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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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Qualitative Data Analysis and Presentation of Analysis Results

• First Online: 10 February 2022

Cite this chapter

• Charles P. Friedman 4 ,
• Jeremy C. Wyatt 5 &
• Joan S. Ash 6  

Part of the book series: Health Informatics ((HI))

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While the prior two chapters introduced the reader to the nature of qualitative evaluation and qualitative data collection, this chapter describes qualitative data analysis processes and how to present the results of analysis in a credible manner. The chapter explains different approaches to qualitative data analysis, how qualitative data analysis software can assist with the analysis process, how to code data, what is involved in interpretation, and the use of graphics in both the analysis and reporting processes.

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Department of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, MI, USA

Charles P. Friedman

Department of Primary Care, Population Sciences and Medical Education, School of Medicine, University of Southampton, Southampton, UK

Jeremy C. Wyatt

Department of Medical Informatics and Clinical Epidemiology, School of Medicine, Oregon Health & Science University, Portland, OR, USA

Joan S. Ash

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Correspondence to Charles P. Friedman .

Answers to Self-Tests

Self-test 16.1.

They should use an editing style. Using a template style, they would be imposing a preconceived list of terms upon the data, as if they were indexing the data. However, this project enters new territory and little is as yet known about this information resource, so it would be difficult developing a list of applicable codes at this early point. The editing style would let them develop a code book of terms that arise from the data.

The team’s selection of software depends on how big the project and budget will be. If the project ends with one hospital and perhaps 30 participants, a freely available software package might suffice. However, if the scope goes beyond that and team members need to take advantage of more sophisticated capabilities, a more powerful package should be considered.

Self-Test 16.2

Interview with A codes might be: Fun, Best years, Risk, Work hard, Peers, Be open, Bad times, Hope you’re lucky, Never could have planned, Opportunity, Take chances, Hard times, No bed of roses

Interview with B codes might be: Influencing what happens, Don’t have preconceptions of what is possible, Set the bar, Expend the energy, Look at history, I look to see what’s the lesson, We’re lucky, Colleagues, Open field, Everything is solvable, Can’t plan for serendipity

How they are alike: Lucky, Hard work/expend the energy, Peers/colleagues, Open/open field, Never could have planned/can’t plan for serendipity

How they are different: A talks about opportunity, risk, taking chances, hard times and B mentions setting the bar, looking at history for lessons, and solving any problems

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Friedman, C.P., Wyatt, J.C., Ash, J.S. (2022). Qualitative Data Analysis and Presentation of Analysis Results. In: Evaluation Methods in Biomedical and Health Informatics. Health Informatics. Springer, Cham. https://doi.org/10.1007/978-3-030-86453-8_16

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• Published: 26 April 2008

Analysing and presenting qualitative data

• P. Burnard 1 ,
• P. Gill 2 ,
• K. Stewart 3 ,
• E. Treasure 4 &
• B. Chadwick 5  

British Dental Journal volume  204 ,  pages 429–432 ( 2008 ) Cite this article

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Analysing and presenting qualitative data is one of the most confusing aspects of qualitative research.

This paper provides a pragmatic approach using a form of thematic content analysis. Approaches to presenting qualitative data are also discussed.

The process of qualitative data analysis is labour intensive and time consuming. Those who are unsure about this approach should seek appropriate advice.

This paper provides a pragmatic approach to analysing qualitative data, using actual data from a qualitative dental public health study for demonstration purposes. The paper also critically explores how computers can be used to facilitate this process, the debate about the verification (validation) of qualitative analyses and how to write up and present qualitative research studies.

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Introduction

Previous papers in this series have introduced readers to qualitative research and identified approaches to collecting qualitative data. However, for those new to this approach, one of the most bewildering aspects of qualitative research is, perhaps, how to analyse and present the data once it has been collected. This final paper therefore considers a method of analysing and presenting textual data gathered during qualitative work. boxed-text

Box 1: Qualitative research in dentistry

Qualitative research in dentistry

Methods of data collection in qualitative research: interviews and focus groups

Conducting qualitative interviews with school children in dental research

Approaches to analysing qualitative data

There are two fundamental approaches to analysing qualitative data (although each can be handled in a variety of different ways): the deductive approach and the inductive approach. 1 , 2 Deductive approaches involve using a structure or predetermined framework to analyse data. Essentially, the researcher imposes their own structure or theories on the data and then uses these to analyse the interview transcripts. 3

This approach is useful in studies where researchers are already aware of probable participant responses. For example, if a study explored patients' reasons for complaining about their dentist, the interview may explore common reasons for patients' complaints, such as trauma following treatment and communication problems. The data analysis would then consist of examining each interview to determine how many patients had complaints of each type and the extent to which complaints of each type co-occur. 3 However, while this approach is relatively quick and easy, it is inflexible and can potentially bias the whole analysis process as the coding framework has been decided in advance, which can severely limit theme and theory development.

Conversely, the inductive approach involves analysing data with little or no predetermined theory, structure or framework and uses the actual data itself to derive the structure of analysis. This approach is comprehensive and therefore time-consuming and is most suitable where little or nothing is known about the study phenomenon. Inductive analysis is the most common approach used to analyse qualitative data 2 and is, therefore, the focus of this paper.

Whilst a variety of inductive approaches to analysing qualitative data are available, the method of analysis described in this paper is that of thematic content analysis , and is, perhaps, the most common method of data analysis used in qualitative work. 4 , 5 This method arose out of the approach known as grounded theory, 6 although the method can be used in a range of other types of qualitative work, including ethnography and phenomenology (see the first paper in this series 7 for definitions). Indeed, the process of thematic content analysis is often very similar in all types of qualitative research, in that the process involves analysing transcripts, identifying themes within those data and gathering together examples of those themes from the text.

Data collection and data analysis

Interview transcripts, field notes and observations provide a descriptive account of the study, but they do not provide explanations. 4 It is the researcher who has to make sense of the data that have been collected by exploring and interpreting them.

Quantitative and qualitative research differ somewhat in their approach to data analysis. In quantitative research, data analysis often only occurs after all or much of data have been collected. However, in qualitative research, data analysis often begins during, or immediately after, the first data are collected, although this process continues and is modified throughout the study. Initial analysis of the data may also further inform subsequent data collection. For example, interview schedules may be slightly modified in light of emerging findings, where additional clarification may be required.

Computer software for data analysis

The method of analysis described in this paper involves managing the data 'by hand'. However, there are several computer-assisted qualitative data analysis software (CAQDAS) packages available that can be used to manage and help in the analysis of qualitative data. Common programmes include ATLAS. ti and NVivo. It should be noted, however, that such programs do not 'analyse' the data – that is the task of the researcher – they simply manage the data and make handling of them easier.

For example, computer packages can help to manage, sort and organise large volumes of qualitative data, store, annotate and retrieve text, locate words, phrases and segments of data, prepare diagrams and extract quotes. 8 However, whilst computer programmes can facilitate data analysis, making the process easier and, arguably, more flexible, accurate and comprehensive, they do not confirm or deny the scientific value or quality of qualitative research, as they are merely instruments, as good or as bad as the researcher using them.

Stages in the process

Regardless of whether data are analysed by hand or using computer software, the process of thematic content analysis is essentially the same, in that it involves identifying themes and categories that 'emerge from the data'. This involves discovering themes in the interview transcripts and attempting to verify, confirm and qualify them by searching through the data and repeating the process to identify further themes and categories. 4

In order to do this, once the interviews have been transcribed verbatim, the researcher reads each transcript and makes notes in the margins of words, theories or short phrases that sum up what is being said in the text. This is usually known as open coding. The aim, however, is to offer a summary statement or word for each element that is discussed in the transcript. The exception to this is when the respondent has clearly gone off track and begun to move away from the topic under discussion. Such deviations (as long as they really are deviations) can simply be uncoded. Such 'off the topic' material is sometimes known as 'dross'. 9

Table 1 is an example of the initial coding framework used in the data generated from an actual interview with a child in a qualitative dental public health study, exploring primary school children's understanding of food. 10

In the second stage, the researcher collects together all of the words and phrases from all of the interviews onto a clean set of pages. These can then be worked through and all duplications crossed out. This will have the effect of reducing the numbers of 'categories' quite considerably. 11 , 12 Using a section of the initial coding framework from the above study, 10 such a list of categories might read as follows:

Children's perception of food

Positive notions of food and their consequences

Negative notions of food and their consequences

Peer influence

Healthy/unhealthy foods

Effects of sweets and chocolates

Effects of 'junk food'

Food choices in school

Diet in childhood

Food preferences

Expected diet as a 'grown up'

Food choices and preferences of friendship groups

Effects of fizzy drinks

Perceptions of adult/child diets

The need to be 'healthy' as an adult.

Once this second, shorter list of categories has been compiled, the researcher goes a stage further and looks for overlapping or similar categories. Informed by the analytical and theoretical ideas developed during the research, these categories are further refined and reduced in number by grouping them together. 4 A list of several categories (perhaps up to a maximum of twelve) can then be compiled. If we consider the above example, we might eventually come up with the reduced list shown in Table 2 .

This reduced list forms the final category system that can be used to divide up all of the interviews. 12 The next stage is to allocate each of the categories its own coloured marking pen and then each transcript is worked through and data that fit under a particular category are marked with the according colour. Finally, all of the sections of data, under each of the categories (and thus assigned a particular colour) are cut out and pasted onto the A4 sheets. Subject dividers can then be labelled with each category label and the corresponding coloured snippets, on each of the pages, are filed in a lever arch file. What the researcher has achieved is an organised dataset, filed in one folder. It is from this folder that the report of the findings can be written.

As discussed earlier, computer programmes can be used to manage this process and may be particularly useful in qualitative studies with larger datasets. However, researchers wishing to use such software should first undertake appropriate training and should be aware that most programmes often do not abide by normal MS Windows conventions (eg, most interview transcripts have to be converted from MS Word into rich text format before they can be imported into the programme for analysis).

Verification

The analysis of qualitative data does, of course, involve interpreting the study findings. However, this process is arguably more subjective than the process normally associated with quantitative data analysis, since a common belief amongst social scientists is that a definitive, objective view of social reality does not exist. For example, some quantitative researchers claim that qualitative accounts cannot be held straightforwardly to represent the social world, thus different researchers may interpret the same data somewhat differently. 4 Consequently, this leads to the issue of the verifiability of qualitative data analysis.

