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Before constructing a qualitative study, you must first know your approach. Second, you must know how you intend to gather the research. These are called research methods. Notably, qualitative research methods vary drastically from quantitative research methods.

Qualitative Research Design Approaches

To perform qualitative research, you must choose at least one research design approach that fits your topic. It is not uncommon for a researcher to employ more than one approach throughout their study. Here are five common design approaches:

1. Historical Study

A historical study is the ideal choice for studies that involve extensive examination of the past - including people, events and documents. The purpose of a historical study is to draw conclusions about the present and future, based off research conducted in the past. ¹

This model depends on adequate interview sources and historical documents. It is essential to validate the accuracy of the data and find primary sources. Depending on the goal of the researcher, this form of study may result in a biography, which is why the term "historical study" is sometimes used interchangeably with "biographical study."

2. Phenomenology

Phenomenology is a wide-ranging form of study. In this research model, the researcher looks to gather information that explains how individuals experience a phenomenon and how they feel about it. This model recognizes that there is no single objective reality; instead, everyone experiences things differently. ¹

The outcome is described from the point of view of the participants. However, the researcher is still able to derive a set of findings that can be used to identify themes surrounding the phenomena under study.

3. Grounded Theory

The purpose of grounded theory is to develop a theory surrounding a social issue. This theory seeks not only to identify problems in social scenes, but also to define how people deal with those problems. Grounded theory is unique among qualitative design approaches, because it depends solely on the data gleaned through the research process. ². Often, the initial research question is progressively reformed and refined as more information is gathered on the topic. In this sense, the participants help shape the study.

4. Ethnography

Ethnography is the study of a specific grouping within a culture. Researchers pursuing this study format will immerse themselves into the culture they are researching. The qualitative data is gathered through direct observation of - and interaction with - participants who belong to that culture. The information is then presented through their perspective. Ultimately, this study aims at understanding group culture. ¹

5. Case Study

Case studies, one of the most common qualitative designs, are used to examine a person, group, community or institution. Researchers often use a bounded theory approach that confines the case study in terms of time or space. To conduct the case study, the researcher may draw upon multiple sources of data, such as observation, interviews and documents. All participants chosen must share a unifying factor, which means they all must have a direct or indirect connection to the research question or subject being studied. After collecting the data, the researcher will analyze it to identify common or prominent themes.

Qualitative Research Methods

Qualitative data can be collected using a variety of methods, and multiple methods may be used throughout the data collection phase of a single qualitative study. 2

Interview: Researchers can conduct in-depth, face-to-face interviews with participants. This allows them to gain insights from the participants to best understand their experience.

Focus Groups: Focus groups are similar to interviews, but involve multiple participants at once. They are another route to obtaining responses and making interview observations.

Observation: A less direct method than interviews or focus groups, this method requires careful attention to participants' activities and behaviors in order to gather data.

Document analysis: Researchers can gather useful data from print documents as well as electronic records. Careful analysis is needed to draw conclusions from the body of related documents.

Interested in starting your doctoral journey? Grand Canyon University has a wide variety of qualitative and quantitative programs and resources to help you. Click Request Info above to learn more about your first step

Retrieved from:

¹University of Missouri-St. Louis, Qualitative Research Designs in May 2021

²Research Rundowns, Qualitative Research Design in May 2021

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5 Qualitative Research Designs and Research Methods

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Before constructing a qualitative study, you must first know your approach. Second, you must know how you intend to gather the research.

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QUALITATIVE Research Design: Everything You Need To Know (With Examples)

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How to Choose a Qualitative Research Design

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Collecting and Analyzing Qualitative Data

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Interviews

Collecting data through interviews with participants is a characteristic of many qualitative studies. Interviews give the most direct and straightforward approach to gathering detailed and rich data regarding a particular phenomenon. The type of interview used to collect data can be tailored to the research question, the characteristics of participants and the preferred approach of the researcher. Interviews are most often carried out face-to-face, though the use of telephone interviews to overcome geographical barriers to participant recruitment is becoming more prevalent. 1

