Using Thematic Analysis and Affinity Mapping

February 9, 2024

Qualitative data provides insights into the thoughts, feelings, behaviors, and motivations of individuals. Unlike quantitative data, which focuses on numerical measurements and statistical analysis, qualitative data offers a deeper understanding of users’ experiences and perceptions.

In this post, we introduce thematic analysis and affinity mapping, explaining how these methods can help you thoroughly analyze qualitative data and derive actionable insights.

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How do you collect qualitative data?

Because qualitative data centers around understanding the depths of whys and hows, it is often based on insights from a sample size greater than 10. Qualitative data is text-based, and often arises from various research methods designed to explore subjective experiences. Common sources of qualitative data in user research include:

  • Interviews: In-depth conversations with participants allow researchers to understand participant's perspectives, experiences, and attitudes.
  • Focus groups: Group discussions facilitate interactions among participants, revealing shared beliefs, opinions, and behaviors.
  • Observations: Researchers directly observe individuals in natural or controlled settings to understand their behaviors and interactions.
  • Surveys with open-ended questions: Open-ended survey responses provide participants with the opportunity to express themselves freely, offering rich qualitative insights.
  • Diary studies: Written reflections or records maintained by participants offer firsthand accounts of their experiences over time.
🖌️ Quick Tip
For robust research insights, triangulate qualitative data with quantitative data. The richness and depth of qualitative data complement numerical data.

Common challenges when analyzing qualitative data

While qualitative data yields rich insights into user behaviors and experience, there are some common hurdles when analyzing qualitative data:

High volume and complexity

One of the primary challenges researchers face when dealing with qualitative data is the sheer volume and complexity of the information. Unlike quantitative data, which is often structured and numerical, qualitative data can consist of large amounts of unstructured text, audio, or video recordings. Sorting through this type of data can be time-consuming and overwhelming, requiring careful attention to detail.

Subjective interpretation

Another challenge is the subjective nature of qualitative analysis. Interpreting text involves making sense of individuals' thoughts, emotions, and experiences, which can vary widely from person to person. Researchers must navigate this subjectivity while maintaining objectivity in their analysis. Additionally, researchers may bring their own biases and assumptions. An effective way to minimize subjectivity is involving various team members during the analysis step and identifying common themes that are conspicuous.

Superficial insights that lack depth

There is a risk of analysis becoming a mere regurgitation of data, resulting in superficial insights derived solely from memorable quotes or descriptions. This happens when researchers focus on surface-level observations without delving deeper into underlying patterns or themes. As a result, the analysis may lack depth and fail to uncover meaningful insights that inform the research objectives.

Saturated and contradicting data

In qualitative research, there is a point of diminishing returns, in which collecting additional data no longer provides new insights or information. This is why research that involves qualitative data collection often requires less than 10 participants because even with less than 10 participants, researchers can gather a plethora of data and insights. On the other hand, contradictory data poses a challenge in qualitative analysis, particularly when the sample size is limited compared to quantitative research. Working with smaller sample sizes make it more likely to encounter conflicting or divergent perspectives among participants. Managing contradictory data requires careful consideration and interpretation to understand the underlying reasons for discrepancies and identify common themes or patterns across participants.

As mentioned above, analyzing qualitative data lets you tap into rich, in-depth insights into users’ behaviors and experiences, but at the cost of intense time and effort commitment. As textual data is broad and challenging to process, analyzing qualitative data requires a more organized and systematic approach. Thematic analysis provides a systematic approach to uncovering patterns, themes, and meanings within textual data.

What is thematic analysis?

Thematic analysis is a method for identifying, analyzing, and reporting patterns or themes within qualitative data. It involves systematically coding and categorizing data to identify recurring concepts or ideas that capture key aspects of users' experiences, perspectives, or behaviors. When conducting thematic analysis, an deductive approach is often used.

The deductive approach involves testing and validating assumptions and hypotheses that have been predefined during the study planning stage. With deductive reasoning, you know what you want to examine more carefully as you scrutinize the data.

🖌️ Quick Tip
Don’t worry if you discover new emerging themes (inductive approach). These themes could be added to your list of hypotheses as one of the surprising findings.

Coding in qualitative research

While coding may sound technical, coding in qualitative research is nothing like writing code blocks in programming languages. Coding is a process for analyzing and making sense of the data collected from participants to uncover patterns, themes, and insights. It involves a process of assigning labels or tags to segments of qualitative data, such as interview transcripts, observation notes, or survey responses, based on their content, meaning, or significance.

🖌️ Quick Tip
You can imagine coding as hashtags in social media. These hashtags label and categorize content to better organize your data.

At a high level, there are two approaches to labeling or coding your data: Descriptive and interpretive coding.

