… nobody took me seriously.” (Linda)

Consequently, the women perceived that their concerns were ignored. They also learned that the HCPs saw their prolonged physical problems after SPT as an inevitable part of childbirth, which the women should accept. One woman resigned:

“But then [the physician] says something like this: ’Well, that's completely normal’, but I felt like, ‘Yes, but it doesn't feel normal'.” (Emma)

After the genital and pelvic floor examinations, the HCPs often guaranteed the women that ‘everything looked fine’, i.e., reinforcing the normality of the genital area. Although the women described to the HCPs that they struggled with SPT-related problems, their concerns were met with a comment on the physical appearance rather than a comprehensive examination of the pelvic floor's functionality.

One woman responded:

“They think ‘everything looks fine’ and ‘everything looks good and repaired’. I still have problems. I was also referred to a surgeon, who did a rectoscopy, and ‘it looked so nice’. Then, I was referred to a urotherapist to learn how to pinch my muscles because ‘everything would be so good’. She helped me get a second opinion in XX [town], where they discovered that there was still damage." (Jin)

Another woman expressed:

”I couldn’t care less what it [genital area] looks like. Nobody will be down there watching. I only need it [genital area] to function as intended.” (Anna)

Consequently, the women felt ignored and unheard in their contact with healthcare services. They perceived that HCPs did not listen to them, leaving them feeling invisible, sometimes even having severe health problems.

“I was hospitalised with sepsis before someone listened to me.” (Josefin)

Being labelled as a hysterical woman

The women also experienced being labelled as the ‘hysterical woman’ who exaggerated their persistent symptoms and had mental health problems. The women described how the HCPs accused them of imagining their SPT-related health problems. One woman indignantly revealed that the HCP she encountered said, 'These problems only exist in your head’ (Joanna), i.e., suggesting that the perceived symptoms did not exist and rejecting the health concerns. Hence, this attitude made some women believe their problems were a product of their imagination and sometimes made them even question their sanity.

Moreover, the HCPs’ condescending attitudes towards the women made them feel dismissed and devalued. For example, the women shared that HCPs laughed at them or were rough or cold during the examination. Moreover, HCPs expressed that they had ‘seen worse’ (Amanda). Some women also conveyed that they were advised ‘to drink some wine to feel better’ (Elin) when discussing painful intercourses due to their SPT-related health problems.

“You are constantly dismissed, ‘No, but everything looks fine, you have no problems’. Then you start to think you’re imagining things. And then you may not dare to talk about the injuries.” (Jin)

Fighting persistently for access and legitimacy in no (wo)man's-land

The subtheme ‘Fighting persistently for access and legitimacy in no (wo)man's-land’ referred to the women’s experience of gender constructs related to inaccessible healthcare services and their often year-long struggles to access this gendered healthcare and linked social insurance systems. The difficulties in accessing care created negative attitudes towards the healthcare services, making the women wish for general improvements in women’s healthcare.

Struggling to access the gendered healthcare and social insurance systems

The women pointed out that after giving birth, they needed more extensive information on their injury, precautions, available help (follow-up care or re-operation), and sick leave. To overcome the lack of required information, they had to request or actively search for it on their own, which also led to uncertainty about where and when to seek further help if needed.

“I was sent home with a brochure and a pat on the shoulder.” (Amanda)

The women also experienced a lack of adequate healthcare services targeted at their SPT-related health problems. For example, many women did not have access to a pelvic floor clinic or had to travel long distances to see specialists. Hence, their place of residence decided the quality of care the women received. Moreover, some women problematised the organisation of postpartum care as they missed out on follow-up care and even, in some cases, were denied follow-up care or referrals to specialised care were lost. As a result, some women had no opportunity to talk to the operating physician or experienced no follow-up care, although they requested it.

“They said it can take up to a year to get better. So, when that year had passed, and before starting to work again, I called different places in the hospital and asked: What should I do now? […] It took several months before I got an appointment with the surgeon for an assessment. And then I had to get a second opinion. So, it took like seven months before I got an appointment at [a specialist clinic].” (Hawa)

For the women, access to healthcare services, sick leave certificates, and HCPs’ dismissive attitudes were perceived as gender-related, i.e., difficulties in obtaining help from women’s healthcare services would not exist if the services were more women-oriented. One woman illustrated this by expressing: ‘If men gave birth to babies, the situation would not be like this’ (Joanna). Moreover, they perceived that women’s healthcare services were not prioritised. They explicitly stated that the absence of sick leave certificates and benefits was related to their gender. The women were expected to cope without sick leave benefits because vaginal and perineal lacerations of any scope were viewed as a natural part of childbirth, a normal process of a woman’s body. Thus, sequelae thereof did not exist or were taboo in society.

“Everything that happens during and after childbirth and related injuries has been a taboo discussion topic, so it has been completely ‘normal’ to suffer from persistent pain.” (Anna)

“I have applied for compensation from the national patient insurance. I got rejection after rejection; nothing has gone wrong. I was told: 'You simply must expect these things in childbirth. And a caesarean section is not less risky'.” (Hawa)

Thus, the women argued that society and the government did not invest needed resources in women’s healthcare. In addition, those few women receiving a short period of sick cash benefits had it immediately after giving birth or after re-operation, but not for prolonged problems. Further, the women noted that they were not offered sick leave certificates due to persistent physical SPT-related health problems but instead due to mental issues, such as depression or anxiety.

“I've heard about women who have been mentally unwell and have hurt their children. So maybe physicians get cautious and put women on sick leave if they say, ‘I'm not feeling mentally well’. Then they act quickly because they think it's so important. But they don't think about the physical injuries because that's part of [childbirth].” (Jenny)

However, the women shared how they fought long and hard for acknowledgement and care and made demands; for many, this process had covered years. They had to repeatedly insist that something was wrong and felt pressure to prove their health problems to the HCPs. In some women, this led to their persistent problems being diagnosed and acknowledged after several years of delay. The struggle for care involved countless visits and referrals to different HCPs, demanding much strength and persistence, which exhausted them. Sometimes, the sequelae had to develop into an acute health situation, or some women decided to pay for private care to access the proper treatment and rehabilitation. Further, with time, they also became explicit about their demands for sick leave certificates and benefits.

“Well, it [short sick leave period because of birth traumas] just feels like scorn. To me, it is not a sufficient length of sick leave.” (Elin)

Wishing for improvement in women's healthcare

The perceived lack of adequate care and rehabilitation, access to sick leave benefits, and HCPs’ attitude negatively influenced the women’s opinions on healthcare services, especially postpartum healthcare. In addition, the women perceived many HCPs as unprofessional, indifferent, and unstructured. As a result, the women mistrusted the HCPs and lost hope in healthcare services. Thus, they were reluctant to seek further care and were anxious about receiving proper treatment or that HCPs would miss important things.

“I am not being listened to in women's healthcare. This is partly why I feel so disappointed.” (Linda) “You just don't trust the healthcare system. […] Some people have been struggling with their injuries for like 18 years. But the [specialist clinic] – I finally received fantastic treatment, and what if it could be available everywhere [in Sweden]?” (Hawa)

Moreover, the women described a struggle for their rights when deciding whether to report the HCPs to the authorities and pointed out the need to improve women’s healthcare. Reporting HCPs was perceived as complicated as the women did not want to blame specific individuals. The women saw that the major problem lay within the healthcare system and with individual HCPs.

“In the end, I met a fantastic person [healthcare professional]. She wanted me to report the mistreatment when I eventually had the strength. Because no one listened when I said I was ill. So, she has offered to help me if I want to, but I don't know if I have the strength to file a complaint.” (Josefin)

A wish to improve women’s healthcare services was articulated, especially regarding personal follow-up care beyond one year postpartum and the possibility of full-time or part-time sick leave certificates and benefits for persistent problems on equal terms. This wish also strengthened their decision not to give up searching for help and to raise their voices to help themselves and other women.

“I received physiotherapy and the follow-up surveys [the Perineal Laceration Register] during the first year, but thereafter I would have liked to have an annual follow-up for the next years to ensure the status and potential re-operations. […] I can google, but I want to have that information in dialog with a living person, but you do not get that.” (Jenny)

Partaking in developing educational material for HCPs or starting a career within women’s healthcare were some women’s ways to contribute and increase competency in persistent SPT-related health problems.

“One of my strategies since I got the injury is also to try to influence. Being able to be involved and influence what postpartum care should consist of.” (Jin)

Facing multidimensional losses when no help in sight

The subtheme ‘Facing multidimensional losses when no help in sight’ covered physical and mental health consequences and the financial and social losses the participating women faced when no support or access to needed care and rehabilitation was provided.

Being physically victimised by HCP's malpractice

The women’s experiences covered either being misdiagnosed during the suturing after birth or in the following years when seeking help for persistent SPT-related health problems. Further, they shared how physicians had incorrectly sutured vaginal and perineal muscles after childbirth, leading the women to live with incontinence, pain, prolapses, or sexual dysfunction if their vaginas were sutured too tight. They also described how they endured infections, wound ruptures, sepsis, necrosis, and re-operations. Additionally, the women perceived a general lack of competency regarding communication and persistent SPT-related health problems, including problems related to sex life and sexual functioning, besides a more specific lack regarding suturing techniques and ultrasound examinations.

“I was referred to a specialist clinic. And they found out that all the muscles were separated, the internal and external sphincters were torn, and my pinching ability was kind of weak. So, it was quite the opposite, really, quite the opposite. None of what the other physician had said was true [laughs]. Absolutely incredible. And she is supposed to be a specialist.” (Hawa)

Aching inside

Living with troubled postpartum bodies and the absence of HCPs’ legitimation of the women´s problems made them struggle mentally, feeling speechless and silenced. This neglect reinforced irritation, anger, distress, bitterness, and disappointment towards the HCPs and the healthcare services. One woman illustrated the emotional struggle in this way:

“It's just that the health services don’t believe you, which makes you feel terrible. It's a big deal that no one listens.” (Josefin)

Moreover, the women felt uncertain about their health status due to a default medical diagnosis with concerns for their future and which staff to trust. Consequently, some had to bite the bullet, put up with their situation, and try to think positively. Other women were denying or diminishing their SPT-related health problems, accepting that their symptoms would improve, even disappear or that their condition was ‘normal’ as they had been told. Further, the women described despair because their neglected health problems caused by their SPT made them feel exposed, unsure, and hopeless. In some, this desperation resulted in a mental breakdown, a fear of losing custody of their child due to mental illness or suicidal thoughts.

“Something broke inside of me that day. I felt entirely omitted; I was close to leaving my son and committing suicide. Nobody understood how bad everything was.” (Elin)

Additionally, the women suffered emotionally when motherhood was crushed. Their partner had to take the primary responsibility for the family, and the children had to come in second place as the mothers suffered from various physical and mental health problems. As a result, the women felt they missed their children’s development and could not use their parental social security benefits as desired.

“I feel devasted because people tell me, ‘You are on maternity leave’. I’m not on maternity leave; I’m sick. I should be on sick leave.” (Jaanika)

Suffering financial and societal losses

Moreover, the women suffered financially and societally due to persistent health problems. Some women were denied financial compensation from Patient Insurance (a national insurance system where patients can seek compensation for care injuries). The Social Security Agency and the HCPs were perceived as obstacles to receiving sick cash benefits. They noted that ‘extensive’ health problems were required to receive sick cash benefits and that their health problems paradoxically were not seen as extensive or even a problem per se by the HCPs; hence, no sick leave certificates were issued.

