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Qualitative Data Analysis: What is it, Methods + Examples

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

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

What is Qualitative Data Analysis?

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

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

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

Importance of Qualitative Data Analysis

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

In-Depth Understanding

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

Contextual Insight

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

Theory Development

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

Participant Perspectives

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

Exploratory Research

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

Types of Qualitative Data

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

Interviews and Focus Groups

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

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

Observations and Field Notes

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

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

Textual and Visual Data

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

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

When to Choose Qualitative Data Analysis over Quantitative Data Analysis

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

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

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

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

Qualitative Data Analysis Methods and Examples

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

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

Method 1: Content Analysis

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

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

Steps to Do Content Analysis

Follow these steps when conducting content analysis:

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

Benefits & Challenges

There are various advantages to using content analysis:

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

However, keep in mind the challenges that arise:

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

Example of Content Analysis

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

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

Method 2: Thematic Analysis

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

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

Steps to Do Thematic Analysis

Follow these steps when conducting a thematic analysis:

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

Thematic analysis has various benefits:

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

However, challenges may arise, such as:

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

Example of Thematic Analysis

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

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

Method 3: Narrative Analysis

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

Steps to Do Narrative Analysis

The following steps are involved in narrative analysis:

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

There are various advantages to narrative analysis:

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

However, difficulties may arise, such as:

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

Example of Narrative Analysis

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

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

Method 4: Grounded Theory Analysis

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

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

Steps to Do Grounded Theory Analysis

Grounded theory analysis requires the following steps:

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

Grounded theory analysis has various benefits:

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

However, challenges might arise with:

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

Example of Grounded Theory Analysis

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

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

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

Method 5: Discourse Analysis

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

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

Steps to Do Discourse Analysis

Conducting discourse analysis entails the following steps:

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

There are various advantages of using discourse analysis:

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

However, the following challenges may arise:

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

Example of Discourse Analysis

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

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

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

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

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

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

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Qualitative Data Analysis Methods 101:

The “big 6” methods + examples.

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

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

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

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

What (exactly) is qualitative data analysis?

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

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

So, if it’s not numbers, what is it?

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

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

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

So, qualitative analysis is easier than quantitative, right?

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

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

Need a helping hand?

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

Without further delay, let’s get into it.

The “Big 6” Qualitative Analysis Methods 

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

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

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

Let’s take a look at each of them…

QDA Method #1: Qualitative Content Analysis

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

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

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

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

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

QDA Method #2: Narrative Analysis 

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

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

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

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

QDA Method #3: Discourse Analysis 

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

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

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

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

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

QDA Method #4: Thematic Analysis

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

Let’s take a look at an example.

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

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

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

QDA Method #5: Grounded theory (GT) 

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

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

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

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

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

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

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

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

QDA Method #6:   Interpretive Phenomenological Analysis (IPA)

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

Let’s just stick with IPA, okay?

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

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

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

How to choose the right analysis method

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

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

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

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

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

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

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

Let’s recap on QDA methods…

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

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

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

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

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86 Comments

This has been very helpful. Thank you.

Thank you madam,

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

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Thanks madam . It is very important .

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Great presentation

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

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very informative sequential presentation

Precise explanation of method.

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

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

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

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

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

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

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

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

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i need a citation of your book.

Thanks a lot , remarkable indeed, enlighting to the best

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

Keep writing useful artikel.

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

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Very helpful .Thanks.

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

The session was very helpful and insightful. Thank you

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

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

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

It is a very nice work

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

This is Amazing and well explained, thanks

great overview

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

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

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

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

Very helpful indeed. Thanku so much for the insight.

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very educative

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

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

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

I learnt a lot. Thank you

Relevant and Informative, thanks !

Well-planned and organized, thanks much! 🙂

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

Clear explanation on qualitative and how about Case study

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Wow, Thanks for making my life easy

This was helpful thanks .

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

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

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

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

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

very informative content, thank you.

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

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

Beautifully explained.

Thanks a lot

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

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

content analysis can be qualitative research?

THANK YOU VERY MUCH.

Thank you very much for such a wonderful content

do you have any material on Data collection

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

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

very helpful, thank you so much

The tutorial is useful. I benefited a lot.

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• Volume 17, Issue 1
• Qualitative data analysis: a practical example
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• Helen Noble 1 ,
• Joanna Smith 2
• 1 School of Nursing and Midwifery, Queens's University Belfast , Belfast , UK
• 2 Department of Health Sciences , University of Huddersfield , Huddersfield , UK
• Correspondence to : Dr Helen Noble School of Nursing and Midwifery, Queen's University Belfast, Medical Biology Centre, 97 Lisburn Road, Belfast BT9 7BL, UK; helen.noble{at}qub.ac.uk

https://doi.org/10.1136/eb-2013-101603

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The aim of this paper is to equip readers with an understanding of the principles of qualitative data analysis and offer a practical example of how analysis might be undertaken in an interview-based study.

What is qualitative data analysis?

What are the approaches in undertaking qualitative data analysis.

Although qualitative data analysis is inductive and focuses on meaning, approaches in analysing data are diverse with different purposes and ontological (concerned with the nature of being) and epistemological (knowledge and understanding) underpinnings. 2 Identifying an appropriate approach in analysing qualitative data analysis to meet the aim of a study can be challenging. One way to understand qualitative data analysis is to consider the processes involved. 3 Approaches can be divided into four broad groups: quasistatistical approaches such as content analysis; the use of frameworks or matrices such as a framework approach and thematic analysis; interpretative approaches that include interpretative phenomenological analysis and grounded theory; and sociolinguistic approaches such as discourse analysis and conversation analysis. However, there are commonalities across approaches. Data analysis is an interactive process, where data are systematically searched and analysed in order to provide an illuminating description of phenomena; for example, the experience of carers supporting dying patients with renal disease 4 or student nurses’ experiences following assignment referral. 5 Data analysis is an iterative or recurring process, essential to the creativity of the analysis, development of ideas, clarifying meaning and the reworking of concepts as new insights ‘emerge’ or are identified in the data.

Do you need data software packages when analysing qualitative data?

Qualitative data software packages are not a prerequisite for undertaking qualitative analysis but a range of programmes are available that can assist the qualitative researcher. Software programmes vary in design and application but can be divided into text retrievers, code and retrieve packages and theory builders. 6 NVivo and NUD*IST are widely used because they have sophisticated code and retrieve functions and modelling capabilities, which speed up the process of managing large data sets and data retrieval. Repetitions within data can be quantified and memos and hyperlinks attached to data. Analytical processes can be mapped and tracked and linkages across data visualised leading to theory development. 6 Disadvantages of using qualitative data software packages include the complexity of the software and some programmes are not compatible with standard text format. Extensive coding and categorising can result in data becoming unmanageable and researchers may find visualising data on screen inhibits conceptualisation of the data.

How do you begin analysing qualitative data?

Despite the diversity of qualitative methods, the subsequent analysis is based on a common set of principles and for interview data includes: transcribing the interviews; immersing oneself within the data to gain detailed insights into the phenomena being explored; developing a data coding system; and linking codes or units of data to form overarching themes/concepts, which may lead to the development of theory. 2 Identifying recurring and significant themes, whereby data are methodically searched to identify patterns in order to provide an illuminating description of a phenomenon, is a central skill in undertaking qualitative data analysis. Table 1 contains an extract of data taken from a research study which included interviews with carers of people with end-stage renal disease managed without dialysis. The extract is taken from a carer who is trying to understand why her mother was not offered dialysis. The first stage of data analysis involves the process of initial coding, whereby each line of the data is considered to identify keywords or phrases; these are sometimes known as in vivo codes (highlighted) because they retain participants’ words.

• View inline

Data extract containing units of data and line-by-line coding

When transcripts have been broken down into manageable sections, the researcher sorts and sifts them, searching for types, classes, sequences, processes, patterns or wholes. The next stage of data analysis involves bringing similar categories together into broader themes. Table 2 provides an example of the early development of codes and categories and how these link to form broad initial themes.