The key variation between interview types relates to the degree of structure. An open or unstructured interview will often be based on a single question, with the interviewer and interviewee then shaping the conversation in real time, rather than following a prewritten schedule. This can be particularly suited to methods in which participants are being encouraged to tell a story of their life or experiences, such as narrative enquiry. An example of the type of study in which these open, conversational interviews are well suited was an exploration of the impact of time on the work of Registered Nurses. 2

A common approach in qualitative research is the semistructured interview, where core elements of the phenomenon being studied are explicitly asked about by the interviewer. A well-designed semistructured interview should ensure data are captured in key areas while still allowing flexibility for participants to bring their own personality and perspective to the discussion. Finally, interviews can be much more rigidly structured to provide greater control for the researcher, essentially becoming questionnaires where responses are verbal rather than written.

Deciding where to place an interview design on this 'structural spectrum' will depend on the question to be answered and the skills of the researcher. A very structured approach is easy to administer and analyse but may not allow the participant to express themselves fully. At the other end of the spectrum, an open approach allows for freedom and flexibility, but requires the researcher to walk an investigative tightrope that maintains the focus of an interview without forcing participants into particular areas of discussion.

An example of a semistructured interview schedule for a study exploring surgeons' perceptions of postoperative pain 3 can be seen in box 1. An interview schedule such as this is normally developed prior to the start of the study following an extensive review of the literature and often also draws on the researcher's clinical experience. Before data collection commences, the interview schedule is often reviewed by a small group of subject experts to ensure all key issues have been included.

Interviews present several challenges to researchers. Most interviews are recorded and will need transcribing before analysing. This can be extremely time-consuming, with 1 hour of interview requiring 5-6 hours to transcribe. 4 The analysis itself is also time-consuming, requiring transcriptions to be pored over word-for-word and line-by-line. Interviews also present the problem of bias the researcher needs to take care to avoid leading questions or providing non-verbal signals that might influence the responses of participants.

Focus groups

The focus group is a method of data collection in which a moderator/facilitator (usually a coresearcher) speaks with a group of 6-12 participants about issues related to the research question. As an approach, the focus group offers qualitative researchers an efficient method of gathering the views of many participants at one time. Also, the fact that many people are discussing the same issue together can result in an enhanced level of debate, with the moderator often able to step back and let the focus group enter into a free-flowing discussion. 5 This provides an opportunity to gather rich data from a specific population about a particular area of interest, such as barriers perceived by student nurses when trying to communicate with patients with cancer. 6

From a participant perspective, the focus group may provide a more relaxing environment than a one-to-one interview; they will not need to be involved with every part of the discussion and may feel more comfortable expressing views when they are shared by others in the group. Focus groups also allow participants to 'bounce' ideas off each other which sometimes results in different perspectives emerging from the discussion. However, focus groups are not without their difficulties. As with interviews, focus groups provide a vast amount of data to be transcribed and analysed, with discussions often lasting 1-2 hours. Moderators also need to be highly skilled to ensure that the discussion can flow while remaining focused and that all participants are encouraged to speak, while ensuring that no individuals dominate the discussion. 7

Observation

Participant and non-participant observation are powerful tools for collecting qualitative data, as they give nurse researchers an opportunity to capture a wide array of information-such as verbal and non-verbal communication, actions (eg, techniques of providing care) and environmental factors-within a care setting. Another advantage of observation is that the researcher gains a first-hand picture of what actually happens in clinical practice. 8 If the researcher is adopting a qualitative approach to observation they will normally record field notes. Field notes can take many forms, such as a chronological log of what is happening in the setting, a description of what has been observed, a record of conversations with participants or an expanded account of impressions from the fieldwork. 9 10

As with other qualitative data collection techniques, observation provides an enormous amount of data to be captured and analysed-one approach to helping with collection and analysis is to digitally record observations to allow for repeated viewing. 11 Observation also provides the researcher with some unique methodological and ethical challenges. Methodologically, the act of being observed may change the behaviour of the participant (often referred to as the 'Hawthorne effect'), impacting on the value of findings. However, most researchers report a process of habitation taking place where, after a relatively short period of time, those being observed revert to their normal behaviour. Ethically, the researcher will need to consider when and how they should intervene if they view poor practice that could put patients at risk.