“He [the physician] tried to argue and clarify my pain situation in the sick leave certificate to meet the requirements for a sick leave benefit at the Social Security Agency. I was in so much pain and had to lie down to breastfeed. But, no, ‘If you can manage to hold the baby when breastfeeding, then you are on maternity leave, not sick leave benefit’ [mimicking the official at the Social Security Agency who rejected the certificate and consequently also the sick cash benefit]”. (Jaanika)

Furthermore, the women were set back financially and societally because they could not work full-time due to their persistent health problems. Therefore, some women chose to compensate for their work absence with part-time parental benefits to diminish their working hours and cover their inability to work due to persistent SPT-related health problems. Without a sick leave certificate, i.e., the physicians or the officials at the Social Security Agency’s acknowledgement of a ‘true’ health problem, partners or other relatives were obliged to adjust their work schedules to support or unburden the woman’s suffering and inability to work full-time. This reduction in working hours for the SPT-affected women and, in some cases, their partners was expressed to potentially negatively affect their upcoming careers and pensions. As a result, the women experienced being caught between stools in the social insurance systems:

“[…] You end up in a position where you are neither on sick leave nor unemployment benefits and at the same time cannot perform any offered work [due to persistent problems]. But multiple societal bodies demand and expect you to be a part of the working force, and nobody really listens.” (Elin)

Depending on other’s advocacy to navigate an arbitrary system

The last subtheme, ‘Depending on other’s advocacy to navigate an arbitrary system’, highlights the women’s experiences of, often by chance, finding a single devoted professional, i.e., a ‘key person’, to access needed care and rehabilitation. Such a ‘key person’ was vital to recognising persistent problems, legitimating symptoms, and enabling access to needed care, sick leave, and rehabilitation. The women who finally had legitimation for their health problems described that the medical diagnosis also came with a feeling of sanity and empowerment, relieving them of their paradoxical situation.

Encountering a ‘key person’ to receive needed care

A support system was a prerequisite for enduring their health problems and finding the strength to fight for access to care. This system could be a partner, other family members, or friends who gave the women power and courage, but most importantly – encountering a professional who saw their problems and provided referrals or other options to obtain the needed help and support. In most cases, women would search for years for competent HCPs, such as midwives, physicians, or physiotherapists, who would listen and acknowledge persistent problems. This ‘key person’ showed empathy and trustworthiness, creating relief and security. Further, the ‘key person’ was portrayed as competent, attentive, professional, and respectful. The ‘key persons’ also shared women’s outrage at the mistreatment and default healthcare they endured. Additionally, these ‘key persons’ were surprised that the women were not on sick cash benefits due to their symptoms and that they had to compensate for their financial situation with parental benefits or reduced working hours and lower salaries. Consequently, finding this ‘key person’, often by chance or word of mouth, was crucial for accessing care and marked a significant turning point in the women’s recovery.

“I sought help from another midwife, as I felt something was wrong. This midwife referred me to the physiotherapist, who referred me to a specialist, who then referred me to surgery and rehabilitation.” (Malin)

Some women received follow-up care for their persistent SPT-related health problems during the first year postpartum. If persistent problems occurred and were acknowledged, the women were offered different surgical approaches with various outcomes, consultations by colon specialists, physiotherapy, and psychiatric care. They were grateful for the help they received but felt more comprehensive care was needed.

Feeling sane and empowered

Confirmation of persistent SPT-related health problems was expressed as liberating, strengthening and, as one woman put it, a ‘win’ (Elin). Receiving a medical diagnosis and appurtenant treatment was relieving because the medical confirmation of the symptoms released a considerable burden. These women described being acknowledged, and the diagnosis proved that health problems existed, and the struggles were not in vain. Furthermore, it explicitly stated to everyone, including themselves, that they were not ‘crazy’, ‘imagining things’ or ‘hysterical’.

“So, my laceration has been classified as an injury caused by the healthcare services. This was somehow a confirmation. It's not just that it's in my head, but it has been established that it is a medical injury, and it could have been avoided.” (Jin)

Alongside feelings of sanity and being legitimised, the women experienced empowerment. The women felt supported and confident. Thus, finding an agency to address the taboo of their SPT by talking openly about it and helping others in the same situation was also seen as therapeutic. Further, the legitimation of the sequelae and access to appropriate care gave them time to heal and process their trauma. Receiving sick leave certificates and benefits was seen as a part of the empowerment and legitimacy of their persistent SPT-related health problems, reducing stress, and easing the financial burden. Furthermore, access to occupational rehabilitation and understanding at work became available. Thus, the women who had received the help they needed after a struggle to obtain it were hopeful about the future and possible recovery.

“I have regained my authority to speak up. It [SPT-related health problems] should be out in the open, not withheld.” (Jaanika)

Our main finding was that women with persistent health problems due to SPT at childbirth were caught in a paradox of living in a normalised but traumatised body, and their health problems were rejected as postpartum normalities. Furthermore, our results elucidated the difficulties in accessing postpartum healthcare, rehabilitation, and sick leave benefits. Therefore, the women struggled with neglected healthcare needs, diminished emotional well-being, and loss of financial and social status. Our study highlighted experiences up to 5 years after sustaining SPT, which showed that some women’s SPT-related health problems do not diminish with time. They faced challenges functioning in daily life, at work, and in society. In contrast, finding a ‘key person’, i.e., a professional who acknowledged the women’s persistent problems as legitimate, was a prerequisite for accessing all the needed care and sick leave and enhancing empowerment for the women. Thus, this ‘key person’ was not blinded by the obstetric gaze and instead used their agency and advocacy as support.

In the following, we will discuss our findings related to other empirical studies and problematise them with theoretical reflections.

The paradox of normalising the postpartum body

In our findings, the paradox arose when the HCPs dismissed physical health problems after SPT despite women’s perceived symptoms. Central in this context was a normalisation process where health problems were regarded as ‘normal’ by HCPs, a phenomenon also found in prior research on SPT [ 17 , 18 , 19 , 20 , 21 , 22 ]. The HCPs’ normalisation of women’s health problems can also be found regarding other medical conditions affecting women, such as pelvic organ prolapse [ 52 ], menstrual pain [ 53 ], endometriosis [ 54 ] or nausea and vomiting during pregnancy [ 55 ]. In light of the medicalisation of women’s healthcare, where the medical field has sought to pathologise natural bodily processes such as pregnancy and childbirth [ 33 ], actual medical conditions such as persistent SPT-related health problems are paradoxically normalised. Our findings, therefore, highlight the need to challenge HCPs’ views of what constitutes a ‘normal postpartum body’ or ‘normal postpartum symptoms’ after sustaining SPT.

The key to healthcare

In the context of denied legitimacy of health problems and neglected needs, it appeared that the women became dependent on the goodwill of a ‘key person’, personified as the respectful, competent, and empathetic HCP. Prior research on SPT has also found women struggling with accessing healthcare [ 6 , 17 ] and specific HCPs as enablers of care [ 12 ]. The dependency on a ‘key person’ to access adequate care might highlight a structural problem within the provision of postpartum SPT-related healthcare. Globally, there are a few national guidelines on SPT management and prevention [ 56 ]. Additionally, no national guidelines regarding postpartum care of SPT exist in Sweden, and pelvic floor teams are only available in some Swedish regions [ 16 ]. In our study, the women lacked information, and competent HCPs were hard to find or located far away. Other studies have shown poor patient information and education as a postpartum problem [ 6 , 10 , 18 ], indicating a need to develop targeted oral and written information on wound healing and recovery. Further, women in Australia describe similar challenges to accessing SPT-related healthcare when having persistent SPT-related health problems [ 18 ]. The absence of national Australian guidelines may have led to inconsistent care, failing to meet women’s healthcare needs. Further, women from rural areas have had additional difficulties accessing needed care. In 2021, a clinical standard for SPT was implemented in Australia, comprising care standards for follow-up [ 57 ]. Thus, to improve the national situation in Sweden, more research and resources must be allocated to develop evidence-based recommendations, preferably internationally accepted guidelines [ 56 ]. Moreover, the accessibility of SPT-related healthcare, such as pelvic floor clinics, needs to be expanded so that women can easily meet their ‘key person’ if required.

Woman-(de)centred care?

We found that HCPs were obstructed by their obstetric gaze when assessing women with persistent SPT-related health problems. Obstetric gaze derives from the medical gaze notions [ 58 ], suggesting a gaze that splits the individual from the body, constructing the care-seeker as a medical object or condition instead of an individual with a social context. This gaze blinded HCPs who normalised obvious health problems. Recent advances in women’s healthcare in industrial countries and midwifery research show development towards continuity of care models with a woman-centred approach in different caseload-midwifery projects and informed choice regarding place of childbirth [ 28 , 59 , 60 , 61 ]. Wom e n-centred care [ 2 ] is a widespread care philosophy within midwifery that advocates for providing individualised care to women. Further, wom a n-centred care emphasises the individual woman’s healthcare needs and situation, incorporating the concepts of choice, control, continuity of caregiver, and self-determination. It can be argued that the obstetric gaze obstructed HCPs in providing wom a n-centred care because they did not acknowledge the women’s healthcare needs. Consequently, the women did not have control over their health situation. Making women feel empowered [ 2 , 62 ] is crucial in woman-centred care. Hence, the ‘key persons’ in our study managed to provide wom a n-centred care where acknowledgement of problems as real medical problems and access to care made the women experience empowerment. Therefore, we argue that guidelines regarding follow-up care after SPT should ideally be developed with wom a n-centred care as its core.

Everything looks fine

The biomedical model has traditionally focused on normality and abnormality rather than health [ 63 ]. Theoretically, the ‘obstetric gaze’ is closely tied to the ‘medical gaze’ and the ‘male gaze’, referring to the biomedical paradigm and its power [ 27 , 58 ]. In our study, the obstetric gaze judged the women’s persistent health problems due to SPT as ‘normal’ and the appearance of their genital area as ‘fine’, which created a paradoxical situation regarding the legitimacy of their ongoing health problems after SPT. Generally, the healthcare sector is critiqued for reducing the body to only incorporating organs and tissue, i.e., focusing on physical symptoms [ 27 ].

The women in our study, of which most showed more than one significant symptom after SPT, noted that HCPs would comment on the physical appearance of the perineal area rather than its functionality by telling them that ‘everything looked fine’. The focus on looks rather than functionality regarding SPT-related health problems aligns with the findings presented by others [ 17 ]. Having women describe how their persistent physical pelvic floor problems after SPT during childbirth are trivialised, normalised, questioned, and labelled as mental health issues is of utmost concern. This implies the need for rapid improvements in HCPs’ knowledge and organisation of care but also raises the question of what is considered a normal status and recovery after any perineal laceration in the short- and long-term perspective. A similar discursive focus on women’s appearance instead of their health problems has also been found among HCPs when women seek care for chronic pain [ 64 ]. The sentence ‘Everything looks fine’ can be interpreted as an objectifying, gendered discourse in an obstetric context. This discourse may reinforce the obstetric gaze and, in the broader sense, the medical gaze [ 58 ]. The Swedish Health and Medical Care Act [ 39 ] advocates for the respectful treatment of patients. Hence, it is noteworthy that the women experienced being judged by the looks of their genital area in their medical encounters rather than HCPs addressing the functionality. Such treatment does not align with the legislation and calls for a discourse analysis of the attitudes of HCPs towards women with persistent SPT-related health problems and their experiences of providing care for affected women.