Development of initial themes from descriptive codes

Table 3 presents an example of further category development leading to final themes which link to an overarching concept.

Development of final themes and overarching concept

How do qualitative researchers ensure data analysis procedures are transparent and robust?

In congruence with quantitative researchers, ensuring qualitative studies are methodologically robust is essential. Qualitative researchers need to be explicit in describing how and why they undertook the research. However, qualitative research is criticised for lacking transparency in relation to the analytical processes employed, which hinders the ability of the reader to critically appraise study findings. 7 In the three tables presented the progress from units of data to coding to theme development is illustrated. ‘Not involved in treatment decisions’ appears in each table and informs one of the final themes. Documenting the movement from units of data to final themes allows for transparency of data analysis. Although other researchers may interpret the data differently, appreciating and understanding how the themes were developed is an essential part of demonstrating the robustness of the findings. Qualitative researchers must demonstrate rigour, associated with openness, relevance to practice and congruence of the methodological approch. 2 In summary qualitative research is complex in that it produces large amounts of data and analysis is time consuming and complex. High-quality data analysis requires a researcher with expertise, vision and veracity.

• Cheater F ,
• Robshaw M ,
• McLafferty E ,
• Maggs-Rapport F

Competing interests None.

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Qualitative case study data analysis: an example from practice

Affiliation.

• 1 School of Nursing and Midwifery, National University of Ireland, Galway, Republic of Ireland.
• PMID: 25976531
• DOI: 10.7748/nr.22.5.8.e1307

Aim: To illustrate an approach to data analysis in qualitative case study methodology.

Background: There is often little detail in case study research about how data were analysed. However, it is important that comprehensive analysis procedures are used because there are often large sets of data from multiple sources of evidence. Furthermore, the ability to describe in detail how the analysis was conducted ensures rigour in reporting qualitative research.

Data sources: The research example used is a multiple case study that explored the role of the clinical skills laboratory in preparing students for the real world of practice. Data analysis was conducted using a framework guided by the four stages of analysis outlined by Morse ( 1994 ): comprehending, synthesising, theorising and recontextualising. The specific strategies for analysis in these stages centred on the work of Miles and Huberman ( 1994 ), which has been successfully used in case study research. The data were managed using NVivo software.

Review methods: Literature examining qualitative data analysis was reviewed and strategies illustrated by the case study example provided. Discussion Each stage of the analysis framework is described with illustration from the research example for the purpose of highlighting the benefits of a systematic approach to handling large data sets from multiple sources.

Conclusion: By providing an example of how each stage of the analysis was conducted, it is hoped that researchers will be able to consider the benefits of such an approach to their own case study analysis.

Implications for research/practice: This paper illustrates specific strategies that can be employed when conducting data analysis in case study research and other qualitative research designs.

Keywords: Case study data analysis; case study research methodology; clinical skills research; qualitative case study methodology; qualitative data analysis; qualitative research.

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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, 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 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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• What Is Qualitative Research? | Methods & Examples

What Is Qualitative Research? | Methods & Examples

Published on June 19, 2020 by Pritha Bhandari . Revised on June 22, 2023.

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

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

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

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

Table of contents

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

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

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

Note that qualitative research is at risk for certain research biases including the Hawthorne effect , observer bias , recall bias , and social desirability bias . While not always totally avoidable, awareness of potential biases as you collect and analyze your data can prevent them from impacting your work too much.

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

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

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

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

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

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

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

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

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

• Flexibility

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

• Natural settings

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

• Meaningful insights

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

• Generation of new ideas

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

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

• Unreliability

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

• Subjectivity

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

• Limited generalizability

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

• Labor-intensive

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

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Chi square goodness of fit test
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Inclusion and exclusion criteria

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

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

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

There are five common approaches to qualitative research :

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

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

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

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

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

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• Am J Pharm Educ
• v.74(8); 2010 Oct 11

Presenting and Evaluating Qualitative Research

The purpose of this paper is to help authors to think about ways to present qualitative research papers in the American Journal of Pharmaceutical Education . It also discusses methods for reviewers to assess the rigour, quality, and usefulness of qualitative research. Examples of different ways to present data from interviews, observations, and focus groups are included. The paper concludes with guidance for publishing qualitative research and a checklist for authors and reviewers.

INTRODUCTION

Policy and practice decisions, including those in education, increasingly are informed by findings from qualitative as well as quantitative research. Qualitative research is useful to policymakers because it often describes the settings in which policies will be implemented. Qualitative research is also useful to both pharmacy practitioners and pharmacy academics who are involved in researching educational issues in both universities and practice and in developing teaching and learning.

Qualitative research involves the collection, analysis, and interpretation of data that are not easily reduced to numbers. These data relate to the social world and the concepts and behaviors of people within it. Qualitative research can be found in all social sciences and in the applied fields that derive from them, for example, research in health services, nursing, and pharmacy. 1 It looks at X in terms of how X varies in different circumstances rather than how big is X or how many Xs are there? 2 Textbooks often subdivide research into qualitative and quantitative approaches, furthering the common assumption that there are fundamental differences between the 2 approaches. With pharmacy educators who have been trained in the natural and clinical sciences, there is often a tendency to embrace quantitative research, perhaps due to familiarity. A growing consensus is emerging that sees both qualitative and quantitative approaches as useful to answering research questions and understanding the world. Increasingly mixed methods research is being carried out where the researcher explicitly combines the quantitative and qualitative aspects of the study. 3 , 4

Like healthcare, education involves complex human interactions that can rarely be studied or explained in simple terms. Complex educational situations demand complex understanding; thus, the scope of educational research can be extended by the use of qualitative methods. Qualitative research can sometimes provide a better understanding of the nature of educational problems and thus add to insights into teaching and learning in a number of contexts. For example, at the University of Nottingham, we conducted in-depth interviews with pharmacists to determine their perceptions of continuing professional development and who had influenced their learning. We also have used a case study approach using observation of practice and in-depth interviews to explore physiotherapists' views of influences on their leaning in practice. We have conducted in-depth interviews with a variety of stakeholders in Malawi, Africa, to explore the issues surrounding pharmacy academic capacity building. A colleague has interviewed and conducted focus groups with students to explore cultural issues as part of a joint Nottingham-Malaysia pharmacy degree program. Another colleague has interviewed pharmacists and patients regarding their expectations before and after clinic appointments and then observed pharmacist-patient communication in clinics and assessed it using the Calgary Cambridge model in order to develop recommendations for communication skills training. 5 We have also performed documentary analysis on curriculum data to compare pharmacist and nurse supplementary prescribing courses in the United Kingdom.

It is important to choose the most appropriate methods for what is being investigated. Qualitative research is not appropriate to answer every research question and researchers need to think carefully about their objectives. Do they wish to study a particular phenomenon in depth (eg, students' perceptions of studying in a different culture)? Or are they more interested in making standardized comparisons and accounting for variance (eg, examining differences in examination grades after changing the way the content of a module is taught). Clearly a quantitative approach would be more appropriate in the last example. As with any research project, a clear research objective has to be identified to know which methods should be applied.

Types of qualitative data include:

• Audio recordings and transcripts from in-depth or semi-structured interviews
• Structured interview questionnaires containing substantial open comments including a substantial number of responses to open comment items.
• Audio recordings and transcripts from focus group sessions.
• Field notes (notes taken by the researcher while in the field [setting] being studied)
• Video recordings (eg, lecture delivery, class assignments, laboratory performance)
• Case study notes
• Documents (reports, meeting minutes, e-mails)
• Diaries, video diaries
• Observation notes
• Press clippings
• Photographs

RIGOUR IN QUALITATIVE RESEARCH

Qualitative research is often criticized as biased, small scale, anecdotal, and/or lacking rigor; however, when it is carried out properly it is unbiased, in depth, valid, reliable, credible and rigorous. In qualitative research, there needs to be a way of assessing the “extent to which claims are supported by convincing evidence.” 1 Although the terms reliability and validity traditionally have been associated with quantitative research, increasingly they are being seen as important concepts in qualitative research as well. Examining the data for reliability and validity assesses both the objectivity and credibility of the research. Validity relates to the honesty and genuineness of the research data, while reliability relates to the reproducibility and stability of the data.