There is, therefore, a debate as to whether qualitative researchers should have their analyses verified or validated by a third party. 13 , 14 It has been argued that this process can make the analysis more rigorous and reduce the element bias. There are two key ways of having data analyses validated by others: respondent validation (or member check) – returning to the study participants and asking them to validate analyses – and peer review (or peer debrief, also referred to as inter-rater reliability) – whereby another qualitative researcher analyses the data independently. 13 , 14 , 15

Participant validation involves returning to respondents and asking them to carefully read through their interview transcripts and/or data analysis for them to validate, or refute, the researcher's interpretation of the data. Whilst this can arguably help to refine theme and theory development, the process is hugely time consuming and, if it does not occur relatively soon after data collection and analysis, participants may have also changed their perceptions and views because of temporal effects and potential changes in their situation, health, and perhaps even as a result of participation in the study. 15

Some respondents may also want to modify their opinions on re-presentation of the data if they now feel that, on reflection, their original comments are not 'socially desirable'. There is also the problem of how to present such information to people who are likely to be non-academics. Furthermore, it is possible that some participants will not recognise some of the emerging theories, as each of them will probably have contributed only a portion of the data. 16

The process of peer review involves at least one other suitably experienced researcher independently reviewing and exploring interview transcripts, data analysis and emerging themes. It has been argued that this process may help to guard against the potential for lone researcher bias and help to provide additional insights into theme and theory development. 14 , 16 , 17 However, many researchers also feel that the value of this approach is questionable, since it is possible that each researcher may interpret the data, or parts of it, differently. 8 Also, if both perspectives are grounded in and supported by the data, is one interpretation necessarily stronger or more valid than the other?

Unfortunately, despite perpetual debate, there is no definitive answer to the issue of validity in qualitative analysis. However, to ensure that the analysis process is systematic and rigorous, the whole corpus of collected data must be thoroughly analysed. Therefore, where appropriate, this should also include the search for and identification of relevant 'deviant or contrary cases' – ie, findings that are different or contrary to the main findings, or are simply unique to some or even just one respondent. Qualitative researchers should also utilise a process of 'constant comparison' when analysing data. This essentially involves reading and re-reading data to search for and identify emerging themes in the constant search for understanding and the meaning of the data. 18 , 19 Where appropriate, researchers should also provide a detailed explication in published reports of how data was collected and analysed, as this helps the reader to critically assess the value of the study.

It should also be noted that qualitative data cannot be usefully quantified given the nature, composition and size of the sample group, and ultimately the epistemological aim of the methodology.

Writing and presenting qualitative research

There are two main approaches to writing up the findings of qualitative research. 20 The first is to simply report key findings under each main theme or category, using appropriate verbatim quotes to illustrate those findings. This is then accompanied by a linking, separate discussion chapter in which the findings are discussed in relation to existing research (as in quantitative studies). The second is to do the same but to incorporate the discussion into the findings chapter. Below are brief examples of the two approaches, using actual data from a qualitative dental public health study that explored primary school children's understanding of food. 10

Example a (the traditional approach):

Contrasts and contradictions

The interviews demonstrated that children are able to operate contrasts and contradictions about food effortlessly. These contradictions are both sophisticated and complex, incorporating positive and negative notions relating to food and its health and social consequences, which they are able to fluently adopt when talking about food:

'My mother says drink juice because it's healthy and she says if you don't drink it you won't get healthy and you won't have any sweets and you'll end up having to go to hospital if you don't eat anything like vegetables because you'll get weak' . (Girl, school 3, age 11 years).

If this approach was used, the findings chapter would subsequently be followed by a separate supporting discussion and conclusion section in which the findings would be critically discussed and compared to the appropriate existing research. As in quantitative research, these supporting chapters would also be used to develop theories or hypothesise about the data and, if appropriate, to make realistic conclusions and recommendations for practice and further research.

Example b (combined findings and discussion chapter):

Copying friends

In this study, as with others (eg Ludvigsen & Sharma 21 and Watt & Sheiham 22 ), peer influence is a strong factor, with children copying each other's food choices at school meal times:

Girl: 'They say “copy me and what I have.”'

Interviewer: 'And do you copy them if they say that?'

Girl: 'Yes.'

Interviewer: 'Why do you copy them if they say that?'

Girl: 'Because they are my friends.'

(Girl, school 1, age 7).

Children also identified friendship groups according to the school meal type they have. Children have been known to have school dinners, or packed lunches if their friends also have the same. 21

If this approach was used, the combined findings and discussion section would simply be followed by a concluding chapter. Further guidance on writing up qualitative reports can be found in the literature. 20

This paper has described a pragmatic process of thematic content analysis as a method of analysing qualitative data generated by interviews or focus groups. Other approaches to analysis are available and are discussed in the literature. 23 , 24 , 25 The method described here offers a method of generating categories under which similar themes or categories can be collated. The paper also briefly illustrates two different ways of presenting qualitative reports, having analysed the data.

This analysis process, when done properly, is systematic and rigorous and therefore labour-intensive and time consuming. 4 Consequently, for those undertaking this process for the first time, we recommend seeking advice from experienced qualitative researchers.

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Qualitative Data Analysis

23 Presenting the Results of Qualitative Analysis

Mikaila Mariel Lemonik Arthur

Qualitative research is not finished just because you have determined the main findings or conclusions of your study. Indeed, disseminating the results is an essential part of the research process. By sharing your results with others, whether in written form as scholarly paper or an applied report or in some alternative format like an oral presentation, an infographic, or a video, you ensure that your findings become part of the ongoing conversation of scholarship in your field, forming part of the foundation for future researchers. This chapter provides an introduction to writing about qualitative research findings. It will outline how writing continues to contribute to the analysis process, what concerns researchers should keep in mind as they draft their presentations of findings, and how best to organize qualitative research writing

As you move through the research process, it is essential to keep yourself organized. Organizing your data, memos, and notes aids both the analytical and the writing processes. Whether you use electronic or physical, real-world filing and organizational systems, these systems help make sense of the mountains of data you have and assure you focus your attention on the themes and ideas you have determined are important (Warren and Karner 2015). Be sure that you have kept detailed notes on all of the decisions you have made and procedures you have followed in carrying out research design, data collection, and analysis, as these will guide your ultimate write-up.

First and foremost, researchers should keep in mind that writing is in fact a form of thinking. Writing is an excellent way to discover ideas and arguments and to further develop an analysis. As you write, more ideas will occur to you, things that were previously confusing will start to make sense, and arguments will take a clear shape rather than being amorphous and poorly-organized. However, writing-as-thinking cannot be the final version that you share with others. Good-quality writing does not display the workings of your thought process. It is reorganized and revised (more on that later) to present the data and arguments important in a particular piece. And revision is totally normal! No one expects the first draft of a piece of writing to be ready for prime time. So write rough drafts and memos and notes to yourself and use them to think, and then revise them until the piece is the way you want it to be for sharing.

Bergin (2018) lays out a set of key concerns for appropriate writing about research. First, present your results accurately, without exaggerating or misrepresenting. It is very easy to overstate your findings by accident if you are enthusiastic about what you have found, so it is important to take care and use appropriate cautions about the limitations of the research. You also need to work to ensure that you communicate your findings in a way people can understand, using clear and appropriate language that is adjusted to the level of those you are communicating with. And you must be clear and transparent about the methodological strategies employed in the research. Remember, the goal is, as much as possible, to describe your research in a way that would permit others to replicate the study. There are a variety of other concerns and decision points that qualitative researchers must keep in mind, including the extent to which to include quantification in their presentation of results, ethics, considerations of audience and voice, and how to bring the richness of qualitative data to life.

Quantification, as you have learned, refers to the process of turning data into numbers. It can indeed be very useful to count and tabulate quantitative data drawn from qualitative research. For instance, if you were doing a study of dual-earner households and wanted to know how many had an equal division of household labor and how many did not, you might want to count those numbers up and include them as part of the final write-up. However, researchers need to take care when they are writing about quantified qualitative data. Qualitative data is not as generalizable as quantitative data, so quantification can be very misleading. Thus, qualitative researchers should strive to use raw numbers instead of the percentages that are more appropriate for quantitative research. Writing, for instance, “15 of the 20 people I interviewed prefer pancakes to waffles” is a simple description of the data; writing “75% of people prefer pancakes” suggests a generalizable claim that is not likely supported by the data. Note that mixing numbers with qualitative data is really a type of mixed-methods approach. Mixed-methods approaches are good, but sometimes they seduce researchers into focusing on the persuasive power of numbers and tables rather than capitalizing on the inherent richness of their qualitative data.

A variety of issues of scholarly ethics and research integrity are raised by the writing process. Some of these are unique to qualitative research, while others are more universal concerns for all academic and professional writing. For example, it is essential to avoid plagiarism and misuse of sources. All quotations that appear in a text must be properly cited, whether with in-text and bibliographic citations to the source or with an attribution to the research participant (or the participant’s pseudonym or description in order to protect confidentiality) who said those words. Where writers will paraphrase a text or a participant’s words, they need to make sure that the paraphrase they develop accurately reflects the meaning of the original words. Thus, some scholars suggest that participants should have the opportunity to read (or to have read to them, if they cannot read the text themselves) all sections of the text in which they, their words, or their ideas are presented to ensure accuracy and enable participants to maintain control over their lives.

Audience and Voice

When writing, researchers must consider their audience(s) and the effects they want their writing to have on these audiences. The designated audience will dictate the voice used in the writing, or the individual style and personality of a piece of text. Keep in mind that the potential audience for qualitative research is often much more diverse than that for quantitative research because of the accessibility of the data and the extent to which the writing can be accessible and interesting. Yet individual pieces of writing are typically pitched to a more specific subset of the audience.

Let us consider one potential research study, an ethnography involving participant-observation of the same children both when they are at daycare facility and when they are at home with their families to try to understand how daycare might impact behavior and social development. The findings of this study might be of interest to a wide variety of potential audiences: academic peers, whether at your own academic institution, in your broader discipline, or multidisciplinary; people responsible for creating laws and policies; practitioners who run or teach at day care centers; and the general public, including both people who are interested in child development more generally and those who are themselves parents making decisions about child care for their own children. And the way you write for each of these audiences will be somewhat different. Take a moment and think through what some of these differences might look like.

If you are writing to academic audiences, using specialized academic language and working within the typical constraints of scholarly genres, as will be discussed below, can be an important part of convincing others that your work is legitimate and should be taken seriously. Your writing will be formal. Even if you are writing for students and faculty you already know—your classmates, for instance—you are often asked to imitate the style of academic writing that is used in publications, as this is part of learning to become part of the scholarly conversation. When speaking to academic audiences outside your discipline, you may need to be more careful about jargon and specialized language, as disciplines do not always share the same key terms. For instance, in sociology, scholars use the term diffusion to refer to the way new ideas or practices spread from organization to organization. In the field of international relations, scholars often used the term cascade to refer to the way ideas or practices spread from nation to nation. These terms are describing what is fundamentally the same concept, but they are different terms—and a scholar from one field might have no idea what a scholar from a different field is talking about! Therefore, while the formality and academic structure of the text would stay the same, a writer with a multidisciplinary audience might need to pay more attention to defining their terms in the body of the text.