Being subjected to obstetric gaslighting

In light of the women’s perception of their dismissal as dramatic, illegitimate, and irrational patients, we argue that they faced so-called ‘gaslighting’ in an obstetric context [ 65 , 66 ]. Thus, the women experienced being offered sick leave for mental problems instead of their perceived physical health problems, depicting them as hysterical women who exaggerated their condition. Gaslighting is a concept used in medicine in general [ 66 ] and in obstetrics regarding traumatic childbirth experiences [ 65 ]. The concept of hysteria, i.e., a prior medical diagnosis and historical concept theoretically linked to femininity [ 67 , 68 ] and ‘obstetric gaslighting’ [ 65 ], has also been found in research on women’s chronic pain [ 64 ] and endometriosis [ 69 ]. Men with chronic pain are perceived as brave, and women in pain are hysterical, emotional, whining, malingering, or imagining pain [ 64 ]. Further, women with endometriosis are viewed as ‘reproductive bodies’ with a proneness for hysteria [ 69 ]. Obstetric gaslighting, enforced by the normalisation of SPT-related health problems and the gendered stereotype of women as hysterical patients, puts women with SPT in an inferior position towards HCPs and can, therefore, be interpreted as a demonstration of institutional power [ 65 ]. Hence, being overlooked by the obstetric gaze might constitute a form of obstetric gaslighting, a concept that has not been applied to SPT before.

Implications and significance

Our study indicated that women continue to have problems accessing healthcare for persistent SPT-related health problems several years postpartum. Additionally, women with persistent SPT-related health problems often depended on a ‘key person’ with the competence to open the doors to comprehensive care, as shown in our findings. The Swedish Government launched a multi-million project from 2015 to 2022 to improve and promote women’s health [ 70 ]. Despite this investment, the depicted experiences of the included women reflect upon remaining structural and clinical problems within Swedish healthcare, which need further attention, investigation, and actions. Additionally, there are considerable differences in reported satisfaction and prevalence of complications at the one-year follow-up between the regions [ 3 ], indicating that there are suboptimal healthcare services. With a significant variation in satisfaction and recovery at one year, there are reasons to believe that women with prolonged problems may experience problems getting access to needed care.

Our study also showed that SPT-related healthcare services are not available on equal terms to women with persistent SPT-related health problems. In general, many women within this group had problems accessing care and sick leave for years. However, depending on where the women reside, not all women have access to specialised care. This inequity may be explained by Sweden having 21 self-governing health regions, and in the absence of national guidelines regarding SPT care and follow-up, the healthcare provision for affected women varies. To secure access to postpartum care for women with SPT in general and those with different prerequisites within this group, implementation studies are needed to develop and evaluate the effect of national guidelines for follow-up care regarding SPT.

Strengths and limitations

This study has strengths and limitations that need to be addressed. A significant strength, enhancing credibility and transferability, was providing a clear context and thick descriptions of our results, where we thoroughly portrayed the women’s voices using quotations [ 35 ]. Further, our detailed account of the study context, data collection, and data analysis process facilitated the transferability of our study. Including three women born outside of Sweden added to the variety of the sample and thus improved credibility because qualitative research often overlooks immigrants' experiences. However, the migrant women spoke Swedish well enough to participate in an interview, indicating that they have been living in Sweden for some time and might be familiar with the healthcare system. Finally, the credibility and dependability of this study were also strengthened by the frequent use of interdisciplinary triangulation between the authors throughout data analysis and the writing process, as well as peer review at a research seminar.

A potential limitation was that this study may not have fully explored the situation of women with fourth-degree lacerations or those with lower education, as most participants had third-degree perineal lacerations and higher education. Further, we could not include non-binary persons and same-sex or single parents, which may be a weakness; consequently, future studies should focus on the under-represented participant groups and migrant women needing an interpreter. Additionally, all women responded voluntarily to the study invitation. Thus, our participants might be particularly outspoken about their problems or interested in raising their voices or experiences. However, they represented a variety of persistent SPT-related health problems of various severity, and some had been able to get access to medical help, whereas others had not. Additionally, our findings cohered to similar studies [ 12 , 17 ] covering shorter periods after the SPT, which may indicate that the experiences of the challenging search for needed help remain over time. Therefore, our findings may reflect other women’s experiences seeking care for SPT-related health problems and may be transferable to other women’s experiences with persistent health problems of a rare condition.

The data for this study was comprehensive and rich. Information power in qualitative research is an ongoing discussion, and the number of participants and their representativity can be seen as a limitation of credibility and transferability [ 71 , 72 ]. Graneheim, Lindgren and Lundman [ 36 ] argue that sample size should be determined by the study’s aim and the data’s quality so that variations in experiences can be captured. They do, therefore, not recommend a specific number of participants, but others do [ 71 ]. With this in mind, the authors believe that the women’s detailed descriptions of the included concepts and the extensive length of the conducted interviews enabled us to achieve sufficient information power based on the richness of the data [ 72 ].

By qualitatively exploring how women with persistent SPT-related health problems experienced their healthcare encounters, we interpreted that they faced a paradox of being reassured of normality by HCPs despite reporting sequelae symptoms. Thus, women’s needs for medical care, rehabilitation, and sick leave were largely neglected. Further, our study might indicate a structural problem within women’s postpartum healthcare, indicating that access to care depended on encountering a ‘key person’, a professional who acknowledged persistent problems as real symptoms. Access to quality care provided with a professional attitude was essential for the future well-being of women with persistent SPT-related health problems. Thus, it should not depend on meeting a single ‘key person’. Therefore, national guidelines for long-term postpartum care of persistent SPT-related health problems must be developed in Sweden. Additionally, to ensure that healthcare services meet the individual needs of women with persistent SPT-related health problems, it is crucial to consider arranging the organisation and availability of quality care for these women from a woman-centred perspective.

Availability of data and materials

The original recordings and transcripts from the current study are not publicly available due to securing the individual privacy and confidentiality of the participants. Data are available from the corresponding author upon reasonable request.

Abbreviations

Healthcare professionals

Interquartile range

Strategic Research Area Health Care Science

• Severe perineal trauma

Sexual and reproductive health and rights

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Acknowledgements

We want to thank the participating women for generously sharing their experiences.

Open access funding provided by Umea University. This work was supported by the Research Lift (SWE: Forskningslyftet) and Strategic Research Area Health Care Science (SFO-V), Umeå University. The funders had no specific role in the conceptualisation, design, data collection, analysis, publication decision, or manuscript preparation.

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Katharina Tjernström, Inger Lindberg & Margareta Persson

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KT: conceptualisation; data curation; formal analysis; investigation; methodology; validation; visualisation; writing - original draft; writing - review & editing. IL: conceptualisation; methodology; supervision; visualisation; writing - review & editing. MW: conceptualisation; methodology; supervision; visualisation; writing - review & editing. MP: conceptualisation; data curation; funding acquisition; methodology; project administration; supervision; visualisation; writing - review & editing. All authors read and approved the final manuscript.

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The Swedish Ethical Review Authority approved the study. An amendment to the original ethical approval of the research project to further explore the experiences of encounters with healthcare services was obtained (Dnr: 2020-035410908 and Dnr: 2022-02784-02). The study was undertaken in compliance with research ethics guidelines. All participation was voluntary, and participants received oral and written information about the study and provided written informed consent before the interviews. No interview questions were mandatory, and the women decided if and how detailed they wanted to share their experiences.

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12913_2024_11037_moesm1_esm.pdf.

Additional file 1. Semi-structured interview guide for individual interviews; contains interview questions aimed at highlighting the experience of everyday life and working life after suffering 3 rd or 4 th degree perineal laceration at childbirth (i.e., severe perineal trauma [SPT]).

Additional file 2. Consolidated criteria for reporting qualitative studies (COREQ): 32-item checklist.

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Tjernström, K., Lindberg, I., Wiklund, M. et al. Overlooked by the obstetric gaze – how women with persistent health problems due to severe perineal trauma experience encounters with healthcare services: a qualitative study. BMC Health Serv Res 24 , 610 (2024). https://doi.org/10.1186/s12913-024-11037-5

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What is Qualitative Data Analysis?

Understanding qualitative information analysis is important for researchers searching for to uncover nuanced insights from non-numerical statistics. By exploring qualitative statistics evaluation, you can still draw close its importance in studies, understand its methodologies, and determine while and the way to apply it successfully to extract meaningful insights from qualitative records.

The article goals to provide a complete manual to expertise qualitative records evaluation, masking its significance, methodologies, steps, advantages, disadvantages, and applications.

Table of Content

Understanding Qualitative Data Analysis

Importance of qualitative data analysis, steps to perform qualitative data analysis, 1. craft clear research questions, 2. gather rich customer insights, 3. organize and categorize data, 4. uncover themes and patterns : coding, 5. make hypotheses and validating, methodologies in qualitative data analysis, advantages of qualitative data analysis, disadvantages of qualitative data analysis, when qualitative data analysis is used, applications of qualitative data analysis.

Qualitative data analysis is the process of systematically examining and deciphering qualitative facts (such as textual content, pix, motion pictures, or observations) to discover patterns, themes, and meanings inside the statistics· Unlike quantitative statistics evaluation, which focuses on numerical measurements and statistical strategies, qualitative statistics analysis emphasizes know-how the context, nuances, and subjective views embedded inside the information.

Qualitative facts evaluation is crucial because it is going past the bloodless hard information and numbers to provide a richer expertise of why and the way things appear. Qualitative statistics analysis is important for numerous motives:

• Understanding Complexity and unveils the “Why” : Quantitative facts tells you “what” came about (e· g·, sales figures), however qualitative evaluation sheds light on the motives in the back of it (e·g·, consumer comments on product features).
• Contextual Insight : Numbers don’t exist in a vacuum. Qualitative information affords context to quantitative findings, making the bigger photo clearer· Imagine high customer churn – interviews would possibly monitor lacking functionalities or perplexing interfaces.
• Uncovers Emotions and Opinions: Qualitative records faucets into the human element· Surveys with open ended questions or awareness companies can display emotions, critiques, and motivations that can’t be captured by using numbers on my own.
• Informs Better Decisions: By understanding the “why” and the “how” at the back of customer behavior or employee sentiment, companies can make greater knowledgeable decisions about product improvement, advertising techniques, and internal techniques.
• Generates New Ideas : Qualitative analysis can spark clean thoughts and hypotheses· For example, via analyzing consumer interviews, commonplace subject matters may emerge that cause totally new product features.
• Complements Quantitative Data : While each facts sorts are precious, they paintings quality collectively· Imagine combining website site visitors records (quantitative) with person comments (qualitative) to apprehend user revel in on a particular webpage.

In essence, qualitative data evaluation bridges the gap among the what and the why, providing a nuanced know-how that empowers better choice making·

Qualitative data analysis process, follow the structure in below steps:

Qualitative information evaluation procedure, comply with the shape in underneath steps:

Before diving into evaluation, it is critical to outline clear and particular studies questions. These questions ought to articulate what you want to study from the data and manual your analysis towards actionable insights. For instance, asking “How do employees understand the organizational culture inside our agency?” helps makes a speciality of know-how personnel’ perceptions of the organizational subculture inside a selected business enterprise. By exploring employees’ perspectives, attitudes, and stories related to organizational tradition, researchers can find valuable insights into workplace dynamics, communication patterns, management patterns, and worker delight degrees.