The validity of research findings refers to the extent to which the findings are an accurate representation of the phenomena they are intended to represent. The reliability of a study refers to the reproducibility of the findings. Validity can be substantiated by a number of techniques including triangulation use of contradictory evidence, respondent validation, and constant comparison. Triangulation is using 2 or more methods to study the same phenomenon. Contradictory evidence, often known as deviant cases, must be sought out, examined, and accounted for in the analysis to ensure that researcher bias does not interfere with or alter their perception of the data and any insights offered. Respondent validation, which is allowing participants to read through the data and analyses and provide feedback on the researchers' interpretations of their responses, provides researchers with a method of checking for inconsistencies, challenges the researchers' assumptions, and provides them with an opportunity to re-analyze their data. The use of constant comparison means that one piece of data (for example, an interview) is compared with previous data and not considered on its own, enabling researchers to treat the data as a whole rather than fragmenting it. Constant comparison also enables the researcher to identify emerging/unanticipated themes within the research project.

STRENGTHS AND LIMITATIONS OF QUALITATIVE RESEARCH

Qualitative researchers have been criticized for overusing interviews and focus groups at the expense of other methods such as ethnography, observation, documentary analysis, case studies, and conversational analysis. Qualitative research has numerous strengths when properly conducted.

Strengths of Qualitative Research

• Issues can be examined in detail and in depth.
• Interviews are not restricted to specific questions and can be guided/redirected by the researcher in real time.
• The research framework and direction can be quickly revised as new information emerges.
• The data based on human experience that is obtained is powerful and sometimes more compelling than quantitative data.
• Subtleties and complexities about the research subjects and/or topic are discovered that are often missed by more positivistic enquiries.
• Data usually are collected from a few cases or individuals so findings cannot be generalized to a larger population. Findings can however be transferable to another setting.

Limitations of Qualitative Research

• Research quality is heavily dependent on the individual skills of the researcher and more easily influenced by the researcher's personal biases and idiosyncrasies.
• Rigor is more difficult to maintain, assess, and demonstrate.
• The volume of data makes analysis and interpretation time consuming.
• It is sometimes not as well understood and accepted as quantitative research within the scientific community
• The researcher's presence during data gathering, which is often unavoidable in qualitative research, can affect the subjects' responses.
• Issues of anonymity and confidentiality can present problems when presenting findings
• Findings can be more difficult and time consuming to characterize in a visual way.

PRESENTATION OF QUALITATIVE RESEARCH FINDINGS

The following extracts are examples of how qualitative data might be presented:

Data From an Interview.

The following is an example of how to present and discuss a quote from an interview.

The researcher should select quotes that are poignant and/or most representative of the research findings. Including large portions of an interview in a research paper is not necessary and often tedious for the reader. The setting and speakers should be established in the text at the end of the quote.

The student describes how he had used deep learning in a dispensing module. He was able to draw on learning from a previous module, “I found that while using the e learning programme I was able to apply the knowledge and skills that I had gained in last year's diseases and goals of treatment module.” (interviewee 22, male)

This is an excerpt from an article on curriculum reform that used interviews 5 :

The first question was, “Without the accreditation mandate, how much of this curriculum reform would have been attempted?” According to respondents, accreditation played a significant role in prompting the broad-based curricular change, and their comments revealed a nuanced view. Most indicated that the change would likely have occurred even without the mandate from the accreditation process: “It reflects where the profession wants to be … training a professional who wants to take on more responsibility.” However, they also commented that “if it were not mandated, it could have been a very difficult road.” Or it “would have happened, but much later.” The change would more likely have been incremental, “evolutionary,” or far more limited in its scope. “Accreditation tipped the balance” was the way one person phrased it. “Nobody got serious until the accrediting body said it would no longer accredit programs that did not change.”

Data From Observations

The following example is some data taken from observation of pharmacist patient consultations using the Calgary Cambridge guide. 6 , 7 The data are first presented and a discussion follows:

Pharmacist: We will soon be starting a stop smoking clinic. Patient: Is the interview over now? Pharmacist: No this is part of it. (Laughs) You can't tell me to bog off (sic) yet. (pause) We will be starting a stop smoking service here, Patient: Yes. Pharmacist: with one-to-one and we will be able to help you or try to help you. If you want it. In this example, the pharmacist has picked up from the patient's reaction to the stop smoking clinic that she is not receptive to advice about giving up smoking at this time; in fact she would rather end the consultation. The pharmacist draws on his prior relationship with the patient and makes use of a joke to lighten the tone. He feels his message is important enough to persevere but he presents the information in a succinct and non-pressurised way. His final comment of “If you want it” is important as this makes it clear that he is not putting any pressure on the patient to take up this offer. This extract shows that some patient cues were picked up, and appropriately dealt with, but this was not the case in all examples.

Data From Focus Groups

This excerpt from a study involving 11 focus groups illustrates how findings are presented using representative quotes from focus group participants. 8

Those pharmacists who were initially familiar with CPD endorsed the model for their peers, and suggested it had made a meaningful difference in the way they viewed their own practice. In virtually all focus groups sessions, pharmacists familiar with and supportive of the CPD paradigm had worked in collaborative practice environments such as hospital pharmacy practice. For these pharmacists, the major advantage of CPD was the linking of workplace learning with continuous education. One pharmacist stated, “It's amazing how much I have to learn every day, when I work as a pharmacist. With [the learning portfolio] it helps to show how much learning we all do, every day. It's kind of satisfying to look it over and see how much you accomplish.” Within many of the learning portfolio-sharing sessions, debates emerged regarding the true value of traditional continuing education and its outcome in changing an individual's practice. While participants appreciated the opportunity for social and professional networking inherent in some forms of traditional CE, most eventually conceded that the academic value of most CE programming was limited by the lack of a systematic process for following-up and implementing new learning in the workplace. “Well it's nice to go to these [continuing education] events, but really, I don't know how useful they are. You go, you sit, you listen, but then, well I at least forget.”

The following is an extract from a focus group (conducted by the author) with first-year pharmacy students about community placements. It illustrates how focus groups provide a chance for participants to discuss issues on which they might disagree.

Interviewer: So you are saying that you would prefer health related placements? Student 1: Not exactly so long as I could be developing my communication skill. Student 2: Yes but I still think the more health related the placement is the more I'll gain from it. Student 3: I disagree because other people related skills are useful and you may learn those from taking part in a community project like building a garden. Interviewer: So would you prefer a mixture of health and non health related community placements?

GUIDANCE FOR PUBLISHING QUALITATIVE RESEARCH

Qualitative research is becoming increasingly accepted and published in pharmacy and medical journals. Some journals and publishers have guidelines for presenting qualitative research, for example, the British Medical Journal 9 and Biomedcentral . 10 Medical Education published a useful series of articles on qualitative research. 11 Some of the important issues that should be considered by authors, reviewers and editors when publishing qualitative research are discussed below.

Introduction.

A good introduction provides a brief overview of the manuscript, including the research question and a statement justifying the research question and the reasons for using qualitative research methods. This section also should provide background information, including relevant literature from pharmacy, medicine, and other health professions, as well as literature from the field of education that addresses similar issues. Any specific educational or research terminology used in the manuscript should be defined in the introduction.