Conclusion

The three core approaches to data collection in qualitative research-interviews, focus groups and observation-provide researchers with rich and deep insights. All methods require skill on the part of the researcher, and all produce a large amount of raw data. However, with careful and systematic analysis 12 the data yielded with these methods will allow researchers to develop a detailed understanding of patient experiences and the work of nurses.

View Abstract

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Qualitative research methods allow us to better understand the experiences of patients and carers; they allow us to explore how decisions are made and provide us with a detailed insight into how interventions may alter care. To develop such insights, qualitative research requires data which are holistic, rich and nuanced, allowing themes and findings to emerge through careful analysis.

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When we need to understand and report on people's opinions-such as customer feedback-we turn to qualitative data. Unlike numbers, qualitative data provides deeper insights into emotions, motivations, and experiences. However, analyzing this unstructured information is challenging.

While tools like Excel, Tableau, and Power BI handle numerical data effortlessly, there are fewer tools for analyzing qualitative data. Even with Generative AI, much of the qualitative data analysis still takes place manually. The good news? AI-powered tools are making the process faster and easier.

In this guide, we'll walk you through the five key steps of qualitative data analysis, breaking down both manual and automated approaches:

Here's a quick summary:

‍
Now let's explore these steps in detail!

What Makes Qualitative Data Different?

Qualitative data is typically generated through:

Compared to quantitative data, qualitative data is unstructured and provides more depth. It helps answer questions, formulate hypotheses, and build understanding.

However, analyzing qualitative data is difficult. While businesses can use feedback analytics platforms to process qualitative customer data, many still rely on manual methods. More businesses are shifting toward fully automated analysis because it's cheaper, faster, and just as accurate.

Depending on data privacy rules in relation to Gen AI, some businesses use Microsoft Co-Pilot or ChatGPT, while others opt for advanced AI-powered research tools. These solutions help automate qualitative data analysis, making insights more accessible and actionable.

So, how do we actually analyze qualitative data? Let's break down the process step by step. But first, what is qualitative data analysis?

What is Qualitative Data Analysis?

Qualitative data analysis is the process of gathering, organizing, and interpreting non-numerical data to uncover patterns, themes, and insights. It helps businesses and researchers make sense of open-ended responses, interviews, and other unstructured data sources.

In customer feedback analysis, qualitative data analysis is used to extract meaningful insights from reviews, complaints, chat messages, support interactions, and social media comments-helping businesses understand customer sentiment and improve decision-making.

Tools like Thematic automate this process for customer feedback at scale.

How is qualitative data analysis different from quantitative data analysis?

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.

"Not everything everything that can be counted, counts, and not everything that counts can be counted" - William Bruce Cameron (1963)

This quote, often mistakenly attributed to Einstein, clearly states that while numerical data is valuable, not everything meaningful can be measured. Qualitative data analysis captures the depth of human experiences, emotions, and challenges that statistics alone cannot fully explain.

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 quantitative and qualitative data analysis are important, working together to decode data for different objectives.

Dimension

Qualitative data analysis

Quantitative data analysis

Data type
Text, audio, video, images (rich, unstructured data)

Numbers, metrics, statistics (structured data)

Sources
Interviews, open-ended surveys, reviews, support tickets, social media

Ratings, scores, CSAT, NPS numbers, transaction counts

Analysis time
Days to weeks manually; hours with AI automation

Minutes to hours with spreadsheets and BI tools

Best for
Understanding the why in text

Measuring what's happening and how much

Tools used
QDA softwares; AI QDA like Thematic, or Atlas.ti, NVivo, MAXQDA

Excel, Tableau, Power BI, SPSS

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 a qualitative research method used to examine written and spoken language in relation to its social context. It goes beyond analyzing words and sentences, focusing on how language shapes meaning, social structures, and power dynamics. This method helps researchers understand how people construct reality through communication, revealing the deeper assumptions, values, and influences embedded in language.