There are numerous methods to acquire qualitative information, each offering specific insights into client perceptions and reviews.

• User Feedback: In-app surveys, app rankings, and social media feedback provide direct remarks from users approximately their studies with the products or services.
• In-Depth Interviews : One-on-one interviews allow for deeper exploration of particular topics and offer wealthy, special insights into individuals’ views and behaviors.
• Focus Groups : Facilitating group discussions allows the exploration of numerous viewpoints and permits individuals to construct upon every different’s ideas.
• Review Sites : Analyzing purchaser critiques on systems like Amazon, Yelp, or app shops can monitor not unusual pain points, pride levels, and areas for improvement.
• NPS Follow-Up Questions : Following up on Net Promoter Score (NPS) surveys with open-ended questions allows customers to elaborate on their rankings and provides qualitative context to quantitative ratings.

Efficient facts below is crucial for powerful analysis and interpretation.

• Centralize: Gather all qualitative statistics, along with recordings, notes, and transcripts, right into a valuable repository for smooth get admission to and control.
• Categorize through Research Question : Group facts primarily based at the specific studies questions they deal with. This organizational structure allows maintain consciousness in the course of analysis and guarantees that insights are aligned with the research objectives.

Coding is a scientific manner of assigning labels or categories to segments of qualitative statistics to uncover underlying issues and patterns.

• Theme Identification : Themes are overarching principles or ideas that emerge from the records· During coding, researchers perceive and label segments of statistics that relate to those themes, bearing in mind the identification of vital principles in the dataset.
• Pattern Detection : Patterns seek advice from relationships or connections between exceptional elements in the statistics. By reading coded segments, researchers can locate trends, repetitions, or cause-and-effect relationships, imparting deeper insights into patron perceptions and behaviors.

Based on the identified topics and styles, researchers can formulate hypotheses and draw conclusions about patron experiences and choices.

• Hypothesis Formulation: Hypotheses are tentative causes or predictions based on found styles within the information. Researchers formulate hypotheses to provide an explanation for why certain themes or styles emerge and make predictions approximately their effect on patron behavior.
• Validation : Researchers validate hypotheses by means of segmenting the facts based on one-of-a-kind standards (e.g., demographic elements, usage patterns) and analyzing variations or relationships inside the records. This procedure enables enhance the validity of findings and offers proof to assist conclusions drawn from qualitative evaluation.

There are five common methodologies utilized in Qualitative Data Analysis·

• Thematic Analysis : Thematic Analysis involves systematically figuring out and reading habitual subject matters or styles within qualitative statistics. Researchers begin with the aid of coding the facts, breaking it down into significant segments, and then categorizing these segments based on shared traits. Through iterative analysis, themes are advanced and refined, permitting researchers to benefit insight into the underlying phenomena being studied.
• Content Analysis: Content Analysis focuses on reading textual information to pick out and quantify particular styles or issues. Researchers code the statistics primarily based on predefined classes or subject matters, taking into consideration systematic agency and interpretation of the content. By analyzing how frequently positive themes occur and the way they’re represented inside the data, researchers can draw conclusions and insights relevant to their research objectives.
• Narrative Analysis: Narrative Analysis delves into the narrative or story within qualitative statistics, that specialize in its structure, content, and meaning. Researchers examine the narrative to understand its context and attitude, exploring how individuals assemble and speak their reports thru storytelling. By analyzing the nuances and intricacies of the narrative, researchers can find underlying issues and advantage a deeper know-how of the phenomena being studied.
• Grounded Theory : Grounded Theory is an iterative technique to growing and checking out theoretical frameworks primarily based on empirical facts. Researchers gather, code, and examine information without preconceived hypotheses, permitting theories to emerge from the information itself. Through constant assessment and theoretical sampling, researchers validate and refine theories, main to a deeper knowledge of the phenomenon under investigation.
• Phenomenological Analysis : Phenomenological Analysis objectives to discover and recognize the lived stories and views of people. Researchers analyze and interpret the meanings, essences, and systems of these reviews, figuring out not unusual topics and styles across individual debts. By immersing themselves in members’ subjective stories, researchers advantage perception into the underlying phenomena from the individuals’ perspectives, enriching our expertise of human behavior and phenomena.
• Richness and Depth: Qualitative records evaluation lets in researchers to discover complex phenomena intensive, shooting the richness and complexity of human stories, behaviors, and social processes.
• Flexibility : Qualitative techniques offer flexibility in statistics collection and evaluation, allowing researchers to conform their method based on emergent topics and evolving studies questions.
• Contextual Understanding: Qualitative evaluation presents perception into the context and meaning of information, helping researchers recognize the social, cultural, and historic elements that form human conduct and interactions.
• Subjective Perspectives : Qualitative methods allow researchers to explore subjective perspectives, beliefs, and reviews, offering a nuanced know-how of people’ mind, emotions, and motivations.
• Theory Generation : Qualitative information analysis can cause the generation of recent theories or hypotheses, as researchers uncover patterns, themes, and relationships in the records that might not were formerly recognized.
• Subjectivity: Qualitative records evaluation is inherently subjective, as interpretations can be stimulated with the aid of researchers’ biases, views, and preconceptions .
• Time-Intensive : Qualitative records analysis may be time-consuming, requiring giant data collection, transcription, coding, and interpretation.
• Generalizability: Findings from qualitative studies might not be effortlessly generalizable to larger populations, as the focus is often on know-how unique contexts and reviews in preference to making statistical inferences.
• Validity and Reliability : Ensuring the validity and reliability of qualitative findings may be difficult, as there are fewer standardized methods for assessing and establishing rigor in comparison to quantitative studies.
• Data Management : Managing and organizing qualitative information, together with transcripts, subject notes, and multimedia recordings, can be complicated and require careful documentation and garage.
• Exploratory Research: Qualitative records evaluation is nicely-suited for exploratory studies, wherein the aim is to generate hypotheses, theories, or insights into complex phenomena.
• Understanding Context : Qualitative techniques are precious for knowledge the context and which means of statistics, in particular in studies wherein social, cultural, or ancient factors are vital.
• Subjective Experiences : Qualitative evaluation is good for exploring subjective stories, beliefs, and views, providing a deeper knowledge of people’ mind, feelings, and behaviors.
• Complex Phenomena: Qualitative strategies are effective for studying complex phenomena that can not be effortlessly quantified or measured, allowing researchers to seize the richness and depth of human stories and interactions.
• Complementary to Quantitative Data: Qualitative information analysis can complement quantitative research by means of offering context, intensity, and insight into the meanings at the back of numerical statistics, enriching our knowledge of studies findings.
• Social Sciences: Qualitative information analysis is widely utilized in social sciences to apprehend human conduct, attitudes, and perceptions. Researchers employ qualitative methods to delve into the complexities of social interactions, cultural dynamics, and societal norms. By analyzing qualitative records which include interviews, observations, and textual resources, social scientists benefit insights into the elaborate nuances of human relationships, identity formation, and societal structures.
• Psychology : In psychology, qualitative data evaluation is instrumental in exploring and deciphering person reports, emotions, and motivations. Qualitative methods along with in-depth interviews, cognizance businesses, and narrative evaluation allow psychologists to delve deep into the subjective stories of individuals. This approach facilitates discover underlying meanings, beliefs, and emotions, dropping light on psychological processes, coping mechanisms, and personal narratives.
• Anthropology : Anthropologists use qualitative records evaluation to look at cultural practices, ideals, and social interactions inside various groups and societies. Through ethnographic research strategies such as player statement and interviews, anthropologists immerse themselves within the cultural contexts of different agencies. Qualitative analysis permits them to find the symbolic meanings, rituals, and social systems that form cultural identification and behavior.
• Qualitative Market Research : In the sphere of marketplace research, qualitative statistics analysis is vital for exploring consumer options, perceptions, and behaviors. Qualitative techniques which include consciousness groups, in-depth interviews, and ethnographic research permit marketplace researchers to gain a deeper understanding of customer motivations, choice-making methods, and logo perceptions· By analyzing qualitative facts, entrepreneurs can identify emerging developments, discover unmet wishes, and tell product development and advertising and marketing techniques.
• Healthcare: Qualitative statistics analysis plays a important function in healthcare studies via investigating patient experiences, delight, and healthcare practices. Researchers use qualitative techniques which includes interviews, observations, and patient narratives to explore the subjective reviews of people inside healthcare settings. Qualitative evaluation helps find affected person perspectives on healthcare services, treatment consequences, and pleasant of care, facilitating enhancements in patient-targeted care delivery and healthcare policy.

Qualitative data evaluation offers intensity, context, and know-how to investigate endeavors, enabling researchers to find wealthy insights and discover complicated phenomena via systematic examination of non-numerical information.

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What are the strengths and limitations to utilising creative methods in public and patient involvement in health and social care research? A qualitative systematic review

• Olivia R. Phillips 1 , 2   na1 ,
• Cerian Harries 2 , 3   na1 ,
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• Lauren B. Sherar 2 , 3 ,
• Veronica Varela-Mato 2 , 3 &
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There is increasing interest in using patient and public involvement (PPI) in research to improve the quality of healthcare. Ordinarily, traditional methods have been used such as interviews or focus groups. However, these methods tend to engage a similar demographic of people. Thus, creative methods are being developed to involve patients for whom traditional methods are inaccessible or non-engaging.

To determine the strengths and limitations to using creative PPI methods in health and social care research.

Electronic searches were conducted over five databases on 14th April 2023 (Web of Science, PubMed, ASSIA, CINAHL, Cochrane Library). Studies that involved traditional, non-creative PPI methods were excluded. Creative PPI methods were used to engage with people as research advisors, rather than study participants. Only primary data published in English from 2009 were accepted. Title, abstract and full text screening was undertaken by two independent reviewers before inductive thematic analysis was used to generate themes.

Twelve papers met the inclusion criteria. The creative methods used included songs, poems, drawings, photograph elicitation, drama performance, visualisations, social media, photography, prototype development, cultural animation, card sorting and persona development. Analysis identified four limitations and five strengths to the creative approaches. Limitations included the time and resource intensive nature of creative PPI, the lack of generalisation to wider populations and ethical issues. External factors, such as the lack of infrastructure to support creative PPI, also affected their implementation. Strengths included the disruption of power hierarchies and the creation of a safe space for people to express mundane or “taboo” topics. Creative methods are also engaging, inclusive of people who struggle to participate in traditional PPI and can also be cost and time efficient.

‘Creative PPI’ is an umbrella term encapsulating many different methods of engagement and there are strengths and limitations to each. The choice of which should be determined by the aims and requirements of the research, as well as the characteristics of the PPI group and practical limitations. Creative PPI can be advantageous over more traditional methods, however a hybrid approach could be considered to reap the benefits of both. Creative PPI methods are not widely used; however, this could change over time as PPI becomes embedded even more into research.