The methods section should clearly state and justify why the particular method, for example, face to face semistructured interviews, was chosen. The method should be outlined and illustrated with examples such as the interview questions, focusing exercises, observation criteria, etc. The criteria for selecting the study participants should then be explained and justified. The way in which the participants were recruited and by whom also must be stated. A brief explanation/description should be included of those who were invited to participate but chose not to. It is important to consider “fair dealing,” ie, whether the research design explicitly incorporates a wide range of different perspectives so that the viewpoint of 1 group is never presented as if it represents the sole truth about any situation. The process by which ethical and or research/institutional governance approval was obtained should be described and cited.

The study sample and the research setting should be described. Sampling differs between qualitative and quantitative studies. In quantitative survey studies, it is important to select probability samples so that statistics can be used to provide generalizations to the population from which the sample was drawn. Qualitative research necessitates having a small sample because of the detailed and intensive work required for the study. So sample sizes are not calculated using mathematical rules and probability statistics are not applied. Instead qualitative researchers should describe their sample in terms of characteristics and relevance to the wider population. Purposive sampling is common in qualitative research. Particular individuals are chosen with characteristics relevant to the study who are thought will be most informative. Purposive sampling also may be used to produce maximum variation within a sample. Participants being chosen based for example, on year of study, gender, place of work, etc. Representative samples also may be used, for example, 20 students from each of 6 schools of pharmacy. Convenience samples involve the researcher choosing those who are either most accessible or most willing to take part. This may be fine for exploratory studies; however, this form of sampling may be biased and unrepresentative of the population in question. Theoretical sampling uses insights gained from previous research to inform sample selection for a new study. The method for gaining informed consent from the participants should be described, as well as how anonymity and confidentiality of subjects were guaranteed. The method of recording, eg, audio or video recording, should be noted, along with procedures used for transcribing the data.

Data Analysis.

A description of how the data were analyzed also should be included. Was computer-aided qualitative data analysis software such as NVivo (QSR International, Cambridge, MA) used? Arrival at “data saturation” or the end of data collection should then be described and justified. A good rule when considering how much information to include is that readers should have been given enough information to be able to carry out similar research themselves.

One of the strengths of qualitative research is the recognition that data must always be understood in relation to the context of their production. 1 The analytical approach taken should be described in detail and theoretically justified in light of the research question. If the analysis was repeated by more than 1 researcher to ensure reliability or trustworthiness, this should be stated and methods of resolving any disagreements clearly described. Some researchers ask participants to check the data. If this was done, it should be fully discussed in the paper.

An adequate account of how the findings were produced should be included A description of how the themes and concepts were derived from the data also should be included. Was an inductive or deductive process used? The analysis should not be limited to just those issues that the researcher thinks are important, anticipated themes, but also consider issues that participants raised, ie, emergent themes. Qualitative researchers must be open regarding the data analysis and provide evidence of their thinking, for example, were alternative explanations for the data considered and dismissed, and if so, why were they dismissed? It also is important to present outlying or negative/deviant cases that did not fit with the central interpretation.

The interpretation should usually be grounded in interviewees or respondents' contributions and may be semi-quantified, if this is possible or appropriate, for example, “Half of the respondents said …” “The majority said …” “Three said…” Readers should be presented with data that enable them to “see what the researcher is talking about.” 1 Sufficient data should be presented to allow the reader to clearly see the relationship between the data and the interpretation of the data. Qualitative data conventionally are presented by using illustrative quotes. Quotes are “raw data” and should be compiled and analyzed, not just listed. There should be an explanation of how the quotes were chosen and how they are labeled. For example, have pseudonyms been given to each respondent or are the respondents identified using codes, and if so, how? It is important for the reader to be able to see that a range of participants have contributed to the data and that not all the quotes are drawn from 1 or 2 individuals. There is a tendency for authors to overuse quotes and for papers to be dominated by a series of long quotes with little analysis or discussion. This should be avoided.

Participants do not always state the truth and may say what they think the interviewer wishes to hear. A good qualitative researcher should not only examine what people say but also consider how they structured their responses and how they talked about the subject being discussed, for example, the person's emotions, tone, nonverbal communication, etc. If the research was triangulated with other qualitative or quantitative data, this should be discussed.

Discussion.

The findings should be presented in the context of any similar previous research and or theories. A discussion of the existing literature and how this present research contributes to the area should be included. A consideration must also be made about how transferrable the research would be to other settings. Any particular strengths and limitations of the research also should be discussed. It is common practice to include some discussion within the results section of qualitative research and follow with a concluding discussion.

The author also should reflect on their own influence on the data, including a consideration of how the researcher(s) may have introduced bias to the results. The researcher should critically examine their own influence on the design and development of the research, as well as on data collection and interpretation of the data, eg, were they an experienced teacher who researched teaching methods? If so, they should discuss how this might have influenced their interpretation of the results.

Conclusion.

The conclusion should summarize the main findings from the study and emphasize what the study adds to knowledge in the area being studied. Mays and Pope suggest the researcher ask the following 3 questions to determine whether the conclusions of a qualitative study are valid 12 : How well does this analysis explain why people behave in the way they do? How comprehensible would this explanation be to a thoughtful participant in the setting? How well does the explanation cohere with what we already know?

CHECKLIST FOR QUALITATIVE PAPERS

This paper establishes criteria for judging the quality of qualitative research. It provides guidance for authors and reviewers to prepare and review qualitative research papers for the American Journal of Pharmaceutical Education . A checklist is provided in Appendix 1 to assist both authors and reviewers of qualitative data.

ACKNOWLEDGEMENTS

Thank you to the 3 reviewers whose ideas helped me to shape this paper.

Appendix 1. Checklist for authors and reviewers of qualitative research.

Introduction

• □ Research question is clearly stated.
• □ Research question is justified and related to the existing knowledge base (empirical research, theory, policy).
• □ Any specific research or educational terminology used later in manuscript is defined.
• □ The process by which ethical and or research/institutional governance approval was obtained is described and cited.
• □ Reason for choosing particular research method is stated.
• □ Criteria for selecting study participants are explained and justified.
• □ Recruitment methods are explicitly stated.
• □ Details of who chose not to participate and why are given.
• □ Study sample and research setting used are described.
• □ Method for gaining informed consent from the participants is described.
• □ Maintenance/Preservation of subject anonymity and confidentiality is described.
• □ Method of recording data (eg, audio or video recording) and procedures for transcribing data are described.
• □ Methods are outlined and examples given (eg, interview guide).
• □ Decision to stop data collection is described and justified.
• □ Data analysis and verification are described, including by whom they were performed.
• □ Methods for identifying/extrapolating themes and concepts from the data are discussed.
• □ Sufficient data are presented to allow a reader to assess whether or not the interpretation is supported by the data.
• □ Outlying or negative/deviant cases that do not fit with the central interpretation are presented.
• □ Transferability of research findings to other settings is discussed.
• □ Findings are presented in the context of any similar previous research and social theories.
• □ Discussion often is incorporated into the results in qualitative papers.
• □ A discussion of the existing literature and how this present research contributes to the area is included.
• □ Any particular strengths and limitations of the research are discussed.
• □ Reflection of the influence of the researcher(s) on the data, including a consideration of how the researcher(s) may have introduced bias to the results is included.

Conclusions

• □ The conclusion states the main finings of the study and emphasizes what the study adds to knowledge in the subject area.

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Qualitative data analysis: a practical example

2013, Evidence-Based Nursing

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6 qualitative data analysis examples to inspire you

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

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

Last updated

Reading time.

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

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

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

Find clarity on what customers want

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

Get inspired with 6 qualitative data analysis examples 

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

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

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

1. Art.com 

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

Their approach to qualitative data analysis

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

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

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

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

Thematic’s Impact tool

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

Qualitative data analysis for the win

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

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

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

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

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

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

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

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

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

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

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

Pro tip : want to follow Matalan’s lead? 

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

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

Yatter's approach to qualitative data analysis

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

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

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

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

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

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

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

4. WatchShop

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

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

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

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

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

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

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

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

5. Materials Market

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

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

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

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

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

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

Qualitative data analysis for the win 

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

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

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

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

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

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

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

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

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

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

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

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

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

Find ways to make your qualitative data work for you

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

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

FAQs about qualitative data analysis

What are some examples of qualitative data analysis.