It is not only other academic scholars who expect to see formal writing. Policymakers tend to expect formality when ideas are presented to them, as well. However, the content and style of the writing will be different. Much less academic jargon should be used, and the most important findings and policy implications should be emphasized right from the start rather than initially focusing on prior literature and theoretical models as you might for an academic audience. Long discussions of research methods should also be minimized. Similarly, when you write for practitioners, the findings and implications for practice should be highlighted. The reading level of the text will vary depending on the typical background of the practitioners to whom you are writing—you can make very different assumptions about the general knowledge and reading abilities of a group of hospital medical directors with MDs than you can about a group of case workers who have a post-high-school certificate. Consider the primary language of your audience as well. The fact that someone can get by in spoken English does not mean they have the vocabulary or English reading skills to digest a complex report. But the fact that someone’s vocabulary is limited says little about their intellectual abilities, so try your best to convey the important complexity of the ideas and findings from your research without dumbing them down—even if you must limit your vocabulary usage.

When writing for the general public, you will want to move even further towards emphasizing key findings and policy implications, but you also want to draw on the most interesting aspects of your data. General readers will read sociological texts that are rich with ethnographic or other kinds of detail—it is almost like reality television on a page! And this is a contrast to busy policymakers and practitioners, who probably want to learn the main findings as quickly as possible so they can go about their busy lives. But also keep in mind that there is a wide variation in reading levels. Journalists at publications pegged to the general public are often advised to write at about a tenth-grade reading level, which would leave most of the specialized terminology we develop in our research fields out of reach. If you want to be accessible to even more people, your vocabulary must be even more limited. The excellent exercise of trying to write using the 1,000 most common English words, available at the Up-Goer Five website ( https://www.splasho.com/upgoer5/ ) does a good job of illustrating this challenge (Sanderson n.d.).

Another element of voice is whether to write in the first person. While many students are instructed to avoid the use of the first person in academic writing, this advice needs to be taken with a grain of salt. There are indeed many contexts in which the first person is best avoided, at least as long as writers can find ways to build strong, comprehensible sentences without its use, including most quantitative research writing. However, if the alternative to using the first person is crafting a sentence like “it is proposed that the researcher will conduct interviews,” it is preferable to write “I propose to conduct interviews.” In qualitative research, in fact, the use of the first person is far more common. This is because the researcher is central to the research project. Qualitative researchers can themselves be understood as research instruments, and thus eliminating the use of the first person in writing is in a sense eliminating information about the conduct of the researchers themselves.

But the question really extends beyond the issue of first-person or third-person. Qualitative researchers have choices about how and whether to foreground themselves in their writing, not just in terms of using the first person, but also in terms of whether to emphasize their own subjectivity and reflexivity, their impressions and ideas, and their role in the setting. In contrast, conventional quantitative research in the positivist tradition really tries to eliminate the author from the study—which indeed is exactly why typical quantitative research avoids the use of the first person. Keep in mind that emphasizing researchers’ roles and reflexivity and using the first person does not mean crafting articles that provide overwhelming detail about the author’s thoughts and practices. Readers do not need to hear, and should not be told, which database you used to search for journal articles, how many hours you spent transcribing, or whether the research process was stressful—save these things for the memos you write to yourself. Rather, readers need to hear how you interacted with research participants, how your standpoint may have shaped the findings, and what analytical procedures you carried out.

Making Data Come Alive

One of the most important parts of writing about qualitative research is presenting the data in a way that makes its richness and value accessible to readers. As the discussion of analysis in the prior chapter suggests, there are a variety of ways to do this. Researchers may select key quotes or images to illustrate points, write up specific case studies that exemplify their argument, or develop vignettes (little stories) that illustrate ideas and themes, all drawing directly on the research data. Researchers can also write more lengthy summaries, narratives, and thick descriptions.

Nearly all qualitative work includes quotes from research participants or documents to some extent, though ethnographic work may focus more on thick description than on relaying participants’ own words. When quotes are presented, they must be explained and interpreted—they cannot stand on their own. This is one of the ways in which qualitative research can be distinguished from journalism. Journalism presents what happened, but social science needs to present the “why,” and the why is best explained by the researcher.

So how do authors go about integrating quotes into their written work? Julie Posselt (2017), a sociologist who studies graduate education, provides a set of instructions. First of all, authors need to remain focused on the core questions of their research, and avoid getting distracted by quotes that are interesting or attention-grabbing but not so relevant to the research question. Selecting the right quotes, those that illustrate the ideas and arguments of the paper, is an important part of the writing process. Second, not all quotes should be the same length (just like not all sentences or paragraphs in a paper should be the same length). Include some quotes that are just phrases, others that are a sentence or so, and others that are longer. We call longer quotes, generally those more than about three lines long, block quotes , and they are typically indented on both sides to set them off from the surrounding text. For all quotes, be sure to summarize what the quote should be telling or showing the reader, connect this quote to other quotes that are similar or different, and provide transitions in the discussion to move from quote to quote and from topic to topic. Especially for longer quotes, it is helpful to do some of this writing before the quote to preview what is coming and other writing after the quote to make clear what readers should have come to understand. Remember, it is always the author’s job to interpret the data. Presenting excerpts of the data, like quotes, in a form the reader can access does not minimize the importance of this job. Be sure that you are explaining the meaning of the data you present.

A few more notes about writing with quotes: avoid patchwriting, whether in your literature review or the section of your paper in which quotes from respondents are presented. Patchwriting is a writing practice wherein the author lightly paraphrases original texts but stays so close to those texts that there is little the author has added. Sometimes, this even takes the form of presenting a series of quotes, properly documented, with nothing much in the way of text generated by the author. A patchwriting approach does not build the scholarly conversation forward, as it does not represent any kind of new contribution on the part of the author. It is of course fine to paraphrase quotes, as long as the meaning is not changed. But if you use direct quotes, do not edit the text of the quotes unless how you edit them does not change the meaning and you have made clear through the use of ellipses (…) and brackets ([])what kinds of edits have been made. For example, consider this exchange from Matthew Desmond’s (2012:1317) research on evictions:

The thing was, I wasn’t never gonna let Crystal come and stay with me from the get go. I just told her that to throw her off. And she wasn’t fittin’ to come stay with me with no money…No. Nope. You might as well stay in that shelter.

A paraphrase of this exchange might read “She said that she was going to let Crystal stay with her if Crystal did not have any money.” Paraphrases like that are fine. What is not fine is rewording the statement but treating it like a quote, for instance writing:

The thing was, I was not going to let Crystal come and stay with me from beginning. I just told her that to throw her off. And it was not proper for her to come stay with me without any money…No. Nope. You might as well stay in that shelter.

But as you can see, the change in language and style removes some of the distinct meaning of the original quote. Instead, writers should leave as much of the original language as possible. If some text in the middle of the quote needs to be removed, as in this example, ellipses are used to show that this has occurred. And if a word needs to be added to clarify, it is placed in square brackets to show that it was not part of the original quote.

Data can also be presented through the use of data displays like tables, charts, graphs, diagrams, and infographics created for publication or presentation, as well as through the use of visual material collected during the research process. Note that if visuals are used, the author must have the legal right to use them. Photographs or diagrams created by the author themselves—or by research participants who have signed consent forms for their work to be used, are fine. But photographs, and sometimes even excerpts from archival documents, may be owned by others from whom researchers must get permission in order to use them.

A large percentage of qualitative research does not include any data displays or visualizations. Therefore, researchers should carefully consider whether the use of data displays will help the reader understand the data. One of the most common types of data displays used by qualitative researchers are simple tables. These might include tables summarizing key data about cases included in the study; tables laying out the characteristics of different taxonomic elements or types developed as part of the analysis; tables counting the incidence of various elements; and 2×2 tables (two columns and two rows) illuminating a theory. Basic network or process diagrams are also commonly included. If data displays are used, it is essential that researchers include context and analysis alongside data displays rather than letting them stand by themselves, and it is preferable to continue to present excerpts and examples from the data rather than just relying on summaries in the tables.

If you will be using graphs, infographics, or other data visualizations, it is important that you attend to making them useful and accurate (Bergin 2018). Think about the viewer or user as your audience and ensure the data visualizations will be comprehensible. You may need to include more detail or labels than you might think. Ensure that data visualizations are laid out and labeled clearly and that you make visual choices that enhance viewers’ ability to understand the points you intend to communicate using the visual in question. Finally, given the ease with which it is possible to design visuals that are deceptive or misleading, it is essential to make ethical and responsible choices in the construction of visualization so that viewers will interpret them in accurate ways.

The Genre of Research Writing

As discussed above, the style and format in which results are presented depends on the audience they are intended for. These differences in styles and format are part of the genre of writing. Genre is a term referring to the rules of a specific form of creative or productive work. Thus, the academic journal article—and student papers based on this form—is one genre. A report or policy paper is another. The discussion below will focus on the academic journal article, but note that reports and policy papers follow somewhat different formats. They might begin with an executive summary of one or a few pages, include minimal background, focus on key findings, and conclude with policy implications, shifting methods and details about the data to an appendix. But both academic journal articles and policy papers share some things in common, for instance the necessity for clear writing, a well-organized structure, and the use of headings.

So what factors make up the genre of the academic journal article in sociology? While there is some flexibility, particularly for ethnographic work, academic journal articles tend to follow a fairly standard format. They begin with a “title page” that includes the article title (often witty and involving scholarly inside jokes, but more importantly clearly describing the content of the article); the authors’ names and institutional affiliations, an abstract , and sometimes keywords designed to help others find the article in databases. An abstract is a short summary of the article that appears both at the very beginning of the article and in search databases. Abstracts are designed to aid readers by giving them the opportunity to learn enough about an article that they can determine whether it is worth their time to read the complete text. They are written about the article, and thus not in the first person, and clearly summarize the research question, methodological approach, main findings, and often the implications of the research.

After the abstract comes an “introduction” of a page or two that details the research question, why it matters, and what approach the paper will take. This is followed by a literature review of about a quarter to a third the length of the entire paper. The literature review is often divided, with headings, into topical subsections, and is designed to provide a clear, thorough overview of the prior research literature on which a paper has built—including prior literature the new paper contradicts. At the end of the literature review it should be made clear what researchers know about the research topic and question, what they do not know, and what this new paper aims to do to address what is not known.

The next major section of the paper is the section that describes research design, data collection, and data analysis, often referred to as “research methods” or “methodology.” This section is an essential part of any written or oral presentation of your research. Here, you tell your readers or listeners “how you collected and interpreted your data” (Taylor, Bogdan, and DeVault 2016:215). Taylor, Bogdan, and DeVault suggest that the discussion of your research methods include the following:

• The particular approach to data collection used in the study;
• Any theoretical perspective(s) that shaped your data collection and analytical approach;
• When the study occurred, over how long, and where (concealing identifiable details as needed);
• A description of the setting and participants, including sampling and selection criteria (if an interview-based study, the number of participants should be clearly stated);
• The researcher’s perspective in carrying out the study, including relevant elements of their identity and standpoint, as well as their role (if any) in research settings; and
• The approach to analyzing the data.