Discourse analysis is widely used in various fields, from social research to brand strategy, to uncover how language influences perception, identity, and decision-making​.

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.

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

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 channels, companies are gathering even more 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.

Most commonly, qualitative data stored in third-party solutions. Some businesses pull all data into a central database, such as Snowflake, Amazon Redshift, BigQuery or Databricks. You can export this data manually for a one-off project, but if you need to conduct the analysis more regularly, try to find an automated solution. For example, Voice of Customer or feedback analysis solutions often provide integrations into third-party tools and databases. Alternatively, APIs can be used to gather feedback.

Thematic connects directly to feedback sources like Zendesk, Intercom, Qualtrics, App Store reviews, and more. This helps you pull data automatically so you can start analysis without manual exports.

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, and review analysis is a great place to start. 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 scrape the reviews with a third-party tool.

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, researchers have been using CAQDAS software such as ATLAS.ti, NVivo and MAXQDA. Another popular option was IBM SPSS, which handled both quant and qual data.

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

Assists in the organizing of your data

Help view different interpretations of the data

Allows you to share your data with others for collaboration

Most of these solutions now offer some degree of AI assistance. The main thing to look out for is the ease of use and the ability to bring in your input into AI analysis.

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 ( conversational analytics), 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, you will benefit from a feedback analytics platform. A feedback analytics platform is a software that automates the process of sentiment and thematic analysis, as well as the reporting of the results to the business. Typically, it's managed by a central Voice of Customer or research team to ensure consistent analysis methodology. But others in the company can login to get quick answers or reviews.

These platforms can directly tap into qualitative data sources (review sites, social media, survey responses, etc) through one-click integrations or custom connectors. 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 this data to extract meaningful insights.

Coding is the process of labelling and organizing your data by theme, i.e. to perform thematic analysis on this data. The main goal of coding is to find trends in the data and relationships between the themes.

When coding manually, start by taking 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 use a tool like ChatGPT, you can automate the process of coming up with codes. But if your entire dataset does not fit into a context window, you'll need to manually batch analyze the remainder of the data, adjusting the prompts as you go. Make sure to read our guide on how to analyze feedback using ChatGPT.

If you choose to use a feedback analytics platform, much of this process will be automated 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. In a sentence such as "I really hate the customer service of this computer software company", the phrase "hate the customer service" 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.

Most of these solutions have now introduced AI-assistance. But they weren't build with the idea of automated coding from the ground up, like thematic analysis software described in next section.

Automating the qualitative coding process using thematic analysis software

Advances in AI have now made it possible to read, code and structure qualitative data automatically. This type of automated coding is offered by thematic analysis software, designed specifically for this task.

Learn more: How to use Thematic Analysis AI to theme qualitative data.

Automation makes it far simpler and faster to code the feedback and group it into themes. The AI can be used in various ways:

And much more!

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 is a qualitative data analysis software that automates the coding of qualitative feedback with no training or pre-configuring required. Unlike black-box AI tools, Thematic combines AI-powered automation with human-in-the-loop control. Researchers can edit, refine, and validate themes using the Themes Editor, making analysis transparent and defensible for executive reporting

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

Thematic finds codes and sentiment within text automatically

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.

For enterprise research and insights teams drowning in qualitative feedback, Thematic delivers research-grade analysis that's fast enough for agile decisions and rigorous enough for executive reporting. The result is decision-ready outputs that teams can confidently present to the C-suite.

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:

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!

Visualizing coded qualitative data

The most common way of visualizing coded data is by frequency. Here's an example of how we do it in Thematic, which can be replicated in PowerBI, Tableau or Looker.

But frequency is not always a good gauge of importance. For example, if some people are happy with "deposit checks" feature and others unhappy, what's the overall importance of this theme in feedback? Should we prioritize working on it? This is where a driver analysis, aka impact, becomes important.

Impact

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 NPS in the subset of responses that do not contain that theme

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

You can then visualize this data using a bar chart. It will tell you which themes are dragging the score up or down, and you can even view this over time. If this sounds interesting, check out the demo videos showing how we do it in Thematic.