Plain English Summary

It is important that patients and public are included in the research process from initial brainstorming, through design to delivery. This is known as public and patient involvement (PPI). Their input means that research closely aligns with their wants and needs. Traditionally to get this input, interviews and group discussions are held, but this can exclude people who find these activities non-engaging or inaccessible, for example those with language challenges, learning disabilities or memory issues. Creative methods of PPI can overcome this. This is a broad term describing different (non-traditional) ways of engaging patients and public in research, such as through the use or art, animation or performance. This review investigated the reasons why creative approaches to PPI could be difficult (limitations) or helpful (strengths) in health and social care research. After searching 5 online databases, 12 studies were included in the review. PPI groups included adults, children and people with language and memory impairments. Creative methods included songs, poems, drawings, the use of photos and drama, visualisations, Facebook, creating prototypes, personas and card sorting. Limitations included the time, cost and effort associated with creative methods, the lack of application to other populations, ethical issues and buy-in from the wider research community. Strengths included the feeling of equality between academics and the public, creation of a safe space for people to express themselves, inclusivity, and that creative PPI can be cost and time efficient. Overall, this review suggests that creative PPI is worthwhile, however each method has its own strengths and limitations and the choice of which will depend on the research project, PPI group characteristics and other practical limitations, such as time and financial constraints.

Peer Review reports

Introduction

Patient and public involvement (PPI) is the term used to describe the partnership between patients (including caregivers, potential patients, healthcare users etc.) or the public (a community member with no known interest in the topic) with researchers. It describes research that is done “‘with’ or ‘by’ the public, rather than ‘to,’ ‘about’ or ‘for’ them” [ 1 ]. In 2009, it became a legislative requirement for certain health and social care organisations to include patients, families, carers and communities in not only the planning of health and social care services, but the commissioning, delivery and evaluation of them too [ 2 ]. For example, funding applications for the National Institute of Health and Care Research (NIHR), a UK funding body, mandates a demonstration of how researchers plan to include patients/service users, the public and carers at each stage of the project [ 3 ]. However, this should not simply be a tokenistic, tick-box exercise. PPI should help formulate initial ideas and should be an instrumental, continuous part of the research process. Input from PPI can provide unique insights not yet considered and can ensure that research and health services are closely aligned to the needs and requirements of service users PPI also generally makes research more relevant with clearer outcomes and impacts [ 4 ]. Although this review refers to both patients and the public using the umbrella term ‘PPI’, it is important to acknowledge that these are two different groups with different motivations, needs and interests when it comes to health research and service delivery [ 5 ].

Despite continuing recognition of the need of PPI to improve quality of healthcare, researchers have also recognised that there is no ‘one size fits all’ method for involving patients [ 4 ]. Traditionally, PPI methods invite people to take part in interviews or focus groups to facilitate discussion, or surveys and questionnaires. However, these can sometimes be inaccessible or non-engaging for certain populations. For example, someone with communication difficulties may find it difficult to engage in focus groups or interviews. If individuals lack the appropriate skills to interact in these types of scenarios, they cannot take advantage of the participation opportunities it can provide [ 6 ]. Creative methods, however, aim to resolve these issues. These are a relatively new concept whereby researchers use creative methods (e.g., artwork, animations, Lego), to make PPI more accessible and engaging for those whose voices would otherwise go unheard. They ensure that all populations can engage in research, regardless of their background or skills. Seminal work has previously been conducted in this area, which brought to light the use of creative methodologies in research. Leavy (2008) [ 7 ] discussed how traditional interviews had limits on what could be expressed due to their sterile, jargon-filled and formulaic structure, read by only a few specialised academics. It was this that called for more creative approaches, which included narrative enquiry, fiction-based research, poetry, music, dance, art, theatre, film and visual art. These practices, which can be used in any stage of the research cycle, supported greater empathy, self-reflection and longer-lasting learning experiences compared to interviews [ 7 ]. They also pushed traditional academic boundaries, which made the research accessible not only to researchers, but the public too. Leavy explains that there are similarities between arts-based approaches and scientific approaches: both attempts to investigate what it means to be human through exploration, and used together, these complimentary approaches can progress our understanding of the human experience [ 7 ]. Further, it is important to acknowledge the parallels and nuances between creative and inclusive methods of PPI. Although creative methods aim to be inclusive (this should underlie any PPI activity, whether creative or not), they do not incorporate all types of accessible, inclusive methodologies e.g., using sign language for people with hearing impairments or audio recordings for people who cannot read. Given that there was not enough scope to include an evaluation of all possible inclusive methodologies, this review will focus on creative methods of PPI only.

We aimed to conduct a qualitative systematic review to highlight the strengths of creative PPI in health and social care research, as well as the limitations, which might act as a barrier to their implementation. A qualitative systematic review “brings together research on a topic, systematically searching for research evidence from primary qualitative studies and drawing the findings together” [ 8 ]. This review can then advise researchers of the best practices when designing PPI.

Public involvement

The PHIRST-LIGHT Public Advisory Group (PAG) consists of a team of experienced public contributors with a diverse range of characteristics from across the UK. The PAG was involved in the initial question setting and study design for this review.

Search strategy

For the purpose of this review, the JBI approach for conducting qualitative systematic reviews was followed [ 9 ]. The search terms were (“creativ*” OR “innovat*” OR “authentic” OR “original” OR “inclu*”) AND (“public and patient involvement” OR “patient and public involvement” OR “public and patient involvement and engagement” OR “patient and public involvement and engagement” OR “PPI” OR “PPIE” OR “co-produc*” OR “co-creat*” OR “co-design*” OR “cooperat*” OR “co-operat*”). This search string was modified according to the requirements of each database. Papers were filtered by title, abstract and keywords (see Additional file 1 for search strings). The databases searched included Web of Science (WoS), PubMed, ASSIA and CINAHL. The Cochrane Library was also searched to identify relevant reviews which could lead to the identification of primary research. The search was conducted on 14/04/23. As our aim was to report on the use of creative PPI in research, rather than more generic public engagement, we used electronic databases of scholarly peer-reviewed literature, which represent a wide range of recognised databases. These identified studies published in general international journals (WoS, PubMed), those in social sciences journals (ASSIA), those in nursing and allied health journals (CINAHL), and trials of interventions (Cochrane Library).

Inclusion criteria

Only full-text, English language, primary research papers from 2009 to 2023 were included. This was the chosen timeframe as in 2009 the Health and Social Reform Act made it mandatory for certain Health and Social Care organisations to involve the public and patients in planning, delivering, and evaluating services [ 2 ]. Only creative methods of PPI were accepted, rather than traditional methods, such as interviews or focus groups. For the purposes of this paper, creative PPI included creative art or arts-based approaches (e.g., e.g. stories, songs, drama, drawing, painting, poetry, photography) to enhance engagement. Titles were related to health and social care and the creative PPI was used to engage with people as research advisors, not as study participants. Meta-analyses, conference abstracts, book chapters, commentaries and reviews were excluded. There were no limits concerning study location or the demographic characteristics of the PPI groups. Only qualitative data were accepted.

Quality appraisal

Quality appraisal using the Critical Appraisal Skills Programme (CASP) checklist [ 10 ] was conducted by the primary authors (ORP and CH). This was done independently, and discrepancies were discussed and resolved. If a consensus could not be reached, a third independent reviewer was consulted (JRM). The full list of quality appraisal questions can be found in Additional file 2 .

Data extraction

ORP extracted the study characteristics and a subset of these were checked by CH. Discrepancies were discussed and amendments made. Extracted data included author, title, location, year of publication, year study was carried out, research question/aim, creative methods used, number of participants, mean age, gender, ethnicity of participants, setting, limitations and strengths of creative PPI and main findings.

Data analysis

The included studies were analysed using inductive thematic analysis [ 11 ], where themes were determined by the data. The familiarisation stage took place during full-text reading of the included articles. Anything identified as a strength or limitation to creative PPI methods was extracted verbatim as an initial code and inputted into the data extraction Excel sheet. Similar codes were sorted into broader themes, either under ‘strengths’ or ‘limitations’ and reviewed. Themes were then assigned a name according to the codes.

The search yielded 9978 titles across the 5 databases: Web of Science (1480 results), PubMed (94 results), ASSIA (2454 results), CINAHL (5948 results) and Cochrane Library (2 results), resulting in 8553 different studies after deduplication. ORP and CH independently screened their titles and abstracts, excluding those that did not meet the criteria. After assessment, 12 studies were included (see Fig.  1 ).

PRISMA flowchart of the study selection process

Study characteristics

The included studies were published between 2018 and 2022. Seven were conducted in the UK [ 12 , 14 , 15 , 17 , 18 , 19 , 23 ], two in Canada [ 21 , 22 ], one in Australia [ 13 ], one in Norway [ 16 ] and one in Ireland [ 20 ]. The PPI activities occurred across various settings, including a school [ 12 ], social club [ 12 ], hospital [ 17 ], university [ 22 ], theatre [ 19 ], hotel [ 20 ], or online [ 15 , 21 ], however this information was omitted in 5 studies [ 13 , 14 , 16 , 18 , 23 ]. The number of people attending the PPI sessions varied, ranging from 6 to 289, however the majority (ten studies) had less than 70 participants [ 13 , 14 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 ]. Seven studies did not provide information on the age or gender of the PPI groups. Of those that did, ages ranged from 8 to 76 and were mostly female. The ethnicities of the PPI group members were also rarely recorded (see Additional file 3 for data extraction table).

Types of creative methods

The type of creative methods used to engage the PPI groups were varied. These included songs, poems, drawings, photograph elicitation, drama performance, visualisations, Facebook, photography, prototype development, cultural animation, card sorting and creating personas (see Table  1 ). These were sometimes accompanied by traditional methods of PPI such as interviews and focus group discussions.

The 12 included studies were all deemed to be of good methodological quality, with scores ranging from 6/10 to 10/10 with the CASP critical appraisal tool [ 10 ] (Table  2 ).

Thematic analysis

Analysis identified four limitations and five strengths to creative PPI (see Fig.  2 ). Limitations included the time and resource intensity of creative PPI methods, its lack of generalisation, ethical issues and external factors. Strengths included the disruption of power hierarchies, the engaging and inclusive nature of the methods and their long-term cost and time efficiency. Creative PPI methods also allowed mundane and “taboo” topics to be discussed within a safe space.

Theme map of strengths and limitations

Limitations of creative PPI

Creative ppi methods are time and resource intensive.

The time and resource intensive nature of creative PPI methods is a limitation, most notably for the persona-scenario methodology. Valaitis et al. [ 22 ] used 14 persona-scenario workshops with 70 participants to co-design a healthcare intervention, which aimed to promote optimal aging in Canada. Using the persona method, pairs composed of patients, healthcare providers, community service providers and volunteers developed a fictional character which they believed represented an ‘end-user’ of the healthcare intervention. Due to the depth and richness of the data produced the authors reported that it was time consuming to analyse. Further, they commented that the amount of information was difficult to disseminate to scientific leads and present at team meetings. Additionally, to ensure the production of high-quality data, to probe for details and lead group discussion there was a need for highly skilled facilitators. The resource intensive nature of the creative co-production was also noted in a study using the persona scenario and creative worksheets to develop a prototype decision support tool for individuals with malignant pleural effusion [ 17 ]. With approximately 50 people, this was also likely to yield a high volume of data to consider.

To prepare materials for populations who cannot engage in traditional methods of PPI was also timely. Kearns et al. [ 18 ] developed a feedback questionnaire for people with aphasia to evaluate ICT-delivered rehabilitation. To ensure people could participate effectively, the resources used during the workshops, such as PowerPoints, online images and photographs, had to be aphasia-accessible, which was labour and time intensive. The author warned that this time commitment should not be underestimated.