After the methods section comes a section, variously titled but often called “data,” that takes readers through the analysis. This section is where the thick description narrative; the quotes, broken up by theme or topic, with their interpretation; the discussions of case studies; most data displays (other than perhaps those outlining a theoretical model or summarizing descriptive data about cases); and other similar material appears. The idea of the data section is to give readers the ability to see the data for themselves and to understand how this data supports the ultimate conclusions. Note that all tables and figures included in formal publications should be titled and numbered.

At the end of the paper come one or two summary sections, often called “discussion” and/or “conclusion.” If there is a separate discussion section, it will focus on exploring the overall themes and findings of the paper. The conclusion clearly and succinctly summarizes the findings and conclusions of the paper, the limitations of the research and analysis, any suggestions for future research building on the paper or addressing these limitations, and implications, be they for scholarship and theory or policy and practice.

After the end of the textual material in the paper comes the bibliography, typically called “works cited” or “references.” The references should appear in a consistent citation style—in sociology, we often use the American Sociological Association format (American Sociological Association 2019), but other formats may be used depending on where the piece will eventually be published. Care should be taken to ensure that in-text citations also reflect the chosen citation style. In some papers, there may be an appendix containing supplemental information such as a list of interview questions or an additional data visualization.

Note that when researchers give presentations to scholarly audiences, the presentations typically follow a format similar to that of scholarly papers, though given time limitations they are compressed. Abstracts and works cited are often not part of the presentation, though in-text citations are still used. The literature review presented will be shortened to only focus on the most important aspects of the prior literature, and only key examples from the discussion of data will be included. For long or complex papers, sometimes only one of several findings is the focus of the presentation. Of course, presentations for other audiences may be constructed differently, with greater attention to interesting elements of the data and findings as well as implications and less to the literature review and methods.

Concluding Your Work

After you have written a complete draft of the paper, be sure you take the time to revise and edit your work. There are several important strategies for revision. First, put your work away for a little while. Even waiting a day to revise is better than nothing, but it is best, if possible, to take much more time away from the text. This helps you forget what your writing looks like and makes it easier to find errors, mistakes, and omissions. Second, show your work to others. Ask them to read your work and critique it, pointing out places where the argument is weak, where you may have overlooked alternative explanations, where the writing could be improved, and what else you need to work on. Finally, read your work out loud to yourself (or, if you really need an audience, try reading to some stuffed animals). Reading out loud helps you catch wrong words, tricky sentences, and many other issues. But as important as revision is, try to avoid perfectionism in writing (Warren and Karner 2015). Writing can always be improved, no matter how much time you spend on it. Those improvements, however, have diminishing returns, and at some point the writing process needs to conclude so the writing can be shared with the world.

Of course, the main goal of writing up the results of a research project is to share with others. Thus, researchers should be considering how they intend to disseminate their results. What conferences might be appropriate? Where can the paper be submitted? Note that if you are an undergraduate student, there are a wide variety of journals that accept and publish research conducted by undergraduates. Some publish across disciplines, while others are specific to disciplines. Other work, such as reports, may be best disseminated by publication online on relevant organizational websites.

After a project is completed, be sure to take some time to organize your research materials and archive them for longer-term storage. Some Institutional Review Board (IRB) protocols require that original data, such as interview recordings, transcripts, and field notes, be preserved for a specific number of years in a protected (locked for paper or password-protected for digital) form and then destroyed, so be sure that your plans adhere to the IRB requirements. Be sure you keep any materials that might be relevant for future related research or for answering questions people may ask later about your project.

And then what? Well, then it is time to move on to your next research project. Research is a long-term endeavor, not a one-time-only activity. We build our skills and our expertise as we continue to pursue research. So keep at it.

• Find a short article that uses qualitative methods. The sociological magazine Contexts is a good place to find such pieces. Write an abstract of the article.
• Choose a sociological journal article on a topic you are interested in that uses some form of qualitative methods and is at least 20 pages long. Rewrite the article as a five-page research summary accessible to non-scholarly audiences.
• Choose a concept or idea you have learned in this course and write an explanation of it using the Up-Goer Five Text Editor ( https://www.splasho.com/upgoer5/ ), a website that restricts your writing to the 1,000 most common English words. What was this experience like? What did it teach you about communicating with people who have a more limited English-language vocabulary—and what did it teach you about the utility of having access to complex academic language?
• Select five or more sociological journal articles that all use the same basic type of qualitative methods (interviewing, ethnography, documents, or visual sociology). Using what you have learned about coding, code the methods sections of each article, and use your coding to figure out what is common in how such articles discuss their research design, data collection, and analysis methods.
• Return to an exercise you completed earlier in this course and revise your work. What did you change? How did revising impact the final product?
• Find a quote from the transcript of an interview, a social media post, or elsewhere that has not yet been interpreted or explained. Write a paragraph that includes the quote along with an explanation of its sociological meaning or significance.

The style or personality of a piece of writing, including such elements as tone, word choice, syntax, and rhythm.

A quotation, usually one of some length, which is set off from the main text by being indented on both sides rather than being placed in quotation marks.

A classification of written or artistic work based on form, content, and style.

A short summary of a text written from the perspective of a reader rather than from the perspective of an author.

Social Data Analysis Copyright © 2021 by Mikaila Mariel Lemonik Arthur is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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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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Commentary: Writing and Evaluating Qualitative Research Reports

Yelena p. wu.

1 Division of Public Health, Department of Family and Preventive Medicine, University of Utah,

2 Cancer Control and Population Sciences, Huntsman Cancer Institute,

Deborah Thompson

3 Department of Pediatrics-Nutrition, USDA/ARS Children’s Nutrition Research Center, Baylor College of Medicine,

Karen J. Aroian

4 College of Nursing, University of Central Florida,

Elizabeth L. McQuaid

5 Department of Psychiatry and Human Behavior, Brown University, and

Janet A. Deatrick

6 School of Nursing, University of Pennsylvania

Objective  To provide an overview of qualitative methods, particularly for reviewers and authors who may be less familiar with qualitative research. Methods  A question and answer format is used to address considerations for writing and evaluating qualitative research. Results and Conclusions  When producing qualitative research, individuals are encouraged to address the qualitative research considerations raised and to explicitly identify the systematic strategies used to ensure rigor in study design and methods, analysis, and presentation of findings. Increasing capacity for review and publication of qualitative research within pediatric psychology will advance the field’s ability to gain a better understanding of the specific needs of pediatric populations, tailor interventions more effectively, and promote optimal health.

The Journal of Pediatric Psychology (JPP) has a long history of emphasizing high-quality, methodologically rigorous research in social and behavioral aspects of children’s health ( Palermo, 2013 , 2014 ). Traditionally, research published in JPP has focused on quantitative methodologies. Qualitative approaches are of interest to pediatric psychologists given the important role of qualitative research in developing new theories ( Kelly & Ganong, 2011 ), illustrating important clinical themes ( Kars, Grypdonck, de Bock, & van Delden, 2015 ), developing new instruments ( Thompson, Bhatt, & Watson, 2013 ), understanding patients’ and families’ perspectives and needs ( Bevans, Gardner, Pajer, Riley, & Forrest, 2013 ; Lyons, Goodwin, McCreanor, & Griffin, 2015 ), and documenting new or rarely examined issues ( Haukeland, Fjermestad, Mossige, & Vatne, 2015 ; Valenzuela et al., 2011 ). Further, these methods are integral to intervention development ( Minges et al., 2015 ; Thompson et al., 2007 ) and understanding intervention outcomes ( de Visser et al., 2015 ; Hess & Straub, 2011 ). For example, when designing an intervention, qualitative research can identify patient and family preferences for and perspectives on desirable intervention characteristics and perceived needs ( Cassidy et al., 2013 ; Hess & Straub, 2011 ; Thompson, 2014 ), which may lead to a more targeted, effective intervention.

Both qualitative and quantitative approaches are concerned with issues such as generalizability of study findings (e.g., to whom the study findings can be applied) and rigor. However, qualitative and quantitative methods have different approaches to these issues. The purpose of qualitative research is to contribute knowledge or understanding by describing phenomenon within certain groups or populations of interest. As such, the purpose of qualitative research is not to provide generalizable findings. Instead, qualitative research has a discovery focus and often uses an iterative approach. Thus, qualitative work is often foundational to future qualitative, quantitative, or mixed-methods studies.

At the time of this writing, three of six current calls for papers for special issues of JPP specifically note that manuscripts incorporating qualitative approaches would be welcomed. Despite apparent openness to broadening JPP’s emphasis beyond its traditional quantitative approach, few published articles have used qualitative methods. For example, of 232 research articles published in JPP from 2012 to 2014 (excluding commentaries and reviews), only five used qualitative methods (2% of articles).

The goal of the current article is to present considerations for writing and evaluating qualitative research within the context of pediatric psychology to provide a framework for writing and reviewing manuscripts reporting qualitative findings. The current article may be especially useful to reviewers and authors who are less familiar with qualitative methods. The tenets presented here are grounded in the well-established literature on reporting and evaluating qualitative research, including guidelines and checklists ( Eakin & Mykhalovskiy, 2003 ; Elo et al., 2014 ; Mays & Pope, 2000 ; Tong, Sainsbury, & Craig, 2007 ). For example, the Consolidated Criteria for Reporting Qualitative Research checklist describes essential elements for reporting qualitative findings ( Tong et al., 2007 ). Although the considerations presented in the current manuscript have broad applicability to many fields, examples were purposively selected for the field of pediatric psychology.

Our goal is that this article will stimulate publication of more qualitative research in pediatric psychology and allied fields. More specifically, the goal is to encourage high-quality qualitative research by addressing key issues involved in conducting qualitative studies, and the process of conducting, reporting, and evaluating qualitative findings. Readers interested in more in-depth information on designing and implementing qualitative studies, relevant theoretical frameworks and approaches, and analytic approaches are referred to the well-developed literature in this area ( Clark, 2003 ; Corbin & Strauss, 2008 ; Creswell, 1994 ; Eakin & Mykhalovskiy, 2003 ; Elo et al., 2014 ; Mays & Pope, 2000 ; Miles, Huberman, & Saldaña, 2013 ; Ritchie & Lewis, 2003 ; Saldaña, 2012 ; Sandelowski, 1995 , 2010 ; Tong et al., 2007 ; Yin, 2015 ). Researchers new to qualitative research are also encouraged to obtain specialized training in qualitative methods and/or to collaborate with a qualitative expert in an effort to ensure rigor (i.e., validity).

We begin the article with a definition of qualitative research and an overview of the concept of rigor. While we recognize that qualitative methods comprise multiple and distinct approaches with unique purposes, we present an overview of considerations for writing and evaluating qualitative research that cut across qualitative methods. Specifically, we present basic principles in three broad areas: (1) study design and methods, (2) analytic considerations, and (3) presentation of findings (see Table 1 for a summary of the principles addressed in each area). Each area is addressed using a “question and answer” format. We present a brief explanation of each question, options for how one could address the issue raised, and a suggested recommendation. We recognize, however, that there are no absolute “right” or “wrong” answers and that the most “right” answer for each situation depends on the specific study and its purpose. In fact, our strongest recommendation is that authors of qualitative research manuscripts be explicit about their rationale for design, analytic choices, and strategies so that readers and reviewers can evaluate the rationale and rigor of the study methods.