You can also 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, there are 3 main reasons why it's important:

1) To bring in the business nuance that AI cannot learn from the data itself. For example, are there specific teams responsible for acting on feedback? It's worth organizing themes so that each team can easily see what they can impact.

2) To iron out any errors in the analysis. Even the best AI will still be wrong occasionally.

3) To build trust in the analysis. In Thematic, we show why AI has chosen each theme, so that you can verify its approach.

The result is a higher accuracy of analysis. This is sometimes referred to as augmented intelligence or human in the loop.

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 in 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.

The Top Qualitative Data Analysis Tools: AI + CAQDAS

The right tool depends on whether you're doing academic research, analyzing customer feedback at scale, or somewhere in between. Traditional Computer-Assisted Qualitative Data Analysis Software (CAQDAS) gives researchers hands-on control over manual coding. AI-powered platforms handle volume and speed that manual tools can't match. Here's how the leading options compare:

The Top Qualitative Data Analysis Tools: AI + CAQDAS

The right tool depends on whether you're doing academic research, analyzing customer feedback at scale, or somewhere in between. Traditional Computer-Assisted Qualitative Data Analysis Software (CAQDAS) gives researchers hands-on control over manual coding. AI-powered platforms handle volume and speed that manual tools can't match. Here's how the leading options compare:

Each of these tools has a distinct sweet spot. If you're an enterprise team dealing with thousands of feedback responses per month, the calculus looks very different from an academic researcher working through 30 interview transcripts. For a deeper look at how these platforms stack up - including pricing, setup time, and which use cases each handles best - see our full guide to qualitative data analysis software.

Each of these tools has a distinct sweet spot. If you're an enterprise team dealing with thousands of feedback responses per month, the calculus looks very different from an academic researcher working through 30 interview transcripts.

For a deeper look at how these platforms stack up - including pricing, setup time, and which use cases each handles best - see our full guide to qualitative data analysis software.

Challenges of Qualitative Data Analysis

While qualitative data analysis offers rich insights, it comes with 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.

Thematic addresses these challenges by automating the coding process with transparent AI. Researchers save time, reduce bias through consistent analysis, and maintain control via the Themes Editor - making qualitative data analysis faster and more reliable at scale.

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.

Case Study: How DoorDash Used Qualitative Data Analysis to Improve Dasher Experience

DoorDash, a leading food delivery platform, leveraged qualitative data analysis to better understand its delivery drivers, known as Dashers. By analyzing thousands of feedback points, DoorDash identified key concerns, such as work flexibility and app usability. Using Thematic's AI-driven insights, the company prioritized improvements, including a new reward system for top Dashers and app adjustments to enhance the delivery experience. These changes led to higher driver satisfaction and improved retention, demonstrating how qualitative data analysis can drive impactful business decisions.

Conclusion: Seek automation with human oversight

AI technology is here to stay, and it's powerful enough to automate most of qualitative data analysis. So, as a researcher, you need to learn not just the basics of how to do this task manually, but also how to harness AI to complete this task quicker.

For projects that involve small datasets or one-offs, use ChatGPT or a similar solution. For example, if the objective is simply to quantify a simple question like "Do customers prefer X concepts to Y?". And 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. Especially, once you have huge volumes of data and you need a deeper understanding of the data. For example, the 'why' behind customers' preference for X or Y. Being able to do this fast to help your business move quickly is critical.

The ability to collect a free flow of qualitative feedback data and customer metrics 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.

Or you may interested in some of our free guide: How To Leverage AI To Improve CX.For you insights & CX professionals reading.

Frequently Asked Questions (FAQs)

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

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Qualitative data analysis is a process of structuring & interpreting data to understand what it represents. Learn the qualitative analysis process in 5 steps.

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For direct assistance

Contact us, JHU Data Services for assistance with access to nVivo and ATLAS.ti at the Data Services offices on A level, JHU Eisenhower Library.

Visit our website for more info and our upcoming training workshops!