There are further practical limitations to implementing creative PPI, such as the costs of materials for activities as well as hiring a space for workshops. For example, the included studies in this review utilised pens, paper, worksheets, laptops, arts and craft supplies and magazines and took place in venues such as universities, a social club, and a hotel. Further, although not limited to creative PPI methods exclusively but rather most studies involving the public, a financial incentive was often offered for participation, as well as food, parking, transport and accommodation [ 21 , 22 ].

Creative PPI lacks generalisation

Another barrier to the use of creative PPI methods in health and social care research was the individual nature of its output. Those who participate, usually small in number, produce unique creative outputs specific to their own experiences, opinions and location. Craven et al. [ 13 ], used arts-based visualisations to develop a toolbox for adults with mental health difficulties. They commented, “such an approach might still not be worthwhile”, as the visualisations were individualised and highly personal. This indicates that the output may fail to meet the needs of its end-users. Further, these creative PPI groups were based in certain geographical regions such as Stoke-on-Trent [ 19 ] Sheffield [ 23 ], South Wales [ 12 ] or Ireland [ 20 ], which limits the extent the findings can be applied to wider populations, even within the same area due to individual nuances. Further, the study by Galler et al. [ 16 ], is specific to the Norwegian context and even then, maybe only a sub-group of the Norwegian population as the sample used was of higher socioeconomic status.

However, Grindell et al. [ 17 ], who used persona scenarios, creative worksheets and prototype development, pointed out that the purpose of this type of research is to improve a certain place, rather than apply findings across other populations and locations. Individualised output may, therefore, only be a limitation to research wanting to conduct PPI on a large scale.

If, however, greater generalisation within PPI is deemed necessary, then social media may offer a resolution. Fedorowicz et al. [ 15 ], used Facebook to gain feedback from the public on the use of video-recording methodology for an upcoming project. This had the benefit of including a more diverse range of people (289 people joined the closed group), who were spread geographically around the UK, as well as seven people from overseas.

Creative PPI has ethical issues

As with other research, ethical issues must be taken into consideration. Due to the nature of creative approaches, as well as the personal effort put into them, people often want to be recognised for their work. However, this compromises principles so heavily instilled in research such as anonymity and confidentiality. With the aim of exploring issues related to health and well-being in a town in South Wales, Byrne et al. [ 12 ], asked year 4/5 and year 10 pupils to create poems, songs, drawings and photographs. Community members also created a performance, mainly of monologues, to explore how poverty and inequalities are dealt with. Byrne noted the risks of these arts-based approaches, that being the possibility of over-disclosure and consequent emotional distress, as well as people’s desire to be named for their work. On one hand, the anonymity reduces the sense of ownership of the output as it does not portray a particular individual’s lived experience anymore. On the other hand, however, it could promote a more honest account of lived experience. Supporting this, Webber et al. [ 23 ], who used the persona method to co-design a back pain educational resource prototype, claimed that the anonymity provided by this creative technique allowed individuals to externalise and anonymise their own personal experience, thus creating a more authentic and genuine resource for future users. This implies that anonymity can be both a limitation and strength here.

The use of creative PPI methods is impeded by external factors

Despite the above limitations influencing the implementation of creative PPI techniques, perhaps the most influential is that creative methodologies are simply not mainstream [ 19 ]. This could be linked to the issues above, like time and resource intensity, generalisation and ethical issues but it is also likely to involve more systemic factors within the research community. Micsinszki et al. [ 21 ], who co-designed a hub for the health and well-being of vulnerable populations, commented that there is insufficient infrastructure to conduct meaningful co-design as well as a dominant medical model. Through a more holistic lens, there are “sociopolitical environments that privilege individualism over collectivism, self-sufficiency over collaboration, and scientific expertise over other ways of knowing based on lived experience” [ 21 ]. This, it could be suggested, renders creative co-design methodologies, which are based on the foundations of collectivism, collaboration and imagination an invalid technique in the research field, which is heavily dominated by more scientific methods offering reproducibility, objectivity and reliability.

Although we acknowledge that creative PPI techniques are not always appropriate, it may be that their main limitation is the lack of awareness of these methods or lack of willingness to use them. Further, there is always the risk that PPI, despite being a mandatory part of research, is used in a tokenistic or tick-box fashion [ 20 ], without considering the contribution that meaningful PPI could make to enhancing the research. It may be that PPI, let alone creative PPI, is not at the forefront of researchers’ minds when planning research.

Strengths of creative PPI

Creative ppi disrupts power hierarchies.

One of the main strengths of creative PPI techniques, cited most frequently in the included literature, was that they disrupt traditional power hierarchies [ 12 , 13 , 17 , 19 , 23 ]. For example, the use of theatre performance blurred the lines between professional and lay roles between the community and policy makers [ 12 ]. Individuals created a monologue to portray how poverty and inequality impact daily life and presented this to representatives of the National Assembly of Wales, Welsh Government, the Local Authority, Arts Council and Westminster. Byrne et al. [ 12 ], states how this medium allowed the community to engage with the people who make decisions about their lives in an environment of respect and understanding, where the hierarchies are not as visible as in other settings, e.g., political surgeries. Creative PPI methods have also removed traditional power hierarchies between researchers and adolescents. Cook et al. [ 13 ], used arts-based approaches to explore adolescents’ ideas about the “perfect” condom. They utilised the “Life Happens” resource, where adolescents drew and then decorated a person with their thoughts about sexual relationships, not too dissimilar from the persona-scenario method. This was then combined with hypothetical scenarios about sexuality. A condom-mapping exercise was then implemented, where groups shared the characteristics that make a condom “perfect” on large pieces of paper. Cook et al. [ 13 ], noted that usually power imbalances make it difficult to elicit information from adolescents, however these power imbalances were reduced due to the use of creative co-design techniques.

The same reduction in power hierarchies was noted by Grindell et al. [ 17 ], who used the person-scenario method and creative worksheets with individuals with malignant pleural effusion. This was with the aim of developing a prototype of a decision support tool for patients to help with treatment options. Although this process involved a variety of stakeholders, such as patients, carers and healthcare professionals, creative co-design was cited as a mechanism that worked to reduce power imbalances – a limitation of more traditional methods of research. Creative co-design blurred boundaries between end-users and clinical staff and enabled the sharing of ideas from multiple, valuable perspectives, meaning the prototype was able to suit user needs whilst addressing clinical problems.

Similarly, a specific creative method named cultural animation was also cited to dissolve hierarchies and encourage equal contributions from participants. Within this arts-based approach, Keleman et al. [ 19 ], explored the concept of “good health” with individuals from Stoke-on Trent. Members of the group created art installations using ribbons, buttons, cardboard and straws to depict their idea of a “healthy community”, which was accompanied by a poem. They also created a 3D Facebook page and produced another poem or song addressing the government to communicate their version of a “picture of health”. Public participants said that they found the process empowering, honest, democratic, valuable and practical.

This dissolving of hierarchies and levelling of power is beneficial as it increases the sense of ownership experienced by the creators/producers of the output [ 12 , 17 , 23 ]. This is advantageous as it has been suggested to improve its quality [ 23 ].

Creative PPI allows the unsayable to be said

Creative PPI fosters a safe space for mundane or taboo topics to be shared, which may be difficult to communicate using traditional methods of PPI. For example, the hypothetical nature of condom mapping and persona-scenarios meant that adolescents could discuss a personal topic without fear of discrimination, judgement or personal disclosure [ 13 ]. The safe space allowed a greater volume of ideas to be generated amongst peers where they might not have otherwise. Similarly, Webber et al. [ 23 ], , who used the persona method to co-design the prototype back pain educational resource, also noted how this method creates anonymity whilst allowing people the opportunity to externalise personal experiences, thoughts and feelings. Other creative methods were also used, such as drawing, collaging, role play and creating mood boards. A cardboard cube (labelled a “magic box”) was used to symbolise a physical representation of their final prototype. These creative methods levelled the playing field and made personal experiences accessible in a safe, open environment that fostered trust, as well as understanding from the researchers.

It is not only sensitive subjects that were made easier to articulate through creative PPI. The communication of mundane everyday experiences were also facilitated, which were deemed typically ‘unsayable’. This was specifically given in the context of describing intangible aspects of everyday health and wellbeing [ 11 ]. Graphic designers can also be used to visually represent the outputs of creative PPI. These captured the movement and fluidity of people and well as the relationships between them - things that cannot be spoken but can be depicted [ 21 ].

Creative PPI methods are inclusive

Another strength of creative PPI was that it is inclusive and accessible [ 17 , 19 , 21 ]. The safe space it fosters, as well as the dismantling of hierarchies, welcomed people from a diverse range of backgrounds and provided equal opportunities [ 21 ], especially for those with communication and memory difficulties who might be otherwise excluded from PPI. Kelemen et al. [ 19 ], who used creative methods to explore health and well-being in Stoke-on-Trent, discussed how people from different backgrounds came together and connected, discussed and reached a consensus over a topic which evoked strong emotions, that they all have in common. Individuals said that the techniques used “sets people to open up as they are not overwhelmed by words”. Similarly, creative activities, such as the persona method, have been stated to allow people to express themselves in an inclusive environment using a common language. Kearns et al. [ 18 ], who used aphasia-accessible material to develop a questionnaire with aphasic individuals, described how they felt comfortable in contributing to workshops (although this material was time-consuming to make, see ‘Limitations of creative PPI’ ).

Despite the general inclusivity of creative PPI, it can also be exclusive, particularly if online mediums are used. Fedorowicz et al. [ 15 ], used Facebook to create a PPI group, and although this may rectify previous drawbacks about lack of generalisation of creative methods (as Facebook can reach a greater number of people, globally), it excluded those who are not digitally active or have limited internet access or knowledge of technology. Online methods have other issues too. Maintaining the online group was cited as challenging and the volume of responses required researchers to interact outside of their working hours. Despite this, online methods like Facebook are very accessible for people who are physically disabled.

Creative PPI methods are engaging

The process of creative PPI is typically more engaging and produces more colourful data than traditional methods [ 13 ]. Individuals are permitted and encouraged to explore a creative self [ 19 ], which can lead to the exploration of new ideas and an overall increased enjoyment of the process. This increased engagement is particularly beneficial for younger PPI groups. For example, to involve children in the development of health food products, Galler et al. [ 16 ] asked 9-12-year-olds to take photos of their food and present it to other children in a “show and tell” fashion. They then created a newspaper article describing a new healthy snack. In this creative focus group, children were given lab coats to further their identity as inventors. Galler et al. [ 16 ], notes that the methods were highly engaging and facilitated teamwork and group learning. This collaborative nature of problem-solving was also observed in adults who used personas and creative worksheets to develop the resource for lower back pain [ 23 ]. Dementia patients too have been reported to enjoy the creative and informal approach to idea generation [ 20 ].

The use of cultural animation allowed people to connect with each other in a way that traditional methods do not [ 19 , 21 ]. These connections were held in place by boundary objects, such as ribbons, buttons, fabric and picture frames, which symbolised a shared meaning between people and an exchange of knowledge and emotion. Asking groups to create an art installation using these objects further fostered teamwork and collaboration, both at an individual and collective level. The exploration of a creative self increased energy levels and encouraged productive discussions and problem-solving [ 19 ]. Objects also encouraged a solution-focused approach and permitted people to think beyond their usual everyday scope [ 17 ]. They also allowed facilitators to probe deeper about the greater meanings carried by the object, which acted as a metaphor [ 21 ].