Summary of Overarching Principles to Address in Qualitative Research Manuscripts

What Is Qualitative Research?

Qualitative methods are used across many areas of health research, including health psychology ( Gough & Deatrick, 2015 ), to study the meaning of people’s lives in their real-world roles, represent their views and perspectives, identify important contextual conditions, discover new or additional insights about existing social and behavioral concepts, and acknowledge the contribution of multiple perspectives ( Yin, 2015 ). Qualitative research is a family of approaches rather than a single approach. There are multiple and distinct qualitative methodologies or stances (e.g., constructivism, post-positivism, critical theory), each with different underlying ontological and epistemological assumptions ( Lincoln, Lynham, & Guba, 2011 ). However, certain features are common to most qualitative approaches and distinguish qualitative research from quantitative research ( Creswell, 1994 ).

Key to all qualitative methodologies is that multiple perspectives about a phenomenon of interest are essential, and that those perspectives are best inductively derived or discovered from people with personal experience regarding that phenomenon. These perspectives or definitions may differ from “conventional wisdom.” Thus, meanings need to be discovered from the population under study to ensure optimal understanding. For instance, in a recent qualitative study about texting while driving, adolescents said that they did not approve of texting while driving. The investigators, however, discovered that the respondents did not consider themselves driving while a vehicle was stopped at a red light. In other words, the respondents did approve of texting while stopped at a red light. In addition, the adolescents said that they highly valued being constantly connected via texting. Thus, what is meant by “driving” and the value of “being connected” need to be considered when approaching the issue of texting while driving with adolescents ( McDonald & Sommers, 2015 ).

Qualitative methods are also distinct from a mixed-method approach (i.e., integration of qualitative and quantitative approaches; Creswell, 2013b ). A mixed-methods study may include a first phase of quantitative data collection that provides results that inform a second phase of the study that includes qualitative data collection, or vice versa. A mixed-methods study may also include concurrent quantitative and qualitative data collection. The timing, priority, and stage of integration of the two approaches (quantitative and qualitative) are complex and vary depending on the research question; they also dictate how to attend to differing qualitative and quantitative principles ( Creswell et al., 2011 ). Understanding the basic tenets of qualitative research is preliminary to integrating qualitative research with another approach that has different tenets. A full discussion of the integration of qualitative and quantitative research approaches is beyond the scope of this article. Readers interested in the topic are referred to one of the many excellent resources on the topic ( Creswell, 2013b ).

What Are Typical Qualitative Research Questions?

Qualitative research questions are typically open-ended and are framed in the spirit of discovery and exploration and to address existing knowledge gaps. The current manuscript provides exemplar pediatric qualitative studies that illustrate key issues that arise when reporting and evaluating qualitative studies. Example research questions that are contained in the studies cited in the current manuscript are presented in Table 2 .

Example Qualitative Research Questions From the Pediatric Literature

What Are Rigor and Transparency in Qualitative Research?

There are several overarching principles with unique application in qualitative research, including definitions of scientific rigor and the importance of transparency. Quantitative research generally uses the terms reliability and validity to describe the rigor of research, while in qualitative research, rigor refers to the goal of seeking to understand the tacit knowledge of participants’ conception of reality ( Polanyi, 1958 ). For example, Haukeland and colleagues (2015) used qualitative analysis to identify themes describing the emotional experiences of a unique and understudied population—pediatric siblings of children with rare medical conditions such as Turner syndrome and Duchenne muscular dystrophy. Within this context, the authors’ rendering of the diverse and contradictory emotions experienced by siblings of children with these rare conditions represents “rigor” within a qualitative framework.

While debate exists regarding the terminology describing and strategies for strengthening scientific rigor in qualitative studies ( Guba, 1981 ; Morse, 2015a , 2015b ; Sandelowski, 1993a ; Whittemore, Chase, & Mandle, 2001 ), little debate exists regarding the importance of explaining strategies used to strengthen rigor. Such strategies should be appropriate for the specific study; therefore, it is wise to clearly describe what is relevant for each study. For example, in terms of strengthening credibility or the plausibility of data analysis and interpretation, prolonged engagement with participants is appropriate when conducting an observational study (e.g., observations of parent–child mealtime interactions; Hughes et al., 2011 ; Power et al., 2015 ). For an interview-only study, however, it would be more practical to strengthen credibility through other strategies (e.g., keeping detailed field notes about the interviews included in the analysis).

Dependability is the stability of a data analysis protocol. For instance, stepwise development of a coding system from an “a priori” list of codes based on the underlying conceptual framework or existing literature (e.g., creating initial codes for potential barriers to medication adherence based on prior studies) may be essential for analysis of data from semi-structured interviews using multiple coders. But this may not be the ideal strategy if the purpose is to inductively derive all possible coding categories directly from data in an area where little is known. For some research questions, the strategy may be to strengthen confirmability or to verify a specific phenomenon of interest using different sources of data before generating conclusions. This process, which is commonly referred to in the research literature as triangulation, may also include collecting different types of data (e.g., interview data, observational data), using multiple coders to incorporate different ways of interpreting the data, or using multiple theories ( Krefting, 1991 ; Ritchie & Lewis, 2003 ). Alternatively, another investigator may use triangulation to provide complementarity data ( Krefting, 1991 ) to garner additional information to deepen understanding. Because the purpose of qualitative research is to discover multiple perspectives about a phenomenon, it is not necessarily appropriate to attain concordance across studies or investigators when independently analyzing data. Some qualitative experts also believe that it is inappropriate to use triangulation to confirm findings, but this debate has not been resolved within the field ( Ritchie & Lewis, 2003 ; Tobin & Begley, 2004 ). More agreement exists, however, regarding the value of triangulation to complement, deepen, or expand understanding of a particular topic or issue ( Ritchie & Lewis, 2003 ). Finally, instead of basing a study on a sample that allows for generalizing statistical results to other populations, investigators in qualitative research studies are focused on designing a study and conveying the results so that the reader understands the transferability of the results. Strategies for transferability may include explanations of how the sample was selected and descriptive characteristics of study participants, which provides a context for the results and enables readers to decide if other samples share critical attributes. A study is deemed transferable if relevant contextual features are common to both the study sample and the larger population.

Strategies to enhance rigor should be used systematically across each phase of a study. That is, rigor needs to be identified, managed, and documented throughout the research process: during the preparation phase (data collection and sampling), organization phase (analysis and interpretation), and reporting phase (manuscript or final report; Elo et al., 2014 ). From this perspective, the strategies help strengthen the trustworthiness of the overall study (i.e., to what extent the study findings are worth heeding; Eakin & Mykhalovskiy, 2003 ; Lincoln & Guba, 1985 ).

A good example of managing and documenting rigor and trustworthiness can be found in a study of family treatment decisions for children with cancer ( Kelly & Ganong, 2011 ). The researchers describe how they promoted the rigor of the study and strengthening its credibility by triangulating data sources (e.g., obtaining data from children’s custodial parents, stepparents, etc.), debriefing (e.g., holding detailed conversations with colleagues about the data and interpretations of the data), member checking (i.e., presenting preliminary findings to participants to obtain their feedback and interpretation), and reviewing study procedure decisions and analytic procedures with a second party.

Transparency is another key concept in written reports of qualitative research. In other words, enough detail should be provided for the reader to understand what was done and why ( Ritchie & Lewis, 2003 ). Examples of information that should be included are a clear rationale for selecting a particular population or people with certain characteristics, the research question being investigated, and a meaningful explanation of why this research question was selected (i.e., the gap in knowledge or understanding that is being investigated; Ritchie & Lewis, 2003 ). Clearly describing recruitment, enrollment, data collection, and data analysis or extraction methods are equally important ( Dixon-Woods, Shaw, Agarwal, & Smith, 2004 ). Coherency among methods and transparency about research decisions adds to the robustness of qualitative research ( Tobin & Begley, 2004 ) and provides a context for understanding the findings and their implications.

Study Design and Methods

Is qualitative research hypothesis driven.

In contrast to quantitative research, qualitative research is not typically hypothesis driven ( Creswell, 1994 ; Ritchie & Lewis, 2003 ). A risk associated with using hypotheses in qualitative research is that the findings could be biased by the hypotheses. Alternatively, qualitative research is exploratory and typically guided by a research question or conceptual framework rather than hypotheses ( Creswell, 1994 ; Ritchie & Lewis, 2003 ). As previously stated, the goal of qualitative research is to increase understanding in areas where little is known by developing deeper insight into complex situations or processes. According to Richards and Morse (2013) , “If you know what you are likely to find, …  you should not be working qualitatively” (p. 28). Thus, we do not recommend that a hypothesis be stated in manuscripts presenting qualitative data.

What Is the Role of Theory in Qualitative Research?

Consistent with the exploratory nature of qualitative research, one particular qualitative method, grounded theory, is used specifically for discovering substantive theory (i.e., working theories of action or processes developed for a specific area of concern; Bryant & Charmaz, 2010 ; Glaser & Strauss, 1967 ). This method uses a series of structured steps to break down qualitative data into codes, organize the codes into conceptual categories, and link the categories into a theory that explains the phenomenon under study. For example, Kelly and Ganong (2011) used grounded theory methods to produce a substantive theory about how single and re-partnered parents (e.g., households with a step-parent) made treatment decisions for children with childhood cancer. The theory of decision making developed in this study included “moving to place,” which described the ways in which parents from different family structures (e.g., single and re-partnered parents) were involved in the child’s treatment decision-making. The resulting theory also delineated the causal conditions, context, and intervening factors that contributed to the strategies used for moving to place.

Theories may be used in other types of qualitative research as well, serving as the impetus or organizing framework for the study ( Sandelowski, 1993b ). For example, Izaguirre and Keefer (2014) used Social Cognitive Theory ( Bandura, 1986 ) to investigate self-efficacy among adolescents with inflammatory bowel disease. The impetus for selecting the theory was to inform the development of a self-efficacy measure for adolescent self-management. In another study on health care transition in youth with Type 1 Diabetes ( Pierce, Wysocki, & Aroian, 2016 ), the investigators adapted a social-ecological model—the Socio-ecological Model of Adolescent and Young Adult Transition Readiness (SMART) model ( Schwartz, Tuchman, Hobbie, & Ginsberg, 2011 )—to their study population ( Pierce & Wysocki, 2015 ). Pierce et al. (2016) are currently using the adapted SMART model to focus their data collection and structure the preliminary analysis of their data about diabetes health care transition.