Descriptive Coding

Descriptive coding is usually conducted in the initial coding phase, where labels are assigned based on surface-level characteristics or content. These codes are succinct and descriptive, capturing the essence of the data without interpretation or inference. Descriptive coding aids in systematically organizing and categorizing data, thereby laying the groundwork for deeper analysis and interpretation

Examples of descriptive coding could be:

  • Demographic information
  • Pain points
  • Tool usage

Interpretive Coding

Interpretive coding typically extends from descriptive coding by exploring the underlying meaning, context, and implications of the data. It involves analyzing the data to reveal patterns, themes, or relationships among other coded data. This approach entails a higher level of abstraction and inference, as it necessitates interpreting the data within the broader context of the research objectives and theoretical frameworks.

Examples of interpretive coding could build upon previous descriptive examples:

  • How demographic info affects tool usage
  • Side effects caused from pain points
🖌️ Quick Tip
You can have multiple codes or labels for a single piece of data.

Coding is integral to the process as it facilitates the effective organization and structuring of data. While thematic analysis is readily accessible, proficient coding demands practice, especially when managing substantial volumes of data. Given the absence of definitive answers regarding how data should be labeled, it is advisable to engage in practice and discern which approach aligns most effectively with your methodology.

A systematic approach to analyzing qualitative data

Analyzing qualitative data can pose challenges, particularly when subjective interpretation is necessary, especially when handling large volumes of data. However, employing a systematic framework that integrates thematic analysis, deductive reasoning, and coding techniques can greatly enhance the efficacy of qualitative data analysis.

  • Thematic analysis provides a structured approach to identifying patterns and themes within the data.
  • Deductive reasoning allows you to test existing theories or hypotheses against the data, providing a framework for interpretation.
  • Systematic coding practice helps you better organize and categorize information, facilitating the identification of key themes and insights.

What is affinity mapping?

Affinity mapping or diagramming represents a specialized approach within thematic analysis. It entails visualizing individual data points and grouping them based on similarity. These groups consolidate related ideas, observations, or insights into clusters or themes, thereby revealing underlying patterns and trends. Affinity mapping can be executed through physical means, such as stickies, or digitally via whiteboard tools. Utilizing whiteboard and stickies, which are easily maneuverable idea units, facilitates collaborative data interpretation among team members. By structuring data visually into clusters or groups, affinity mapping facilitates the extraction of meaningful insights, identification of pivotal patterns, and informed decision-making within product development or design processes.

Affinity mapping or diagramming involves visually laying out and organizing the data on a physical or digital whiteboard

Step-by-step guide for affinity mapping

Below is a high level step-by-step for affinity mapping.

  1. Collect data and transcribe
  2. Organize and visualize the data
  3. Familiarize yourself with the data
  4. Code the data
  5. Evaluate for emerging themes
  6. Expand upon insights by connecting different themes
  7. Create a compelling narrative
🖌️ Quick Tip
A quick way to code the data is first grouping the data by content. Then later review each group to assign a proper label and relevant insights.

1. Collect data and transcribe

Begin by collecting qualitative data. If the data originates from audio or video recordings, it's essential to transcribe them to ensure all data is in a readable format for analysis. Several video recording software options provide transcription services that can assist in this process.

2. Organize and visualize the data

After collecting and transcribing your data, proceed to visually organize it. This may entail utilizing sticky notes or digital cards on a whiteboard to arrange the data in a format that is easy to manipulate and rearrange. Consider using different colors to represent individual participants. When arranging stickies on digital whiteboard tools, it's advisable to retain the original copy to maintain a record.

3. Familiarize yourself with the data

Take a moment to acquaint yourself with the data by skimming through it. This initial review can offer you a sense of the content and help identify any initial patterns or themes. Consider organizing a collaborative affinity mapping activity with stakeholders at this stage. Workshops are common methods for engaging the entire team. If multiple moderators are available to assist, consider dividing into smaller groups to address specific portions of the data.

4. Code the Data

Whether working collaboratively or individually, begin coding the data by assigning descriptive labels or tags to specific sections or quotes. This coding process assists in categorizing and organizing the data according to common themes or topics. There are no limits to the number of codes you can create. Consider this initial coding step as a divergent activity aimed at labeling as many initial themes as possible. If you're using a whiteboard tool, another convenient method is to place large header titles for each label and swiftly assign each piece of data accordingly.

5. Evaluate for emerging themes

After coding the data, assess it for emerging themes or patterns. Seek out recurring ideas, insights, or concepts that surface across various data points. Scrutinize each grouped data to uncover any additional patterns that may have been overlooked. These emerging themes form the foundation for constructing your affinity map.

6. Expand upon insights by connect different themes

After identifying the themes, continue to analyze the data to deepen your understanding and uncover additional insights. Try to examine how one group relates to another. This may involve revisiting the data, conducting additional coding, or seeking input from team members to further refine the analysis.