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

Thematic analysis with tools like Thematic.com and EnjoyHQ

Sentiment analysis with tools like Lumoa

Root-cause analysis with tools like Hotjar

What are the types of qualitative data analysis?

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

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

Thematic analysis: looking for common themes that emerge

Sentiment analysis: exploring what people feel and why

Narrative analysis: examining the stories people tell 

How can you get started with qualitative data analysis?

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

What are the benefits of qualitative data analysis?

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

Building customer empathy

Improving customer acquisition and retention

Boosting engagement 

Recognizing confusion about messaging

Improving website experiences

How do qualitative and quantitative data analysis work together?

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

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Quantitative vs. Qualitative Research Design: Understanding the Differences

As a future professional in the social and education landscape, research design is one of the most critical strategies that you will master to identify challenges, ask questions and form data-driven solutions to address problems specific to your industry. 

Many approaches to research design exist, and not all work in every circumstance. While all data-focused research methods are valid in their own right, certain research design methods are more appropriate for specific study objectives.

Unlock our resource to learn more about jump starting a career in research design — Research Design and Data Analysis for the Social Good .

We will discuss the differences between quantitative (numerical and statistics-focused) and qualitative (non-numerical and human-focused) research design methods so that you can determine which approach is most strategic given your specific area of graduate-level study. 

Understanding Social Phenomena: Qualitative Research Design

Qualitative research focuses on understanding a phenomenon based on human experience and individual perception. It is a non-numerical methodology relying on interpreting a process or result. Qualitative research also paves the way for uncovering other hypotheses related to social phenomena. 

In its most basic form, qualitative research is exploratory in nature and seeks to understand the subjective experience of individuals based on social reality.

Qualitative data is…

• often used in fields related to education, sociology and anthropology; 
• designed to arrive at conclusions regarding social phenomena; 
• focused on data-gathering techniques like interviews, focus groups or case studies; 
• dedicated to perpetuating a flexible, adaptive approach to data gathering;
• known to lead professionals to deeper insights within the overall research study.

You want to use qualitative data research design if:

• you work in a field concerned with enhancing humankind through the lens of social change;
• your research focuses on understanding complex social trends and individual perceptions of those trends;
• you have interests related to human development and interpersonal relationships.

Examples of Qualitative Research Design in Education

Here are just a few examples of how qualitative research design methods can impact education:

Example 1: Former educators participate in in-depth interviews to help determine why a specific school is experiencing a higher-than-average turnover rate compared to other schools in the region. These interviews help determine the types of resources that will make a difference in teacher retention. 

Example 2: Focus group discussions occur to understand the challenges that neurodivergent students experience in the classroom daily. These discussions prepare administrators, staff, teachers and parents to understand the kinds of support that will augment and improve student outcomes.

Example 3: Case studies examine the impacts of a new education policy that limits the number of teacher aids required in a special needs classroom. These findings help policymakers determine whether the new policy affects the learning outcomes of a particular class of students.

Interpreting the Numbers: Quantitative Research Design

Quantitative research tests hypotheses and measures connections between variables. It relies on insights derived from numbers — countable, measurable and statistically sound data. Quantitative research is a strategic research design used when basing critical decisions on statistical conclusions and quantifiable data.

Quantitative research provides numerical-backed quantifiable data that may approve or discount a theory or hypothesis.

Quantitative data is…

• often used in fields related to education, data analysis and healthcare; 
• designed to arrive at numerical, statistical conclusions based on objective facts;
• focused on data-gathering techniques like experiments, surveys or observations;
• dedicated to using mathematical principles to arrive at conclusions;
• known to lead professionals to indisputable observations within the overall research study.

You want to use quantitative data research design if:

• you work in a field concerned with analyzing data to inform decisions;
• your research focuses on studying relationships between variables to form data-driven conclusions;
• you have interests related to mathematics, statistical analysis and data science.

Examples of Quantitative Research Design in Education

Here are just a few examples of how quantitative research design methods may impact education:

Example 1: Researchers compile data to understand the connection between class sizes and standardized test scores. Researchers can determine if and what the relationship is between smaller, intimate class sizes and higher test scores for grade-school children using statistical and data analysis.

Example 2: Professionals conduct an experiment in which a group of high school students must complete a certain number of community service hours before graduation. Researchers compare those students to another group of students who did not complete service hours — using statistical analysis to determine if the requirement increased college acceptance rates.

Example 3: Teachers take a survey to examine an education policy that restricts the number of extracurricular activities offered at a particular academic institution. The findings help better understand the far-reaching impacts of extracurricular opportunities on academic performance.

Making the Most of Research Design Methods for Good: Vanderbilt University’s Peabody College

Vanderbilt University's Peabody College of Education and Human Development offers a variety of respected, nationally-recognized graduate programs designed with future agents of social change in mind. We foster a culture of excellence and compassion and guide you to become the best you can be — both in the classroom and beyond.

At Peabody College, you will experience

• an inclusive, welcoming community of like-minded professionals;
• the guidance of expert faculty with real-world industry experience;
• opportunities for valuable, hands-on learning experiences,
• the option of specializing depending on your specific area of interest.

Explore our monthly publication — Ideas in Action — for an inside look at how Peabody College translates discoveries into action.

Please click below to explore a few of the graduate degrees offered at Peabody College:

• Child Studies M.Ed. — a rigorous Master of Education degree that prepares students to examine the developmental, learning and social issues concerning children and that allows students to choose from one of two tracks (the Clinical and Developmental Research Track or the Applied Professional Track).
• Cognitive Psychology in Context M.S. — an impactful Master of Science program that emphasizes research design and statistical analysis to understand cognitive processes and real-world applications best, making it perfect for those interested in pursuing doctoral studies in cognitive science.
• Education Policy M.P.P — an analysis-focused Master of Public Policy program designed for future leaders in education policy and practice, allowing students to specialize in either K-12 Education Policy, Higher Education Policy or Quantitative Methods in Education Policy. 
• Quantitative Methods M.Ed. — a data-driven Master of Education degree that teaches the theory and application of quantitative analysis in behavioral, social and educational sciences.

Connect with the Community of Professionals Seeking to Enhance Humankind at Peabody College

At Peabody College, we equip you with the marketable, transferable skills needed to secure a valuable career in education and beyond. You will emerge from the graduate program of your choice ready to enhance humankind in more meaningful ways than you could have imagined.

If you want to develop the sought-after skills needed to be a force for change in the social and educational spaces, you are in the right place .

We invite you to request more information ; we will connect you with an admissions professional who can answer all your questions about choosing one of these transformative graduate degrees at Peabody College. You may also take this opportunity to review our admissions requirements and start your online application today. 

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Data Analysis in Research

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Data analysis in research involves systematically applying statistical and logical techniques to describe, illustrate, condense, and evaluate data. It is a crucial step that enables researchers to identify patterns, relationships, and trends within the data, transforming raw information into valuable insights. Through methods such as descriptive statistics, inferential statistics, and qualitative analysis, researchers can interpret their findings, draw conclusions, and support decision-making processes. An effective data analysis plan and robust methodology ensure the accuracy and reliability of research outcomes, ultimately contributing to the advancement of knowledge across various fields.

What is Data Analysis in Research?

Data analysis in research involves using statistical and logical techniques to describe, summarize, and compare collected data. This includes inspecting, cleaning, transforming, and modeling data to find useful information and support decision-making. Quantitative data provides measurable insights, and a solid research design ensures accuracy and reliability. This process helps validate hypotheses, identify patterns, and make informed conclusions, making it a crucial step in the scientific method.