Regardless of whether theory is induced from data or selected in advance to guide the study, consistent with the principle of transparency , its role should be clearly identified and justified in the research publication ( Bradbury-Jones, Taylor, & Herber, 2014 ; Kelly, 2010 ). Methodological congruence is an important guiding principle in this regard ( Richards & Morse, 2013 ). If a theory frames the study at the outset, it should guide and direct all phases. The resulting publication(s) should relate the phenomenon of interest and the research question(s) to the theory and specify how the theory guided data collection and analysis. The publication(s) should also discuss how the theory fits with the finished product. For instance, authors should describe how the theory provided a framework for the presentation of the findings and discuss the findings in context with the relevant theoretical literature.

A study examining parents’ motivations to promote vegetable consumption in their children ( Hingle et al., 2012 ) provides an example of methodological congruence. The investigators adapted the Model of Goal Directed Behavior ( Bagozzi & Pieters, 1998 ) for parenting practices relevant to vegetable consumption (Model of Goal Directed Vegetable Parenting Practices; MGDVPP). Consistent with the adapted theoretical model and in keeping with the congruence principle, interviews were guided by the theoretical constructs contained within the MGDVPP, including parents’ attitudes, subjective norms, and perceived behavioral control related to promoting vegetable consumption in children ( Hingle et al., 2012 ). The study discovered that the adapted model successfully identified parents’ motivations to encourage their children to eat more vegetables.

The use of the theory should be consistent with the basic goal of qualitative research, which is discovery. Alternatively stated, theories should be used as broad orienting frameworks for exploring topical areas without imposing preconceived ideas and biases. The theory should be consistent with the study findings and not be used to force-fit the researcher’s interpretation of the data ( Sandelowski, 1993b ). Divergence from the theory when it does not fit the study findings is illustrated in a qualitative study of hypertension prevention beliefs in Hispanics ( Aroian, Peters, Rudner, & Waser, 2012 ). This study used the Theory of Planned Behavior as a guiding theoretical framework but found that coding separately for normative and control beliefs was not the best organizing schema for presenting the study findings. When divergence from the original theory occurs, the research report should explain and justify how and why the theory was modified ( Bradbury-Jones et al., 2014 ).

What Are Typical Sampling Methods in Qualitative Studies?

Qualitative sampling methods should be “purposeful” ( Coyne, 1997 ; Patton, 2015 ; Tuckett, 2004 ). Purposeful sampling is based on the study purpose and investigator judgments about which people and settings will provide the richest information for the research questions. The logic underlying this type of sampling differs from the logic underlying quantitative sampling ( Patton, 2015 ). Quantitative research strives for empirical generalization. In qualitative studies, generalizability beyond the study sample is typically not the intent; rather, the focus is on deriving depth and context-embedded meaning for the relevant study population.

Purposeful sampling is a broad term. Theoretical sampling is one particular type of purposeful sampling unique to grounded theory methods ( Coyne, 1997 ). In theoretical sampling, study participants are chosen according to theoretical categories that emerge from ongoing data collection and analyses ( Bryant & Charmaz, 2010 ). Data collection and analysis are conducted concurrently to allow generating and testing hypotheses that emerge from analyzing incoming data. The following example from the previously mentioned qualitative interview study about transition from pediatric to adult care in adolescents with type 1 diabetes ( Pierce et al., 2016 ) illustrates the process of theoretical sampling: An adolescent study participant stated that he was “turned off” by the “childish” posters in his pediatrician’s office. He elaborated that he welcomed transitioning to adult care because his diabetes was discovered when he was 18, an age when he reportedly felt more “mature” than most pediatric patients. These data were coded as “developmental misfit” and prompted a tentative hypothesis about developmental stage at entry for pediatric diabetes care and readiness for health care transition. Examining this hypothesis prompted seeking study participants who varied according to age or developmental stage at time of diagnosis to examine the theoretical relevance of an emerging theme about developmental fit.

Not all purposeful sampling, however, is “theoretical.” For example, ethnographic studies typically seek to understand a group’s cultural beliefs and practices ( Creswell, 2013a ). Consistent with this purpose, researchers conducting an ethnographic study might purposefully select study participants according to specific characteristics that reflect the social roles and positions in a given group or society (e.g., socioeconomic status, education; Johnson, 1990 ).

Random sampling is generally not used in qualitative research. Random selection requires a sufficiently large sample to maximize the potential for chance and, as will be discussed below, sample size is intentionally small in qualitative studies. However, random sampling may be used to verify or clarify findings ( Patton, 2015 ). Validating study findings with a randomly selected subsample can be used to address the possibility that a researcher is inadvertently giving greater attention to cases that reinforce his or her preconceived ideas.

Regardless of the sampling method used, qualitative researchers should clearly describe the sampling strategy and justify how it fits the study when reporting study findings (transparency). A common error is to refer to theoretical sampling when the cases were not chosen according to emerging theoretical concepts. Another common error is to apply sampling principles from quantitative research (e.g., cluster sampling) to convince skeptical reviewers about the rigor or validity of qualitative research. Rigor is best achieved by being purposeful, making sound decisions, and articulating the rationale for those decisions. As mentioned earlier in the discussion of transferability , qualitative researchers are encouraged to describe their methods of sample selection and descriptive characteristics about their sample so that readers and reviewers can judge how the current sample may differ from others. Understanding the characteristics of each qualitative study sample is essential for the iterative nature of qualitative research whereby qualitative findings inform the development of future qualitative, quantitative, or mixed-methods studies. Reviewers should evaluate sampling decisions based on how they fit the study purpose and how they influence the quality of the end product.

What Sample Size Is Needed for Qualitative Research?

No definitive rules exist about sample size in qualitative research. However, sample sizes are typically smaller than those in quantitative studies ( Patton, 2015 ). Small samples often generate a large volume of data and information-rich cases, ultimately leading to insight regarding the phenomenon under study ( Patton, 2015 ; Ritchie & Lewis, 2003 ). Sample sizes of 20–30 cases are typical, but a qualitative sample can be even smaller under some circumstances ( Mason, 2010 ).

Sample size adequacy is evaluated based on the quality of the study findings, specifically the full development of categories and inter-relationships or the adequacy of information about the phenomenon under study ( Corbin & Strauss, 2008 ; Ritchie & Lewis, 2003 ). Small sample sizes are of concern if they do not result in these outcomes. Data saturation (i.e., the point at which no new information, categories, or themes emerge) is often used to judge informational adequacy ( Morgan, 1998 ; Ritchie & Lewis, 2003 ). Although enough participants should be included to obtain saturation ( Morgan, 1998 ), informational adequacy pertains to more than sample size. It is also a function of the quality of the data, which is influenced by study participant characteristics (e.g., cognitive ability, knowledge, representativeness) and the researcher’s data-gathering skills and analytical ability to generate meaningful findings ( Morse, 2015b ; Patton, 2015 ).

Sample size is also influenced by type of qualitative research, the study purpose, the sample, the depth and complexity of the topic investigated, and the method of data collection. In general, the more heterogeneous the sample, the larger the sample size, particularly if the goal is to investigate similarities and differences by specific characteristics ( Ritchie & Lewis, 2003 ). For instance, in a study to conduct an initial exploration of factors underlying parents’ motivations to use good parenting practices, theoretical saturation (i.e., the point at which no new information, categories, or themes emerge) was obtained with a small sample ( n  = 15), most likely because the study was limited to parents of young children ( Hingle et al., 2012 ). If the goal of the study had been, for example, to identify racial/ethnic, gender, or age differences in food parenting practices, a larger sample would likely be needed to obtain saturation or informational adequacy.

Studies that seek to understand maximum variation in a phenomenon might also need a larger sample than one that is seeking to understand extreme or atypical cases. For example, a qualitative study of diet and physical activity in young Australian men conducted focus groups to identify perceived motivators and barriers to healthy eating and physical activity and examine the influence of body weight on their perceptions. Examining the influence of body weight status required 10 focus groups to allow for group assignment based on body mass index ( Ashton et al., 2015 ). More specifically, 61 men were assigned to a healthy-weight focus group ( n  = 3), an overweight/obese focus group ( n  = 3), or a mixed-weight focus group ( n  = 4). Had the researcher not been interested in whether facilitators and barriers differed by weight status, its likely theoretical saturation could have been obtained with fewer groups. Depth of inquiry also influences sample size ( Sandelowski, 1995 ). For instance, an in-depth analysis of an intervention for children with cancer and their families included 16 family members from three families. Study data comprised 52 hrs of videotaped intervention sessions and 10 interviews ( West, Bell, Woodgate, & Moules, 2015 ). Depth was obtained through multiple data points and types of data, which justified sampling only a few families.

Authors of publications describing qualitative findings should show evidence that the data were “saturated” by a sample with sufficient variation to permit detailing shared and divergent perspectives, meanings, or experiences about the topic of inquiry. Decisions related to the sample (e.g., targeted recruitment) should be detailed in publications so that peer reviewers have the context for evaluating the sample and determining how the sample influenced the study findings ( Patton, 2015 ).

Qualitative Data Analysis

When conducting qualitative research, voluminous amounts of data are gathered and must be prepared (i.e., transcribed) and managed. During the analytic process, data are systematically transformed through identifying, defining, interpreting, and describing findings that are meant to comprehensively describe the phenomenon or the abstract qualities that they have in common. The process should be systematic ( dependability ) and well-documented in the analysis section of a qualitative manuscript. For example, Kelly and Ganong (2011) , in their study of medical treatment decisions made by families of children with cancer, described their analytic procedure by outlining their approach to coding and use of memoing (e.g., keeping careful notes about emerging ideas about the data throughout the analytic process), comparative analysis (e.g., comparing data against one another and looking for similarities and differences), and diagram drawing (e.g., pictorially representing the data structure, including relationships between codes).

How Should Researchers Document Coding Reliability?

Because the intent of qualitative research is to account for multiple perspectives, the goal of qualitative analysis is to comprehensively incorporate those perspectives into discernible findings. Researchers accustomed to doing quantitative studies may expect authors to quantify interrater reliability (e.g., kappa statistic) but this is not typical in qualitative research. Rather, the emphasis in qualitative research is on (1) training those gathering data to be rigorous and produce high-quality data and on (2) using systematic processes to document key decisions (e.g., code book), clear direction, and open communication among team members during data analysis. The goal is to make the most of the collective insight of the investigative team to triangulate or complement each other’s efforts to process and interpret the data. Instead of evaluating if two independent raters came to the same numeric rating, reviewers of qualitative manuscripts should judge to what extent the overall process of coding, data management, and data interpretation were systematic and rigorous. Authors of qualitative reports should articulate their coding procedures for others to evaluate. Together, these strategies promote trustworthiness of the study findings.

An example of how these processes are described in the report of a qualitative study is as follows:

The first two authors independently applied the categories to a sample of two interviews and compared their application of the categories to identify lack of clarity and overlap in categories. The investigators created a code book that contained a definition of categories, guidelines for their application, and excerpts of data exemplifying the categories. The first two authors independently coded the data and compared how they applied the categories to the data and resolved any differences during biweekly meetings. ATLAS.ti, version 6.2, was used to document and accommodate ongoing changes and additions to the coding structure ( Palma et al., 2015 , p. 224).