From the researcher’s point of view, co-creative methods gave rise to ideas they might not have initially considered. Valaitis et al. [ 22 ], found that over 40% of the creative outputs were novel ideas brought to light by patients, healthcare providers/community care providers, community service providers and volunteers. One researcher commented, “It [the creative methods] took me on a journey, in a way that when we do other pieces of research it can feel disconnected” [ 23 ]. Another researcher also stated they could not return to the way they used to do research, as they have learnt so much about their own health and community and how they are perceived [ 19 ]. This demonstrates that creative processes not only benefit the project outcomes and the PPI group, but also facilitators and researchers. However, although engaging, creative methods have been criticised for not demonstrating academic rigour [ 17 ]. Moreover, creative PPI may also be exclusive to people who do not like or enjoy creative activities.

Creative PPI methods are cost and time efficient

Creative PPI workshops can often produce output that is visible and tangible. This can save time and money in the long run as the output is either ready to be implemented in a healthcare setting or a first iteration has already been developed. This may also offset the time and costs it takes to implement creative PPI. For example, the prototype of the decision support tool for people with malignant pleural effusion was developed using personas and creative worksheets. The end result was two tangible prototypes to drive the initial idea forward as something to be used in practice [ 17 ]. The use of creative co-design in this case saved clinician time as well as the time it would take to develop this product without the help of its end-users. In the development of this particular prototype, analysis was iterative and informed the next stage of development, which again saved time. The same applies for the feedback questionnaire for the assessment of ICT delivered aphasia rehabilitation. The co-created questionnaire, designed with people with aphasia, was ready to be used in practice [ 18 ]. This suggests that to overcome time and resource barriers to creative PPI, researchers should aim for it to be engaging whilst also producing output.

That useable products are generated during creative workshops signals to participating patients and public members that they have been listened to and their thoughts and opinions acted upon [ 23 ]. For example, the development of the back pain resource based on patient experiences implies that their suggestions were valid and valuable. Further, those who participated in the cultural animation workshop reported that the process visualises change, and that it already feels as though the process of change has started [ 19 ].

The most cost and time efficient method of creative PPI in this review is most likely the use of Facebook to gather feedback on project methodology [ 15 ]. Although there were drawbacks to this, researchers could involve more people from a range of geographical areas at little to no cost. Feedback was instantaneous and no training was required. From the perspective of the PPI group, they could interact however much or little they wish with no time commitment.

This systematic review identified four limitations and five strengths to the use of creative PPI in health and social care research. Creative PPI is time and resource intensive, can raise ethical issues and lacks generalisability. It is also not accepted by the mainstream. These factors may act as barriers to the implementation of creative PPI. However, creative PPI disrupts traditional power hierarchies and creates a safe space for taboo or mundane topics. It is also engaging, inclusive and can be time and cost efficient in the long term.

Something that became apparent during data analysis was that these are not blanket strengths and limitations of creative PPI as a whole. The umbrella term ‘creative PPI’ is broad and encapsulates a wide range of activities, ranging from music and poems to prototype development and persona-scenarios, to more simplistic things like the use of sticky notes and ordering cards. Many different activities can be deemed ‘creative’ and the strengths and limitations of one does not necessarily apply to another. For example, cultural animation takes greater effort to prepare than the use of sticky notes and sorting cards, and the use of Facebook is cheaper and wider reaching than persona development. Researchers should use their discretion and weigh up the benefits and drawbacks of each method to decide on a technique which suits the project. What might be a limitation to creative PPI in one project may not be in another. In some cases, creative PPI may not be suitable at all.

Furthermore, the choice of creative PPI method also depends on the needs and characteristics of the PPI group. Children, adults and people living with dementia or language difficulties all have different engagement needs and capabilities. This indicates that creative PPI is not one size fits all and that the most appropriate method will change depending on the composition of the group. The choice of method will also be determined by the constraints of the research project, namely time, money and the research aim. For example, if there are time constraints, then a method which yields a lot of data and requires a lot of preparation may not be appropriate. If generalisation is important, then an online method is more suitable. Together this indicates that the choice of creative PPI method is highly individualised and dependent on multiple factors.

Although the limitations discussed in this review apply to creative PPI, they are not exclusive to creative PPI. Ethical issues are a consideration within general PPI research, especially when working with more vulnerable populations, such as children or adults living with a disability. It can also be the case that traditional PPI methods lack generalisability, as people who volunteer to be part of such a group are more likely be older, middle class and retired [ 24 ]. Most research is vulnerable to this type of bias, however, it is worth noting that generalisation is not always a goal and research remains valid and meaningful in its absence. Although online methods may somewhat combat issues related to generalisability, these methods still exclude people who do not have access to the internet/technology or who choose not to use it, implying that online PPI methods may not be wholly representative of the general population. Saying this, however, the accessibility of creative PPI techniques differs from person to person, and for some, online mediums may be more accessible (for example for those with a physical disability), and for others, this might be face-to-face. To combat this, a range of methods should be implemented. Planning multiple focus group and interviews for traditional PPI is also time and resource intensive, however the extra resources required to make this creative may be even greater. Although, the rich data provided may be worth the preparation and analysis time, which is also likely to depend on the number of participants and workshop sessions required. PPI, not just creative PPI, often requires the provision of a financial incentive, refreshments, parking and accommodation, which increase costs. These, however, are imperative and non-negotiable, as they increase the accessibility of research, especially to minority and lower-income groups less likely to participate. Adequate funding is also important for co-design studies where repeated engagement is required. One barrier to implementation, which appears to be exclusive to creative methods, however, is that creative methods are not mainstream. This cannot be said for traditional PPI as this is often a mandatory part of research applications.

Regarding the strengths of creative PPI, it could be argued that most appear to be exclusive to creative methodologies. These are inclusive by nature as multiple approaches can be taken to evoke ideas from different populations - approaches that do not necessarily rely on verbal or written communication like interviews and focus groups do. Given the anonymity provided by some creative methods, such as personas, people may be more likely to discuss their personal experiences under the guise of a general end-user, which might be more difficult to maintain when an interviewer is asking an individual questions directly. Additionally, creative methods are by nature more engaging and interactive than traditional methods, although this is a blanket statement and there may be people who find the question-and-answer/group discussion format more engaging. Creative methods have also been cited to eliminate power imbalances which exist in traditional research [ 12 , 13 , 17 , 19 , 23 ]. These imbalances exist between researchers and policy makers and adolescents, adults and the community. Lastly, although this may occur to a greater extent in creative methods like prototype development, it could be suggested that PPI in general – regardless of whether it is creative - is more time and cost efficient in the long-term than not using any PPI to guide or refine the research process. It must be noted that these are observations based on the literature. To be certain these differences exist between creative and traditional methods of PPI, direct empirical evaluation of both should be conducted.

To the best of our knowledge, this is the first review to identify the strengths and limitations to creative PPI, however, similar literature has identified barriers and facilitators to PPI in general. In the context of clinical trials, recruitment difficulties were cited as a barrier, as well as finding public contributors who were free during work/school hours. Trial managers reported finding group dynamics difficult to manage and the academic environment also made some public contributors feel nervous and lacking confidence to speak. Facilitators, however, included the shared ownership of the research – something that has been identified in the current review too. In addition, planning and the provision of knowledge, information and communication were also identified as facilitators [ 25 ]. Other research on the barriers to meaningful PPI in trial oversight committees included trialist confusion or scepticism over the PPI role and the difficulties in finding PPI members who had a basic understanding of research [ 26 ]. However, it could be argued that this is not representative of the average patient or public member. The formality of oversight meetings and the technical language used also acted as a barrier, which may imply that the informal nature of creative methods and its lack of dependency on literacy skills could overcome this. Further, a review of 42 reviews on PPI in health and social care identified financial compensation, resources, training and general support as necessary to conduct PPI, much like in the current review where the resource intensiveness of creative PPI was identified as a limitation. However, others were identified too, such as recruitment and representativeness of public contributors [ 27 ]. Like in the current review, power imbalances were also noted, however this was included as both a barrier and facilitator. Collaboration seemed to diminish hierarchies but not always, as sometimes these imbalances remained between public contributors and healthcare staff, described as a ‘them and us’ culture [ 27 ]. Although these studies compliment the findings of the current review, a direct comparison cannot be made as they do not concern creative methods. However, it does suggest that some strengths and weaknesses are shared between creative and traditional methods of PPI.

Strengths and limitations of this review

Although a general definition of creative PPI exists, it was up to our discretion to decide exactly which activities were deemed as such for this review. For example, we included sorting cards, the use of interactive whiteboards and sticky notes. Other researchers may have a more or less stringent criteria. However, two reviewers were involved in this decision which aids the reliability of the included articles. Further, it may be that some of the strengths and limitations cannot fully be attributed to the creative nature of the PPI process, but rather their co-created nature, however this is hard to disentangle as the included papers involved both these aspects.

During screening, it was difficult to decide whether the article was utilising creative qualitative methodology or creative PPI , as it was often not explicitly labelled as such. Regardless, both approaches involved the public/patients refining a healthcare product/service. This implies that if this review were to be replicated, others may do it differently. This may call for greater standardisation in the reporting of the public’s involvement in research. For example, the NIHR outlines different approaches to PPI, namely “consultation”, “collaboration”, “co-production” and “user-controlled”, which each signify an increased level of public power and influence [ 28 ]. Papers with elements of PPI could use these labels to clarify the extent of public involvement, or even explicitly state that there was no PPI. Further, given our decision to include only scholarly peer-reviewed literature, it is possible that data were missed within the grey literature. Similarly, the literature search will not have identified all papers relating to different types of accessible inclusion. However, the intent of the review was to focus solely on those within the definition of creative.

This review fills a gap in the literature and helps circulate and promote the concept of creative PPI. Each stage of this review, namely screening and quality appraisal, was conducted by two independent reviewers. However, four full texts could not be accessed during the full text reading stage, meaning there are missing data that could have altered or contributed to the findings of this review.

Research recommendations

Given that creative PPI can require effort to prepare, perform and analyse, sufficient time and funding should be allocated in the research protocol to enable meaningful and continuous PPI. This is worthwhile as PPI can significantly change the research output so that it aligns closely with the needs of the group it is to benefit. Researchers should also consider prototype development as a creative PPI activity as this might reduce future time/resource constraints. Shifting from a top-down approach within research to a bottom-up can be advantageous to all stakeholders and can help move creative PPI towards the mainstream. This, however, is the collective responsibility of funding bodies, universities and researchers, as well as committees who approve research bids.

A few of the included studies used creative techniques alongside traditional methods, such as interviews, which could also be used as a hybrid method of PPI, perhaps by researchers who are unfamiliar with creative techniques or to those who wish to reap the benefits of both. Often the characteristics of the PPI group were not included, including age, gender and ethnicity. It would be useful to include such information to assess how representative the PPI group is of the population of interest.

Creative PPI is a relatively novel approach of engaging the public and patients in research and it has both advantages and disadvantages compared to more traditional methods. There are many approaches to implementing creative PPI and the choice of technique will be unique to each piece of research and is reliant on several factors. These include the age and ability of the PPI group as well as the resource limitations of the project. Each method has benefits and drawbacks, which should be considered at the protocol-writing stage. However, given adequate funding, time and planning, creative PPI is a worthwhile and engaging method of generating ideas with end-users of research – ideas which may not be otherwise generated using traditional methods.