Data Management Consultant

Qualitative Data Analysis Software (QDAS) overview

Qualitative research has benefited from a range of software tools facilitating most qualitative methodological techniques, particularly those involving multimedia digital data. These guides focus on two major QDAS products, nVivo and ATLAS.ti. Both programs can be found on the workstations at the Data Services computer lab on A-level, Eisenhower Library, and nVivo is available through JHU's SAFE Desktop. This guide also lists other QDA software and linked resources.

Many university libraries have produced comprehensive guides on nVivo, ATLAS.ti, and other QDA software, to which we will provide links with our gratitude

Schmider, Christian. n.d. What Qualitative Data Analysis Software Can and Can't Do for You - an Intro Video. MERIT Library at the School of Education: School of Education, University of Wisconsin-Madison. Accessed January 7, 2020. https://www.youtube.com/watch?v=tLKfaCiHVic.

Choosing QDA Software

Qualitative Data Analysis (QDA) Software supports a variety of qualitative techniques and methodologies

Qualitative techniques supported by QDAS

Coding and Classifying

Writing: analysis, description, memos

Relating: finding and annotating connections, relationships, patterns

Audio/Visual analysis: marking, clipping, transcribing, annotating

Text mining: computer-aided discovery in large amounts of unstructured text

Visualization: diagramming, relationship and network patterns, quantitative summary

QDAS supported methodologies

Ethnography

Case studies

Grounded theory/ phenomenology

Discourse/narrative analysis

Sociolinguistic analysis

Collaborative qualitative research

Text analysis & text mining

Overview of qualitative methods from ATLAS.ti: https://atlasti.com/qualitative-research-methods/

Decision factors for your research

Methods to feature facilitation (in disciplinary context): How many features directly support your methodology?

Interface for collection, analysis, reports: Do features accommodate most phases of your research workflow?

Visualization and outputs: Does it produce and successfully export needed visualization without extensive modification?

Cost and access to software: Is it worth the investment cost as well as in learning to use it? Look for education discounts.

Core QDAS functions

Basic functions common to most QDA programs, and to NVivo and ATLAS.ti in particular:

Coding
Application of a maintained set of terms and short phrases linked to segments of text or audio/video that can be queried and gathered for comparative analysis.

​ Annotation
Longer narrative notes attached to text or a/v segments, or to codes

Navigation / queries
Quick access to codes and segments that can be brought together in panel views for comparison, advanced Boolean search options, and flexible interlinking of segments, codes, and annotation

Transcription
Most QDAS facilitates transcribing audio and video, ideally maintaining the links between transcript and A/V segments.

Relationships/patterns
Gathering codes, segments, and annotations facilitates pattern discovery and further description of relationships. Some QDAS support social network analysis techniques and visualization

Reporting / collating
A range of reports using queries and filters to assemble data and annotations facilitates analysis and writing results.

Visualization
​Typically includes code tables, social network graphs, and annotated A/V clips.

Collaboration
Shared access to data & analysis, facilitating comments and discussion, and tracking contributor actions and changes.

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Guides: Qualitative Data Analysis Software (nVivo, Atlas.TI, and more): Qualitative Data Analysis Software (QDAS) overview

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Overview and resource links for Qualitative Data Analysis Software (QDAS), uses and how to choose, focusing on QDAS supported by JHU Data Services: nVivo and Atlas.TI, with resource and training links to these and other QDA Software

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Qualitative Data Analysis 101 Tutorial: 6 Analysis Methods + Examples

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Qualitative Data Analysis for Everyone (Module 1)

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Limitations and Delimitations in Research

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Diving Deeper into Limitations and Delimitations – PhDStudent

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If you are working on a thesis, dissertation, or other formal research project, chances are your advisor or committee will ask you to address the delimitations of your study. When faced with this request, many students respond with a puzzled look and then go on to address what are actually the study’s limitations.

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A Review of the Quality Indicators of Rigor in Qualitative Research

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Attributes of rigor and quality and suggested best practices for qualitative research design as they relate to the steps of designing, conducting, and reporting qualitative research in health professions educational scholarship are presented. A…

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Appraising Qualitative Research: Rigor and Trustworthiness

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Imagine you are reading the abstract of a study, and the researcher claims something like “our interviews showed that games can be great for teaching collaboration skills.”