Do I Need to Use a Specialized Qualitative Data Software Program for Analysis?

Multiple computer software packages for qualitative data analysis are currently available ( Silver & Lewins, 2014 ; Yin, 2015 ). These packages allow the researcher to import qualitative data (e.g., interview transcripts) into the software program and organize data segments (e.g., delineate which interview excerpts are relevant to particular themes). Qualitative analysis software can be useful for organizing and sorting through data, including during the analysis phase. Some software programs also offer sophisticated coding and visualization capabilities that facilitate and enhance interpretation and understanding. For example, if data segments are coded by specific characteristics (e.g., gender, race/ethnicity), the data can be sorted and analyzed by these characteristics, which may contribute to an understanding of whether and/or how a particular phenomenon may vary by these characteristics.

The strength of computer software packages for qualitative data analysis is their potential to contribute to methodological rigor by organizing the data for systematic analyses ( John & Johnson, 2000 ; MacMillan & Koenig, 2004 ). However, the programs do not replace the researchers’ analyses. The researcher or research team is ultimately responsible for analyzing the data, identifying the themes and patterns, and placing the findings within the context of the literature. In other words, qualitative data analysis software programs contribute to, but do not ensure scientific rigor or “objectivity” in, the analytic process. In fact, using a software program for analysis is not essential if the researcher demonstrates the use of alternative tools and procedures for rigor.

Presentation of Findings

Should there be overlap between presentation of themes in the results and discussion sections.

Qualitative papers sometimes combine results and discussion into one section to provide a cohesive presentation of the findings along with meaningful linkages to the existing literature ( Burnard, 2004 ; Burnard, Gill, Stewart, Treasure, & Chadwick, 2008 ). Although doing so is an acceptable method for reporting qualitative findings, some journals prefer the two sections to be distinct.

When the journal style is to distinguish the two sections, the results section should describe the findings, that is, the themes, while the discussion section should pull the themes together to make larger-level conclusions and place the findings within the context of the existing literature. For instance, the findings section of a study of how rural African-American adolescents, parents, and community leaders perceived obesity and topics for a proposed obesity prevention program, contained a description of themes about adolescent eating patterns, body shape, and feedback on the proposed weight gain prevention program according to each subset of participants (i.e., adolescents, parents, community leaders). The discussion section then put these themes within the context of findings from prior qualitative and intervention studies in related populations ( Cassidy et al., 2013 ). In the Discussion, when making linkages to the existing literature, it is important to avoid the temptation to extrapolate beyond the findings or to over-interpret them ( Burnard, 2004 ). Linkages between the findings and the existing literature should be supported by ample evidence to avoid spurious or misleading connections ( Burnard, 2004 ).

What Should I Include in the Results Section?

The results section of a qualitative research report is likely to contain more material than customary in quantitative research reports. Findings in a qualitative research paper typically include researcher interpretations of the data as well as data exemplars and the logic that led to researcher interpretations ( Sandelowski & Barroso, 2002 ). Interpretation pertains to the researcher breaking down and recombining the data and creating new meanings (e.g., abstract categories, themes, conceptual models). Select quotes from interviews or other types of data (e.g., participant observation, focus groups) are presented to illustrate or support researcher interpretations. Researchers trained in the quantitative tradition, where interpretation is restricted to the discussion section, may find this surprising; however, in qualitative methods, researcher interpretations represent an important component of the study results. The presentation of the findings, including researcher interpretations (e.g., themes) and data (e.g., quotes) supporting those interpretations, adds to the trustworthiness of the study ( Elo et al., 2014 ).

The Results section should contain a balance between data illustrations (i.e., quotes) and researcher interpretations ( Lofland & Lofland, 2006 ; Sandelowski, 1998 ). Because interpretation arises out of the data, description and interpretation should be combined. Description should be sufficient to support researcher interpretations, and quotes should be used judiciously ( Morrow, 2005 ; Sandelowski, 1994 ). Not every theme needs to be supported by multiple quotes. Rather, quotes should be carefully selected to provide “voice” to the participants and to help the reader understand the phenomenon from the participant’s perspective within the context of the researcher’s interpretation ( Morrow, 2005 ; Ritchie & Lewis, 2003 ). For example, researchers who developed a grounded theory of sexual risk behavior of urban American Indian adolescent girls identified desire for better opportunities as a key deterrent to neighborhood norms for early sexual activity. They illustrated this theme with the following quote: “I don’t want to live in the ‘hood and all that…My sisters are stuck there because they had babies. That isn’t going to happen to me” ( Saftner, Martyn, Momper, Loveland-Cherry, & Low, 2015 , p. 372).

There is no precise formula for the proportion of description to interpretation. Both descriptive and analytic excess should be avoided ( Lofland & Lofland, 2006 ). The former pertains to presentation of unedited field notes or interview transcripts rather than selecting and connecting data to analytic concepts that explain or summarize the data. The latter pertains to focusing on the mechanics of analysis and interpretation without substantiating researcher interpretations with quotes. Reviewer requests for methodological rigor can result in researchers writing qualitative research papers that suffer from analytic excess ( Sandelowski & Barroso, 2002 ). Page limitations of most journals provide a safeguard against descriptive excess, but page limitations should not circumvent researchers from providing the basis for their interpretations.

Additional potential problems with qualitative results sections include under-elaboration, where themes are too few and not clearly defined. The opposite problem, over-elaboration, pertains to too many analytic distinctions that could be collapsed under a higher level of abstraction. Quotes can also be under- or over-interpreted. Care should be taken to ensure the quote(s) selected clearly support the theme to which they are attached. And finally, findings from a qualitative study should be interesting and make clear contributions to the literature ( Lofland & Lofland, 2006 ; Morse, 2015b ).

Should I Quantify My Results? (e.g., Frequency With Which Themes Were Endorsed)

There is controversy over whether to quantify qualitative findings, such as providing counts for the frequency with which particular themes are endorsed by study participants ( Morgan, 1993 ; Sandelowski, 2001 ). Qualitative papers usually report themes and patterns that emerge from the data without quantification ( Dey, 1993 ). However, it is possible to quantify qualitative findings, such as in qualitative content analysis. Qualitative content analysis is a method through which a researcher identifies the frequency with which a phenomenon, such as specific words, phrases, or concepts, is mentioned ( Elo et al., 2014 ; Morgan, 1993 ). Although this method may appeal to quantitative reviewers, it is important to note that this method only fits specific study purposes, such as studies that investigate the language used by a particular group when communicating about a specific topic. In addition, results may be quantified to provide information on whether themes appeared to be common or atypical. Authors should avoid using imprecise language, such as “some participants” or “many participants.” A good example of quantification of results to illustrate more or less typical themes comes from a manuscript describing a qualitative study of school nurses’ perceived barriers to addressing obesity with students and their families. The authors described that all but one nurse reported not having the resources they needed to discuss weight with students and families whereas one-quarter of nurses reported not feeling competent to discuss weight issues ( Steele et al., 2011 ). If quantification of findings is used, authors should provide justification that explains how quantification is consistent with the aims or goals of the study ( Sandelowski, 2001 ).

Conclusions

This article highlighted key theoretical and logistical considerations that arise in designing, conducting, and reporting qualitative research studies (see Table 1 for a summary). This type of research is vital for obtaining patient, family, community, and other stakeholder perspectives about their needs and interests, and will become increasingly critical as our models of health care delivery evolve. For example, qualitative research could contribute to the study of health care providers and systems with the goal of optimizing our health care delivery models. Given the increasing diversity of the populations we serve, qualitative research will also be critical in providing guidance in how to tailor health interventions to key characteristics and increase the likelihood of acceptable, effective treatment approaches. For example, applying qualitative research methods could enhance our understanding of refugee experiences in our health care system, clarify treatment preferences for emerging adults in the midst of health care transitions, examine satisfaction with health care delivery, and evaluate the applicability of our theoretical models of health behavior changes across racial and ethnic groups. Incorporating patient perspectives into treatment is essential to meeting this nation’s priority on patient-centered health care ( Institute of Medicine Committee on Quality of Health Care in America, 2001 ). Authors of qualitative studies who address the methodological choices addressed in this review will make important contributions to the field of pediatric psychology. Qualitative findings will lead to a more informed field that addresses the needs of a wide range of patient populations and produces effective and acceptable population-specific interventions to promote health.

Acknowledgments

The authors thank Bridget Grahmann for her assistance with manuscript preparation.

This work was supported by National Cancer Institute of the National Institutes of Health (K07CA196985 to Y.W.). This work is a publication of the United States Department of Agriculture/Agricultural Research Center (USDA/ARS), Children’s Nutrition Research Center, Department of Pediatrics, Baylor College of Medicine, Houston, Texas. It is also a publication of the USDA/ARS, Children’s Nutrition Research Center, Department of Pediatrics, Baylor College of Medicine, Houston, Texas, and funded in part with federal funds from the USDA/ARS under Cooperative Agreement No. 58‐6250‐0‐008 (to D.T.). The contents of this publication do not necessarily reflect the views or policies of the USDA, nor does mention of trade names, commercial products, or organizations imply endorsement from the U.S. government. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Conflicts of interest : None declared.

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

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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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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?

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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?

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do you have any material on Data collection

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Monograph Matters

Qualitative analysis: process and examples | powerpoint – 85.2.

Authors Laura Wray-Lake and Laura Abrams describe qualitative data analysis, with illustrative examples from their SRCD monograph,  Pathways to Civic Engagement Among Urban Youth of Color . This PowerPoint document includes presenter notes, making it an ideal resource for researchers learning about qualitative analysis and for instructors teaching about it in upper-level undergraduate or graduate courses.

Created by Laura Wray-Lake and Laura S. Abrams. All rights reserved.

Citation: Wray-Lake, L. & Abrams, L. S. (2020) Qualitative Analysis: Process and Examples [PowerPoint]. Retrieved from https://monographmatters.srcd.org/2020/05/12/teachingresources-qualitativeanalysis-powerpoint-85-2

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Use, analysis and presentation of qualitative data

The uses of qualitative data are broad and varied and have been discussed throughout the chapter. Qualitative findings may be published in peer reviewed journals, in non-peer reviewed journals, and in reports for funders and decision-makers. However, the raw data obtained from interviews and focus groups (transcripts of what was said), and observations (field notes on what was observed by the researcher) must first be analysed.

General considerations

No general consensus exists amongst qualitative researchers concerning the process of data analysis. Rather, there are a variety of approaches to analysis and interpretation. These reflect the particular theoretical perspectives or field within which the researcher is working. It could be argued that this is another way in which qualitative research methods significantly differ from quantitative approaches. In the latter, there exists really only one route from data to conclusions, and this is statistical analysis, although there are different statistical approaches available, depending upon the size, distribution, and type of data.