Data availability

No datasets were generated or analysed during the current study.

Abbreviations

Critical Appraisal Skills Programme

The Joanna Briggs Institute

National Institute of Health and Care Research

Public Advisory Group

Public and Patient Involvement

Web of Science

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Acknowledgements

With thanks to the PHIRST-LIGHT public advisory group and consortium for their thoughts and contributions to the design of this work.

The research team is supported by a National Institute for Health and Care Research grant (PHIRST-LIGHT Reference NIHR 135190).

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Olivia R. Phillips and Cerian Harries share joint first authorship.

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Nottingham Centre for Public Health and Epidemiology, Lifespan and Population Health, School of Medicine, University of Nottingham, Clinical Sciences Building, City Hospital Campus, Hucknall Road, Nottingham, NG5 1PB, UK

Olivia R. Phillips, Jo Leonardi-Bee, Holly Knight & Joanne R. Morling

National Institute for Health and Care Research (NIHR) PHIRST-LIGHT, Nottingham, UK

Olivia R. Phillips, Cerian Harries, Jo Leonardi-Bee, Holly Knight, Lauren B. Sherar, Veronica Varela-Mato & Joanne R. Morling

School of Sport, Exercise and Health Sciences, Loughborough University, Epinal Way, Loughborough, Leicestershire, LE11 3TU, UK

Cerian Harries, Lauren B. Sherar & Veronica Varela-Mato

Nottingham Centre for Evidence Based Healthcare, School of Medicine, University of Nottingham, Nottingham, UK

Jo Leonardi-Bee

NIHR Nottingham Biomedical Research Centre (BRC), Nottingham University Hospitals NHS Trust, University of Nottingham, Nottingham, NG7 2UH, UK

Joanne R. Morling

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Author contributions: study design: ORP, CH, JRM, JLB, HK, LBS, VVM, literature searching and screening: ORP, CH, JRM, data curation: ORP, CH, analysis: ORP, CH, JRM, manuscript draft: ORP, CH, JRM, Plain English Summary: ORP, manuscript critical review and editing: ORP, CH, JRM, JLB, HK, LBS, VVM.

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Phillips, O.R., Harries, C., Leonardi-Bee, J. et al. What are the strengths and limitations to utilising creative methods in public and patient involvement in health and social care research? A qualitative systematic review. Res Involv Engagem 10 , 48 (2024). https://doi.org/10.1186/s40900-024-00580-4

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Learn / Guides / Quantitative data analysis guide

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The ultimate guide to quantitative data analysis

Numbers help us make sense of the world. We collect quantitative data on our speed and distance as we drive, the number of hours we spend on our cell phones, and how much we save at the grocery store.

Our businesses run on numbers, too. We spend hours poring over key performance indicators (KPIs) like lead-to-client conversions, net profit margins, and bounce and churn rates.

But all of this quantitative data can feel overwhelming and confusing. Lists and spreadsheets of numbers don’t tell you much on their own—you have to conduct quantitative data analysis to understand them and make informed decisions.

Last updated

Reading time.

This guide explains what quantitative data analysis is and why it’s important, and gives you a four-step process to conduct a quantitative data analysis, so you know exactly what’s happening in your business and what your users need .

Collect quantitative customer data with Hotjar

Use Hotjar’s tools to gather the customer insights you need to make quantitative data analysis a breeze.

What is quantitative data analysis? 

Quantitative data analysis is the process of analyzing and interpreting numerical data. It helps you make sense of information by identifying patterns, trends, and relationships between variables through mathematical calculations and statistical tests. 

With quantitative data analysis, you turn spreadsheets of individual data points into meaningful insights to drive informed decisions. Columns of numbers from an experiment or survey transform into useful insights—like which marketing campaign asset your average customer prefers or which website factors are most closely connected to your bounce rate. 

Without analytics, data is just noise. Analyzing data helps you make decisions which are informed and free from bias.

What quantitative data analysis is not

But as powerful as quantitative data analysis is, it’s not without its limitations. It only gives you the what, not the why . For example, it can tell you how many website visitors or conversions you have on an average day, but it can’t tell you why users visited your site or made a purchase.

For the why behind user behavior, you need qualitative data analysis , a process for making sense of qualitative research like open-ended survey responses, interview clips, or behavioral observations. By analyzing non-numerical data, you gain useful contextual insights to shape your strategy, product, and messaging. 

Quantitative data analysis vs. qualitative data analysis 

Let’s take an even deeper dive into the differences between quantitative data analysis and qualitative data analysis to explore what they do and when you need them.

The bottom line: quantitative data analysis and qualitative data analysis are complementary processes. They work hand-in-hand to tell you what’s happening in your business and why.  

💡 Pro tip: easily toggle between quantitative and qualitative data analysis with Hotjar Funnels . 

The Funnels tool helps you visualize quantitative metrics like drop-off and conversion rates in your sales or conversion funnel to understand when and where users leave your website. You can break down your data even further to compare conversion performance by user segment.

Spot a potential issue? A single click takes you to relevant session recordings , where you see user behaviors like mouse movements, scrolls, and clicks. With this qualitative data to provide context, you'll better understand what you need to optimize to streamline the user experience (UX) and increase conversions .

Hotjar Funnels lets you quickly explore the story behind the quantitative data

4 benefits of quantitative data analysis

There’s a reason product, web design, and marketing teams take time to analyze metrics: the process pays off big time. 

Four major benefits of quantitative data analysis include:

1. Make confident decisions 

With quantitative data analysis, you know you’ve got data-driven insights to back up your decisions . For example, if you launch a concept testing survey to gauge user reactions to a new logo design, and 92% of users rate it ‘very good’—you'll feel certain when you give the designer the green light. 

Since you’re relying less on intuition and more on facts, you reduce the risks of making the wrong decision. (You’ll also find it way easier to get buy-in from team members and stakeholders for your next proposed project. 🙌)

2. Reduce costs

By crunching the numbers, you can spot opportunities to reduce spend . For example, if an ad campaign has lower-than-average click-through rates , you might decide to cut your losses and invest your budget elsewhere. 

Or, by analyzing ecommerce metrics , like website traffic by source, you may find you’re getting very little return on investment from a certain social media channel—and scale back spending in that area.

3. Personalize the user experience

Quantitative data analysis helps you map the customer journey , so you get a better sense of customers’ demographics, what page elements they interact with on your site, and where they drop off or convert . 

These insights let you better personalize your website, product, or communication, so you can segment ads, emails, and website content for specific user personas or target groups.

4. Improve user satisfaction and delight

Quantitative data analysis lets you see where your website or product is doing well—and where it falls short for your users . For example, you might see stellar results from KPIs like time on page, but conversion rates for that page are low. 

These quantitative insights encourage you to dive deeper into qualitative data to see why that’s happening—looking for moments of confusion or frustration on session recordings, for example—so you can make adjustments and optimize your conversions by improving customer satisfaction and delight.

💡Pro tip: use Net Promoter Score® (NPS) surveys to capture quantifiable customer satisfaction data that’s easy for you to analyze and interpret. 

With an NPS tool like Hotjar, you can create an on-page survey to ask users how likely they are to recommend you to others on a scale from 0 to 10. (And for added context, you can ask follow-up questions about why customers selected the rating they did—rich qualitative data is always a bonus!)

Hotjar graphs your quantitative NPS data to show changes over time

4 steps to effective quantitative data analysis 

Quantitative data analysis sounds way more intimidating than it actually is. Here’s how to make sense of your company’s numbers in just four steps:

1. Collect data

Before you can actually start the analysis process, you need data to analyze. This involves conducting quantitative research and collecting numerical data from various sources, including: 

Interviews or focus groups 

Website analytics

Observations, from tools like heatmaps or session recordings

Questionnaires, like surveys or on-page feedback widgets

Just ensure the questions you ask in your surveys are close-ended questions—providing respondents with select choices to choose from instead of open-ended questions that allow for free responses.

Hotjar’s pricing plans survey template provides close-ended questions

 2. Clean data

Once you’ve collected your data, it’s time to clean it up. Look through your results to find errors, duplicates, and omissions. Keep an eye out for outliers, too. Outliers are data points that differ significantly from the rest of the set—and they can skew your results if you don’t remove them.

By taking the time to clean your data set, you ensure your data is accurate, consistent, and relevant before it’s time to analyze. 

3. Analyze and interpret data

At this point, your data’s all cleaned up and ready for the main event. This step involves crunching the numbers to find patterns and trends via mathematical and statistical methods. 

Two main branches of quantitative data analysis exist: 

Descriptive analysis : methods to summarize or describe attributes of your data set. For example, you may calculate key stats like distribution and frequency, or mean, median, and mode.

Inferential analysis : methods that let you draw conclusions from statistics—like analyzing the relationship between variables or making predictions. These methods include t-tests, cross-tabulation, and factor analysis. (For more detailed explanations and how-tos, head to our guide on quantitative data analysis methods.)

Then, interpret your data to determine the best course of action. What does the data suggest you do ? For example, if your analysis shows a strong correlation between email open rate and time sent, you may explore optimal send times for each user segment.

4. Visualize and share data

Once you’ve analyzed and interpreted your data, create easy-to-read, engaging data visualizations—like charts, graphs, and tables—to present your results to team members and stakeholders. Data visualizations highlight similarities and differences between data sets and show the relationships between variables.

Software can do this part for you. For example, the Hotjar Dashboard shows all of your key metrics in one place—and automatically creates bar graphs to show how your top pages’ performance compares. And with just one click, you can navigate to the Trends tool to analyze product metrics for different segments on a single chart. 

Hotjar Trends lets you compare metrics across segments

Discover rich user insights with quantitative data analysis

Conducting quantitative data analysis takes a little bit of time and know-how, but it’s much more manageable than you might think. 

By choosing the right methods and following clear steps, you gain insights into product performance and customer experience —and you’ll be well on your way to making better decisions and creating more customer satisfaction and loyalty.

FAQs about quantitative data analysis

What is quantitative data analysis.

Quantitative data analysis is the process of making sense of numerical data through mathematical calculations and statistical tests. It helps you identify patterns, relationships, and trends to make better decisions.

How is quantitative data analysis different from qualitative data analysis?

Quantitative and qualitative data analysis are both essential processes for making sense of quantitative and qualitative research .

Quantitative data analysis helps you summarize and interpret numerical results from close-ended questions to understand what is happening. Qualitative data analysis helps you summarize and interpret non-numerical results, like opinions or behavior, to understand why the numbers look like they do.

 If you want to make strong data-driven decisions, you need both.

What are some benefits of quantitative data analysis?

Quantitative data analysis turns numbers into rich insights. Some benefits of this process include: 

Making more confident decisions

Identifying ways to cut costs

Personalizing the user experience

Improving customer satisfaction

What methods can I use to analyze quantitative data?

Quantitative data analysis has two branches: descriptive statistics and inferential statistics. 

Descriptive statistics provide a snapshot of the data’s features by calculating measures like mean, median, and mode. 

Inferential statistics , as the name implies, involves making inferences about what the data means. Dozens of methods exist for this branch of quantitative data analysis, but three commonly used techniques are: 

Cross tabulation

Factor analysis