7. Create a compelling narrative

Lastly, synthesize the key themes and insights into a cohesive narrative that addresses the research questions. Craft a narrative that tells a story based on the identified themes, offering context and meaning to the data. Utilize the affinity map as a visual representation of this narrative, emphasizing connections between different themes and ideas.

Tips for effective affinity mapping

Effective affinity mapping is crucial for deriving meaningful insights from qualitative data. Here are some tips to keep in mind:

1. Avoid regurgitation of data

A summary of findings is informative, but it doesn't necessarily provide insights. While uncovering the major pain points of users is valuable, true insight comes from understanding how those pain points impact users' workflow. Instead of merely summarizing or restating the data, strive to delve deeper and unearth insights and underlying patterns. Think critically to establish connections between the identified themes.

2. Interview questions are not themes

Summarizing responses to a particular question is not synonymous with identifying themes. Instead, concentrate on grouping the data into common themes based on its content. Allow the data itself to guide your themes, rather than being influenced solely by the questions asked. This approach ensures that your analysis remains rooted in the actual observations and experiences of users.

Don’t: list out the pain points

Do: make the most outstanding pain point as the theme, providing details and supporting evidence

3. Use whiteboard softwares

Digital platforms, such as Mural, Miro, and FigJam offer collaborative spaces where team members can gather, organize, and visualize qualitative data in real-time. Unlike traditional whiteboards, these digital tools provide enhanced flexibility, allowing for effortless rearrangement of ideas, color coding, and annotation features. Moreover, whiteboard tools facilitate remote collaboration, simplifying participation in affinity mapping sessions for distributed teams.

4. Support themes with quotes, body language, and facial expressions

Incorporate direct quotes and nonverbal cues, like facial expressions captured during user interviews, to vividly illustrate and reinforce your themes. This not only enriches your analysis but also enhances its credibility.

5. Involve stakeholders in the process

Involve stakeholders in the affinity mapping sessions to ensure diverse perspectives are considered, thereby mitigating the subjective nature of interpreting qualitative data. Given that individuals may interpret qualitative data differently, engaging stakeholders can lead to more comprehensive insights.

6. Prioritize themes relevant to research questions

Utilize a deductive approach, honing in on themes that precisely align with the research objectives and questions. While unexpected insights are valuable, it's vital to prioritize addressing the core research inquiries. By emphasizing pertinent themes, your analysis will maintain a smooth, actionable focus.

7. Connect themes into a cohesive narrative

Weave a seamless narrative that centers on the identified themes, creating a captivating tale of the user experience. Highlight the profound impact of individual pain points on users' workflows, demonstrating how proposed solutions effectively mitigate these challenges. By seamlessly connecting themes within a narrative framework, you can vividly illustrate the user journey and showcase the efficacy of your design solutions.

Analyzing qualitative data through thematic analysis and affinity mapping is pivotal for extracting meaningful insights and understanding complex phenomena. Thematic analysis allows researchers to identify patterns, recurring themes, and underlying meanings within the data, providing rich context and depth to their findings. On the other hand, affinity mapping facilitates the organization and synthesis of diverse perspectives and ideas, enabling researchers to uncover connections and relationships that may not be immediately apparent. Together, these methodologies offer a structured approach to distilling qualitative data, fostering a deeper comprehension of human experiences, behaviors, and perceptions. By systematically exploring and interpreting qualitative data through thematic analysis and affinity mapping, researchers can generate valuable insights that inform decision-making, drive innovation, and ultimately lead to more effective solutions and interventions in various fields of study.

Frequently Asked Questions

How do you choose themes in thematic analysis?

Themes in thematic analysis are chosen based on patterns and similarities found in the data. You may identify meaningful units of data, code them, and then group related codes into overarching themes. These themes should reflect the key ideas or concepts present in the data.

What are the benefits of using affinity mapping in user research?

Affinity mapping facilitates sensemaking by visually organizing and synthesizing qualitative data, making it easier to identify patterns, trends, and user needs. It promotes collaboration and consensus-building among team members and stakeholders, leading to more informed design decisions.

Can thematic analysis be used with quantitative data?

Thematic analysis is primarily designed for qualitative data, which includes textual data from interviews, focus groups, surveys, and observations. While some aspects of thematic analysis may overlap with quantitative data analysis, it is not typically used as the primary method for analyzing quantitative data.

How many codes should I use in my qualitative analysis?

There is no fixed rule for the number of codes to use in qualitative analysis, as it depends on the complexity of the data and the research objectives. However, it's generally recommended to use a manageable number of codes that capture the key concepts or themes within the data without becoming overly complex.

Jin Jeon

UX Researcher
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Jin is a UX researcher at Hubble that helps customers collect user research insights. Jin also helps the Hubble marketing team create content related to continuous discovery. Before Hubble, Jin worked at Microsoft as a UX researcher. He graduated with a B.S. in Psychology from U.C. Berkekley and an M.S in Human Computer Interaction from University of Washington.

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