In contrast, the methods available for qualitative analysis vary considerably. However, many of the qualitative methods textbooks do attempt to identify some general features that are common to the analytical phase of qualitative research; these include the following:

• Some form of review of all the information to gain an initial sense of the data, these ideas might then be fed back to the informants for verification purposes.
• The process of organising the data into some manageable form. This is often described as 'reducing the data', and usually involves developing codes or categories. However, as will be argued below, this process can be potentially problematic if the desire of the researcher is to maintain the unique richness of qualitative forms of data.
• Interpreting the data
• Presenting it in some form, e.g. tables, prose, or diagrams.

Having identified these broad stages, it should nevertheless be stated that the process of qualitative analysis is not a linear but rather continuous and iterative (12). That is, an emergent analytical process which moves backwards and forwards from the data to analytical concepts, refining and synthesising the latter as more data becomes available. As has been consistently asserted above, the theoretical approach that informs a piece of qualitative research will essentially determine the process by which the data is to be analysed.

Most qualitative analysis involves induction, that is, interpreting the data in order to derive some theoretical framework or working hypothesis, proposition, or `essence' of the social processes under investigation. Findings are inducted from the data, to generate a theory from the concepts inherent within the data.

It is possible to use a deductive approach with qualitative data: for example, if one charts the frequency with which concept appears within the data as a means of summarising the content, or if a framework approach is used to organise each line of text. Such approaches are often called simple content analysis and may be used when analysing free-text entries in questionnaires, for example. It may be argued however that deductive approaches do not maximise the value available from qualitative data and that inductive approaches are more likely to reveal new theories and progress understanding about the field.

Steps in analysis

1)     managing data: the process of indexing/coding/labelling the data.

The process of coding is an essential first step in managing the analytical process. During coding, elements of the data that are conceived of as sharing some perceived commonality are indexed and linked. Codes can be used to simplify or reduce transcript data to manageable levels, the purpose being to achieve a simple conceptual schema. This process usually involves the exclusive index coding of segments of data text (“line by line coding”) in order to be able to eventually retrieve segments sharing a common code. Alternatively, coding can be used as a method to open up the data, thus enabling the researcher to think or conceptualise beyond the data itself. This allows for more in-depth analysis. The in-depth analysis can be undertaken in several ways.

2)     Main Approaches to Analysing Qualitative Data

In this section three main approaches to qualitative data analysis are discussed. In practice, qualitative researchers may incorporate elements of grounded theory, constant-comparison approaches, and even analytical induction elements when analysing the data. Moreover, there are additional approaches to analysing data that are not discussed here, such as interpretive phenomenological, narrative, and discourse analysis.

Thematic analysis

This method involves the identification and reporting of patterns – called themes – which are retrieved from the primary qualitative data. Thematic analysis has been described as an accessible form of qualitative analysis as it does not require development of theory (see “grounded theory” below for contrast). A “step by step” guide to undertaking thematic analysis can be found in a paper by Braun and Clarke (13).

This approach to analysis has been developed over time by the National Centre for Social Research (12). The term 'framework' derives from the 'thematic framework' which is the central component of this approach to data management and interpretative analysis. The thematic framework is utilised to classify and organise data according to key themes, concepts and emergent categories. Each research study requires its own distinctive thematic framework comprising of a series of main themes, subdivided by a succession of related sub-themes or topics. These categories evolve and are refined (as an iterative process) through the researcher's familiarisation with the raw data and the subsequent cross-sectional labelling. Once the researcher judges that they have a comprehensive list of main and sub-themes, each is then 'charted' or displayed in its own matrix. The response of each research subject is then allocated a row with each column representing a separate subtopic. The final stage of this data management component of 'framework' involves summarising or synthesising the original data from each case (subject) within the appropriate parts of the thematic framework. Gale et al. describe the steps involved when taking a Framework approach (14).

Analytical Induction (AI)

In analytical induction (AI) or `deviant case analysis', each section of the transcript (in the case of interviews or focus group discussions) or notes of an observation is not assigned a single code in a 'final and arbitrary interpretative act' but is merely the first stage in the process of analysis (15). Initially these codes will be generalised but they become progressively more elaborate as more data are examined. Once coding is completed, systematic comparisons are made within and between the labelled transcript data. In AI, generalisability of the final conclusions is achieved by focusing on the `deviant' or contradictory indexed items that emerge at this stage. An attempt is made to modify the initial analytical themes in order to embrace these deviant cases. This procedure is essential to guard against selective attention to data in order to provide a more systematic means of extending analytic thinking. There are clear merits to this deviant case approach applied to interview data analysis, particularly in its drawing attention to the importance of contradictions as being indicative of an important dynamic at work rather than some aberrant occurrence or utterance that cannot be fitted into a code.

Grounded theory

In grounded theory, a set of ideas (the “theory”) is generated from the concepts and constructs retrieved from the coding stages. However, the theory remains grounded in the data, and is obtained from analysis of the codes and “memos” noted during the coding process, which come together to create an overall theory explaining the phenomenon under investigation.

Constant-comparative approach

This is a method often employed as part of grounded theory and involves comparing newly acquired data with the dataset already collected. In this way, each new “unit” of data (e.g. a new interview transcript) is considered in terms of how it changes the developing theory and what it adds to the emerging theory. By using constant-comparative methods it is possible to identify when theoretical saturation is reached as the additional data add little to the established findings. The simultaneous collection of data and analysis is an important feature of qualitative research and its iterative nature therefore allows the researcher to optimise the selection of participants based on features that may be of interested given the emerging findings.

Presentation

The challenges faced in qualitative research reporting do differ somewhat from those faced by quantitative researchers, and this primarily relates to the different forms of data that are being analysed and the interpretative approach to analysis. This requires the following concerns to be addressed in the final report:

• A discussion in the report of the potential transferability of the qualitative findings to other settings.
• There needs to be a discussion of the methods utilised and the reasons why they were appropriate to the object under investigation.
• It needs to be demonstrated that the conclusions drawn within the study are consistent with the evidence. The interpretative analysis needs to be presented in a transparent way so that the reader can follow the processes leading to the conclusions.
• Presenting the depth and richness of qualitative data is a challenge as they cannot be set-out in a neat series of graphs as would be typically found within quantitative research reports. Nevertheless, the imaginative use of diagrams and other schematics to illustrate the analytical process and findings can be a very useful way of simplifying the complexity of the iterative process of the gradual refinement of analytical categories.
• Qualitative methods are used precisely because of their potential to investigate and explain complex and diverse social phenomena, and therefore a report or presentation which focuses only on one element of the findings will be misleading. Any apparent contradictions or inconsistencies that emerged need to be reported upon in as much detail as the recurrent themes found within the study.
• Including verbatim quotes from the research subjects is a very useful way of illustrating the main themes that emerged from the study and in demonstrating the reliability of the conclusions. However, this can be overdone, resulting in an overlong narrative which distracts from the main findings.

Moreover, guidelines for best practice in reporting qualitative research have been produced (16).

© I Crinson & M Leontowitsch 2006, G Morgan 2016

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Analysing and presenting qualitative data

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A Short Guide to Qualitative Studies for Dental Researchers

Faaiz Alhamdani

The goal of medical care is to improve the patient’s quality of life (QOL) by maintaining function and well-being. Consequently, there is an increasing consensus that the patient’s perspective is pivotal in monitoring the outcomes of medical care in general. This is particularly important when management decisions are not clear-cut through a lack of clinical evidence. In such cases, it is imperative to study the health problems from all possible aspects. To achieve this aim we might need to study patients’ experiences and views of a particular disease process. This might optimize management outcomes by improving the clinician’s understanding of patients’ perceptions toward medical intervention. Qualitative studies can offer dental care providers with important aspects of the disease process. Particularly, how does the patient understand a particular oral health problem, and how does this knowledge improve oral health outcomes. This particular aspect cannot be explored using quantitative methods, which is widely used in the medical research field. This short guide tries to shed a light on the use of a qualitative research paradigm through qualitative research methodologies commonly used in medical research. These methodologies are; thematic analysis, phenomenology, interpretive phenomenology, grounded theory, and generic qualitative approaches. These methodologies will be presented in a rather different way from what a medical researcher might expect. This book will use fine art examples as an analogy. The author will use famous artworks to enlighten the main features of each qualitative research methodology.

The goal of medical care is to improve the patient’s quality of life (QOL) by maintaining function and well-being. Consequently, there is an increasing consensus that the patient’s perspective is pivotal in monitoring the outcomes of medical care in general. This is particularly important when management decisions are not clearcut through a lack of clinical evidence. In such cases, it is imperative to study the health problems from all possible aspects. To achieve this aim we might need to study patients’ experiences and views of a particular disease process. This might optimize management outcomes by improving the clinician’s understanding of patients’ perceptions toward medical intervention. Qualitative studies can offer dental care providers with important aspects of the disease process. Particularly, how does the patient understand a particular oral health problem, and how does this knowledge improve oral health outcomes. This particular aspect cannot be explored using quantitative methods, which is widely used in the medical research field. This short guide tries to shed a light on the use of a qualitative research paradigm through qualitative research methodologies commonly used in medical research. These methodologies are; thematic analysis, phenomenology, interpretive phenomenology, grounded theory, and generic qualitative approaches. These methodologies will be presented in a rather different way from what a medical researcher might expect. This book will use fine art examples as an analogy. The author will use famous artworks to enlighten the main features of each qualitative research methodology.

International Journal of Dental Hygiene

Virginia Dickson-Swift , Mark Gussy

Canadian Journal of …

Shafik Dharamsi

Trevor Male

This chapter aims to help you to: • Develop an understanding of how to analyse qualitative data – particularly text; • Recognise there is a strong link between data collection and data analysis; • Undertake content analysis and develop coding techniques for data handling; • Deal with a potential for bias that is commonly to be found in qualitative data handling; • Explore the benefits of computer assisted analysis and investigate some relevant software; • Present your data in a meaningful and helpful way; • Ensure your conclusions are justified by the evidence you have collected.

Ciência & Saúde Coletiva

Vera L P Alves

Qualitative Health research procedures that are not always applied, mainly in the analysis phase. Our objective is to present a systematized technique of step-by-step procedures for qualitative content analysis in the health field: Clinical-Qualitative Content Analysis. Our proposal consider that the qualitative research applied to the field of health, can acquire a perspective analogous to clinical practice and aims to interpret meanings expressed in reports through individual interviews or statements. This analysis takes part of the Clinical-Qualitative Method. The literature review was realized through: a book chapter, eight original articles and three methodological articles. The Clinical-qualitative Content Analysis technique comprises seven steps: 1) Editing material for analysis; 2) Floating reading; 3) Construction of the units of analysis; 4) Construction of codes of meaning; 5) General refining of the codes and the Construction of categories; 6) Discussion; 7) Validity. Th...

Barry Gibson

Douglas Ezzy

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