Examples of Data analysis in Research

• Survey Analysis : Researchers collect survey responses from a sample population to gauge opinions, behaviors, or characteristics. Using descriptive statistics, they summarize the data through means, medians, and modes, and then inferential statistics to generalize findings to a larger population.
• Experimental Analysis : In scientific experiments, researchers manipulate one or more variables to observe the effect on a dependent variable. Data is analyzed using methods such as ANOVA or regression analysis to determine if changes in the independent variable(s) significantly affect the dependent variable.
• Content Analysis : Qualitative research often involves analyzing textual data, such as interview transcripts or open-ended survey responses. Researchers code the data to identify recurring themes, patterns, and categories, providing a deeper understanding of the subject matter.
• Correlation Studies : Researchers explore the relationship between two or more variables using correlation coefficients. For example, a study might examine the correlation between hours of study and academic performance to identify if there is a significant positive relationship.
• Longitudinal Analysis : This type of analysis involves collecting data from the same subjects over a period of time. Researchers analyze this data to observe changes and developments, such as studying the long-term effects of a specific educational intervention on student achievement.
• Meta-Analysis : By combining data from multiple studies, researchers perform a meta-analysis to increase the overall sample size and enhance the reliability of findings. This method helps in synthesizing research results to draw broader conclusions about a particular topic or intervention.

Data analysis in Qualitative Research

Data analysis in qualitative research involves systematically examining non-numeric data, such as interviews, observations, and textual materials, to identify patterns, themes, and meanings. Here are some key steps and methods used in qualitative data analysis:

• Coding : Researchers categorize the data by assigning labels or codes to specific segments of the text. These codes represent themes or concepts relevant to the research question.
• Thematic Analysis : This method involves identifying and analyzing patterns or themes within the data. Researchers review coded data to find recurring topics and construct a coherent narrative around these themes.
• Content Analysis : A systematic approach to categorize verbal or behavioral data to classify, summarize, and tabulate the data. This method often involves counting the frequency of specific words or phrases.
• Narrative Analysis : Researchers focus on the stories and experiences shared by participants, analyzing the structure, content, and context of the narratives to understand how individuals make sense of their experiences.
• Grounded Theory : This method involves generating a theory based on the data collected. Researchers collect and analyze data simultaneously, continually refining and adjusting their theoretical framework as new data emerges.
• Discourse Analysis : Examining language use and communication patterns within the data, researchers analyze how language constructs social realities and power relationships.
• Case Study Analysis : An in-depth analysis of a single case or multiple cases, exploring the complexities and unique aspects of each case to gain a deeper understanding of the phenomenon under study.

Data analysis in Quantitative Research

Data analysis in quantitative research involves the systematic application of statistical techniques to numerical data to identify patterns, relationships, and trends. Here are some common methods used in quantitative data analysis:

• Descriptive Statistics : This includes measures such as mean, median, mode, standard deviation, and range, which summarize and describe the main features of a data set.
• Inferential Statistics : Techniques like t-tests, chi-square tests, and ANOVA (Analysis of Variance) are used to make inferences or generalizations about a population based on a sample.
• Regression Analysis : This method examines the relationship between dependent and independent variables. Simple linear regression analyzes the relationship between two variables, while multiple regression examines the relationship between one dependent variable and several independent variables.
• Correlation Analysis : Researchers use correlation coefficients to measure the strength and direction of the relationship between two variables.
• Factor Analysis : This technique is used to identify underlying relationships between variables by grouping them into factors based on their correlations.
• Cluster Analysis : A method used to group a set of objects or cases into clusters, where objects in the same cluster are more similar to each other than to those in other clusters.
• Hypothesis Testing : This involves testing an assumption or hypothesis about a population parameter. Common tests include z-tests, t-tests, and chi-square tests, which help determine if there is enough evidence to reject the null hypothesis.
• Time Series Analysis : This method analyzes data points collected or recorded at specific time intervals to identify trends, cycles, and seasonal variations.
• Multivariate Analysis : Techniques like MANOVA (Multivariate Analysis of Variance) and PCA (Principal Component Analysis) are used to analyze data that involves multiple variables to understand their effect and relationships.
• Structural Equation Modeling (SEM) : A multivariate statistical analysis technique that is used to analyze structural relationships. This method is a combination of factor analysis and multiple regression analysis and is used to analyze the structural relationship between measured variables and latent constructs.

Data analysis in Research Methodology

Data analysis in research methodology involves the process of systematically applying statistical and logical techniques to describe, condense, recap, and evaluate data. Here are the key components and methods involved:

• Data Preparation : This step includes collecting, cleaning, and organizing raw data. Researchers ensure data quality by handling missing values, removing duplicates, and correcting errors.
• Descriptive Analysis : Researchers use descriptive statistics to summarize the basic features of the data. This includes measures such as mean, median, mode, standard deviation, and graphical representations like histograms and pie charts.
• Inferential Analysis : This involves using statistical tests to make inferences about the population from which the sample was drawn. Common techniques include t-tests, chi-square tests, ANOVA, and regression analysis.
• Qualitative Data Analysis : For non-numeric data, researchers employ methods like coding, thematic analysis, content analysis, narrative analysis, and discourse analysis to identify patterns and themes.
• Quantitative Data Analysis : For numeric data, researchers apply statistical methods such as correlation, regression, factor analysis, cluster analysis, and time series analysis to identify relationships and trends.
• Hypothesis Testing : Researchers test hypotheses using statistical methods to determine whether there is enough evidence to reject the null hypothesis. This involves calculating p-values and confidence intervals.
• Data Interpretation : This step involves interpreting the results of the data analysis. Researchers draw conclusions based on the statistical findings and relate them back to the research questions and objectives.
• Validation and Reliability : Ensuring the validity and reliability of the analysis is crucial. Researchers check for consistency in the results and use methods like cross-validation and reliability testing to confirm their findings.
• Visualization : Effective data visualization techniques, such as charts, graphs, and plots, are used to present the data in a clear and understandable manner, aiding in the interpretation and communication of results.
• Reporting : The final step involves reporting the results in a structured format, often including an introduction, methodology, results, discussion, and conclusion. This report should clearly convey the findings and their implications for the research question.

Types of Data analysis in Research

• Purpose : To summarize and describe the main features of a dataset.
• Methods : Mean, median, mode, standard deviation, frequency distributions, and graphical representations like histograms and pie charts.
• Example : Calculating the average test scores of students in a class.
• Purpose : To make inferences or generalizations about a population based on a sample.
• Methods : T-tests, chi-square tests, ANOVA (Analysis of Variance), regression analysis, and confidence intervals.
• Example : Testing whether a new teaching method significantly affects student performance compared to a traditional method.
• Purpose : To analyze data sets to find patterns, anomalies, and test hypotheses.
• Methods : Visualization techniques like box plots, scatter plots, and heat maps; summary statistics.
• Example : Visualizing the relationship between hours of study and exam scores using a scatter plot.
• Purpose : To make predictions about future outcomes based on historical data.
• Methods : Regression analysis, machine learning algorithms (e.g., decision trees, neural networks), and time series analysis.
• Example : Predicting student graduation rates based on their academic performance and demographic data.
• Purpose : To provide recommendations for decision-making based on data analysis.
• Methods : Optimization algorithms, simulation, and decision analysis.
• Example : Suggesting the best course of action for improving student retention rates based on various predictive factors.
• Purpose : To identify and understand cause-and-effect relationships.
• Methods : Controlled experiments, regression analysis, path analysis, and structural equation modeling (SEM).
• Example : Determining the impact of a specific intervention, like a new curriculum, on student learning outcomes.
• Purpose : To understand the specific mechanisms through which variables affect one another.
• Methods : Detailed modeling and simulation, often used in scientific research to understand biological or physical processes.
• Example : Studying how a specific drug interacts with biological pathways to affect patient health.

How to write Data analysis in Research

Data analysis is crucial for interpreting collected data and drawing meaningful conclusions. Follow these steps to write an effective data analysis section in your research.

1. Prepare Your Data

Ensure your data is clean and organized:

• Remove duplicates and irrelevant data.
• Check for errors and correct them.
• Categorize data if necessary.

2. Choose the Right Analysis Method

Select a method that fits your data type and research question:

• Quantitative Data : Use statistical analysis such as t-tests, ANOVA, regression analysis.
• Qualitative Data : Use thematic analysis, content analysis, or narrative analysis.

3. Describe Your Analytical Techniques

Clearly explain the methods you used:

• Software and Tools : Mention any software (e.g., SPSS, NVivo) used.
• Statistical Tests : Detail the statistical tests applied, such as chi-square tests or correlation analysis.
• Qualitative Techniques : Describe coding and theme identification processes.

4. Present Your Findings

Organize your findings logically:

• Use Tables and Figures : Display data in tables, graphs, and charts for clarity.
• Summarize Key Results : Highlight the most significant findings.
• Include Relevant Statistics : Report p-values, confidence intervals, means, and standard deviations.

5. Interpret the Results

Explain what your findings mean in the context of your research:

• Compare with Hypotheses : State whether the results support your hypotheses.
• Relate to Literature : Compare your results with previous studies.
• Discuss Implications : Explain the significance of your findings.

6. Discuss Limitations

Acknowledge any limitations in your data or analysis:

• Sample Size : Note if the sample size was small.
• Biases : Mention any potential biases in data collection.
• External Factors : Discuss any factors that might have influenced the results.

7. Conclude with a Summary

Wrap up your data analysis section:

• Restate Key Findings : Briefly summarize the main results.
• Future Research : Suggest areas for further investigation.

Importance of Data analysis in Research

Data analysis is a fundamental component of the research process. Here are five key points highlighting its importance:

• Enhances Accuracy and Reliability Data analysis ensures that research findings are accurate and reliable. By using statistical techniques, researchers can minimize errors and biases, ensuring that the results are dependable.
• Facilitates Informed Decision-Making Through data analysis, researchers can make informed decisions based on empirical evidence. This is crucial in fields like healthcare, business, and social sciences, where decisions impact policies, strategies, and outcomes.
• Identifies Trends and Patterns Analyzing data helps researchers uncover trends and patterns that might not be immediately visible. These insights can lead to new hypotheses and areas of study, advancing knowledge in the field.
• Supports Hypothesis Testing Data analysis is vital for testing hypotheses. Researchers can use statistical methods to determine whether their hypotheses are supported or refuted, which is essential for validating theories and advancing scientific understanding.
• Provides a Basis for Predictions By analyzing current and historical data, researchers can develop models that predict future outcomes. This predictive capability is valuable in numerous fields, including economics, climate science, and public health.

FAQ’s

What is the difference between qualitative and quantitative data analysis.

Qualitative analysis focuses on non-numerical data to understand concepts, while quantitative analysis deals with numerical data to identify patterns and relationships.

What is descriptive statistics?

Descriptive statistics summarize and describe the features of a data set, including measures like mean, median, mode, and standard deviation.

What is inferential statistics?

Inferential statistics use sample data to make generalizations about a larger population, often through hypothesis testing and confidence intervals.

What is regression analysis?

Regression analysis examines the relationship between dependent and independent variables, helping to predict outcomes and understand variable impacts.

What is the role of software in data analysis?

Software like SPSS, R, and Excel facilitate data analysis by providing tools for statistical calculations, visualization, and data management.

What are data visualization techniques?

Data visualization techniques include charts, graphs, and maps, which help in presenting data insights clearly and effectively.

What is data cleaning?

Data cleaning involves removing errors, inconsistencies, and missing values from a data set to ensure accuracy and reliability in analysis.

What is the significance of sample size in data analysis?

Sample size affects the accuracy and generalizability of results; larger samples generally provide more reliable insights.

How does correlation differ from causation?

Correlation indicates a relationship between variables, while causation implies one variable directly affects the other.

What are the ethical considerations in data analysis?

Ethical considerations include ensuring data privacy, obtaining informed consent, and avoiding data manipulation or misrepresentation.

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Research Methods: Qualitative

What is qualitative research, about this guide, introduction.

• Qualitative Research Approaches
• Key Resources
• Finding Qualitative Studies

 The purpose of this guide is to provide a starting point for learning about qualitative research. In this guide, you'll find:

• Resources on diverse types of qualitative research.
• An overview of resources for data, methods, coding & analysis
• Popular qualitative software options
• Information on how to find qualitative studies

Qualitative research involves collecting and analyzing non-numerical data (e.g., text, video, or audio) to understand concepts, opinions, or experiences. It can be used to gather in-depth insights into a problem or generate new ideas for research. It answers the hows and whys instead of how many or how much. It could be structured as a stand-alone study, purely relying on qualitative data or it could be part of mixed-methods research that combines qualitative and quantitative data.

Qualitative researchers use multiple systems of inquiry for the study of human phenomena including biography, case study, historical analysis, discourse analysis, ethnography, grounded theory, and phenomenology.

Watch the following video to learn more about Qualitative Research:

(Video best viewed in Edge and Chrome browsers, or click here to view in the Sage Research Methods Database )

Qualitative Approaches

The case study approach is useful to employ when there is a need to obtain an in-depth appreciation of an issue, event or phenomenon of interest, in its natural real-life context.

Ethnography

Ethnographies are an in-depth, holistic type of research used to capture cultural practices, beliefs, traditions, and so on. Here, the researcher observes and interviews members of a culture an ethnic group, a clique, members of a religion, etc. and then analyzes their findings.

Grounded Theory

Researchers will create and test a hypothesis using qualitative data. Often, researchers use grounded theory to understand decision-making, problem-solving, and other types of behavior.

Narrative Research

Researchers use this type of framework to understand different aspects of the human experience and how their subjects assign meaning to their experiences. Researchers use interviews to collect data from a small group of subjects, then discuss those results in the form of a narrative or story.

Phenomenology

This type of research attempts to understand the lived experiences of a group and/or how members of that group find meaning in their experiences. Researchers use interviews, observation, and other qualitative methods to collect data.

Watch the video "Choosing among the Five Qualitative Approaches" from Sage Research Methods database for more on these qualitative approaches:

Note: Video is best viewed using Chrome, Edge, or Safari browsers.

Poth, C. (Academic). (2023). Choosing among five qualitative approaches [Video]. Sage Research Methods. doi.org/10.4135/9781529629866

• Next: Key Resources >>
• Last Updated: Jul 18, 2024 12:37 PM
• URL: https://library.thechicagoschool.edu/qualitative

• DOI: 10.2196/52998
• Corpus ID: 269705598

Determining an Appropriate Sample Size for Qualitative Interviews to Achieve True and Near Code Saturation: Secondary Analysis of Data.

• Claudia M Squire , Kristen C. Giombi , +2 authors Peyton Williams
• Published in Journal of Medical Internet… 22 September 2023

19 References

A simple method to assess and report thematic saturation in qualitative research, what is an adequate sample size operationalising data saturation for theory-based interview studies, sample sizes for saturation in qualitative research: a systematic review of empirical tests., open-ended interview questions and saturation, informing a priori sample size estimation in qualitative concept elicitation interview studies for clinical outcome assessment instrument development., a comparative method for themes saturation (comets) in qualitative interviews, how many interviews are enough to identify metathemes in multisited and cross-cultural research another perspective on guest, bunce, and johnson’s (2006) landmark study, quantifying thematic saturation in qualitative data analysis, characterising and justifying sample size sufficiency in interview-based studies: systematic analysis of qualitative health research over a 15-year period, code saturation versus meaning saturation, related papers.

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What is Data-Driven Analysis? Methods and Examples

Harnessing the power of data-driven analysis can transform how you make decisions , fine-tune your strategies, and, ultimately, boost your bottom line.

But what exactly does it involve, and how can you use it to your advantage? This article will be your go-to guide, breaking down the concept of data-driven analysis and showing you practical ways to apply it to your specific business needs.

• Data-driven analysis is the process of using data to make strategic decisions or gain insights. It cuts across different business processes — you can implement the principles to create data-driven marketing campaigns, improve your product offerings, optimize customer support , and so on.
• A reliable data-driven approach…
• Helps you make the right decisions .
• Enhances customer understanding.
• Improves accountability and transparency within your organization.
• Examples of data analysis scenarios
• Qualitative data analysis.
• Quantitative data analysis.
• Sentiment analysis.
• How to become a data-driven organization:
• Encourage all teams – from marketing and sales to customer success and product development – to view data as an invaluable asset that can inform their decision-making process.
• Establish a data-driven strategy where small and big data can flow freely and securely across different departments (you can use tools like Userpilot ).
• Leverage no-code analytics tools for reports (that way, your non-technical teams won’t have to rely on professional analysts for predictive analysis and data-driven decision-making).
• Ready to implement a data-driven approach to your processes? Book a demo and see how Userpilot can help you track the different analysis types and deploy contextual in-app solutions.

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What is data-driven analysis?

Data-driven analysis is the process of using data to make strategic decisions or gain insights. It involves collecting, analyzing, and interpreting data to reveal patterns , trends , and relationships between components.

By leveraging statistical tools and software, data-driven companies are able to make evidence-based decisions, enhancing accuracy and reducing biases.

Why should you take a data-driven approach?

The benefits of data-driven analysis are enormous. Here are a few:

• Informed decision-making : Data-driven analysis provides a factual basis for decision-making, reducing the risk of relying on gut feelings or inaccurate assumptions. By analyzing trends , patterns, and correlations in data, you can identify opportunities, anticipate challenges, and make strategic choices that are more likely to lead to success.
• Enhanced customer understanding : A data-driven strategy gives valuable insights into customer preferences , behaviors , and pain points . This enables you to personalize products, services, and marketing campaigns , tailoring them to meet the specific needs of your target audience.
• Accountability and transparency : A data-driven approach is inherently transparent because the evidence supporting decisions is laid out. This promotes accountability, as it’s easy to track the performance of different strategies and determine who was responsible for each. This transparency can build trust among stakeholders and foster a culture of continuous improvement within the organization.

Examples of data analysis methods

Data analysis methods vary depending on the specific insights you need. Here are a few common examples:

Qualitative data analysis

This method involves analyzing non-numerical data , such as text-based feedback surveys, user interviews, and customer reviews. It often focuses on identifying themes, patterns, and meanings within the data.

Qualitative analysis is best used when you need to uncover the “why” behind user behavior. For example, if you notice a decline in user engagement, you can trigger qualitative surveys and analyze the responses to understand user pain points and note improvement areas.

Quantitative data analysis

This method focuses on numerical data, allowing you to track and measure key performance indicators (KPIs) for your business. You can analyze user engagement metrics , funnel drops , conversion rates, NPS scores, and churn rates to assess your product’s success and identify areas for optimization.

Quantitative analysis can help you make data-driven decisions about pricing , marketing strategies , and product development . For example, you can use funnel analysis to understand where drop-offs occur most in your conversion process and deploy data-driven solutions.

Sentiment analysis

This type of analysis evaluates textual data to determine the sentiment or emotional tone behind it.

Sentiment analysis uses natural language processing and machine learning to classify opinions as positive, negative, or neutral, making it effective for social media monitoring, customer feedback analysis , and brand reputation management.

One valuable application of this analysis is tracking customer sentiment over time. By monitoring the changing emotional tone in user or company accounts, you can quickly identify when a power user is losing interest in your product or, conversely, when a user is becoming more engaged. This valuable insight can help you proactively address potential issues and capitalize on opportunities.

Userpilot’s Profile Analysis feature can help you effortlessly identify sentiment trends for individual and company accounts:

How to build a data-driven culture for your SaaS?

Creating a data-driven culture in your SaaS organization involves several key steps to ensure data is accessible, understandable, and used to inform decision-making. Follow these steps to get started:

Facilitate data collection across departments

Encourage all teams – from marketing and sales to customer success and product development – to view data as an invaluable asset that can inform decision-making. Regularly discuss relevant metrics and tie them to team goals to reinforce this data-driven mindset.

To streamline data collection, leverage no-code and low-code tools like Userpilot to automate event tracking . These tools simplify capturing user actions within your product—such as button clicks, feature usage , and sign-ups—without requiring extensive coding knowledge.

For instance, you can configure Userpilot to track how many users complete your onboarding flow or engage with specific features, providing insights into areas that might require refinement.

Enable data sharing

Data silos are a common obstacle to a truly data-driven culture. To overcome this, it’s imperative you establish a data management system where relevant data can flow freely and securely across different departments.

Centralized analytics dashboards , reports , and data visualization tools play a pivotal role in achieving this. These platforms offer a unified view of key metrics and insights, ensuring everyone has access to the information they need.

However, access alone is not enough. To prevent “data blindness,” where existing data sits unused and insights are missed, prioritize regular communication – share data analysis reports and discuss key findings in cross-departmental meetings. This collaborative, data-driven approach fosters a shared understanding of the company’s performance and facilitates informed decision-making at all levels.

Leverage no-code analytics tools for reports

No-code analytics tools empower individuals with varying technical skills to generate custom analytics reports and dashboards through intuitive interfaces. This removes the dependency on data analysts and accelerates the process of turning raw data into actionable insights .

Userpilot makes it much easier. You can use templates to set up data analytics dashboards that track your most important metrics or build custom dashboards from scratch, only including the metrics you want to see.

For instance, your product team can set a dashboard to monitor adoption rates , while customer success tracks help center visits and onboarding completion – all without writing a single line of code. This ease of use and customization will encourage greater engagement with data across your organization, leading to a more data-driven approach to problem-solving and decision-making.

For comprehensive insights, it’s best to combine different data analysis methods. Qualitative analysis uncovers the motivations behind customer behavior, quantitative analysis illuminates what customers are doing, and sentiment analysis gauges how customers feel about your product or brand.

Ready to get started with data-driven analysis? Book a demo and see how Userpilot can help you track the different analysis types and deploy contextual in-app solutions.

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#1 QUALITATIVE DATA ANALYSIS SOFTWARE FOR 30 YEARS

NVivo 14 - Leading Qualitative Data Analysis Software with AI Solution

Enhance Your Use of NVivo 14

Harness the Power of AI in NVivo

Harness the Power of AI in NVivo for Qualitative Analysis

The NVivo Getting Started Bundle includes all the essentials you need for your content analysis.

An NVivo license:  The most cited and powerful QDA software for data analysis. Choose a Windows or Mac individual license.

NVivo Core Skills Online Course: Includes videos, live coaching and a Q&A forum to help you analyze qualitative data fast.

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Collaboration Cloud

Collaboration Server

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NVivo Academy

NVivo 14 Licenses

Student Licenses provide access to all the features of NVivo, limited for 12 months.

Individual and small group licenses (up to nine) can be bought online.

Organization licenses are available. If you want to purchase ten or more licenses, or enter an entreprise agreement, contact our sales team.

Enterprise Licensing: Better Research, Insights, and Outcomes for all

Lumivero’s team-based solutions allow you to:, need help choosing qda software, what is nvivo, what can i do with nvivo, who is nvivo for, how much does nvivo cost.

It's easy to buy student, individual and small group licenses (student license limited to one per account, individual and small group licenses up to nine) online.

To purchase ten or more NVivo licenses for your team or organization, Contact Us to reach our sales team or one of our international NVivo partners.

Are there free qualitative data analysis tutorials?

How do i upgrade nvivo.

For individual and small groups (less than 9 individuals), take advantage of the most recent updates and software enhancements by purchasing the latest version here .  For larger groups and institutional/enterprise users, subscription options at volume rates are offered that secure the latest features as they are released. To purchase ten or more NVivo licenses for your team or organization, Contact Us to reach our sales team or one of our international NVivo partners. Lumivero's Support team is committed to your success using our software and actively supports the two previous versions of the most current version.

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