The ultimate guide to thematic analysis in qualitative research

What is thematic analysis?

Thematic analysis is a widely used method for analyzing qualitative data — information relating to opinions, thoughts, feelings, and other descriptive content. This systematic approach has gained significant popularity in social sciences research because it allows researchers to examine multiple qualitative sources and identify recurring patterns and themes across an entire data set.

The qualitative data analyzed might include interview transcripts, focus groups, articles, diaries, blog posts, academic research, web pages, social media content, and even audio and video files. These sources are analyzed collectively, with researchers identifying patterns that run throughout the complete corpus of data.

When conducting thematic analysis, researchers work with data relating to opinions, thoughts, feelings and other descriptive information. It’s become increasingly popular in social sciences research, as it allows researchers to look at a data set containing multiple qualitative sources and pull out the broad themes running through the entire data set.

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The thematic analysis process in 6 steps

While there are many types of thematic analysis, the thematic analysis process can be generalized into six steps. Thematic analysis involves initial analysis, coding data, identifying themes and reporting on the findings.

Source: Nielsen Norman Group

1. Familiarization

During the first stage of thematic analysis, the research teams or researchers become familiar with the dataset. This may involve reading and re-reading, and even transcribing the data. Researchers may note down initial thoughts about the potential themes they perceive in the data, which can be the starting point for assigning initial codes.

2. Coding

Codes in thematic analysis are the method researchers use to identify the ideas and topics in their data and refer to them quickly and easily. Codes can be assigned to snippets of text data or clips from videos and audio files. Depending on the type of thematic analysis used, this can be done with a systematic and rigorous approach, or in a more intuitive manner.

3. Identifying themes

Themes are the overarching ideas and subject areas within the corpus of research data. Researchers can identify themes by collating together the results of the coding process, generating themes that tie together the identified codes into groups according to their meaning or subject matter.

4. Reviewing themes

Once the themes have been defined, the researchers check back to see how well the themes support the coded data extracts. At this stage they may start to organize the themes into a map, or early theoretical framework.

5. Defining and naming themes

As researchers spend more time reviewing the themes, they begin to define them more precisely, giving them names. Themes are different from codes, because they capture patterns in the data rather than just topics, and they relate directly to the research question.

6. Writing up

At this stage, researchers begin to develop the final report, which offers a comprehensive summary of the codes and themes, extracts from the original data that illustrate the findings, and any other data relevant to the analysis. The final report may include a literature review citing other previous research and the observations that helped frame the research question. It can also suggest areas for future research the themes support, and which have come to light during the research process.

Another step which precedes all of these is data collection. Common to almost all forms of qualitative analysis, data collection means bringing together the materials that will be part of the data set, either by finding secondary data or generating first-party data through interviews, surveys and other qualitative methods.

The types of thematic analysis

Thematic analysis is not a monolithic approach but rather encompasses several distinct methodologies, each with its own philosophical underpinnings and practical techniques. Understanding these different approaches is crucial for researchers to select the most appropriate method for their research questions and theoretical frameworks.

The thematic analysis continuum

There are various thematic analysis approaches currently in use. For the most part, they can be viewed as a continuum between two different ideologies. Reflexive thematic analysis (RTA) sits at one end of the continuum of thematic analysis methods. At the other end is code reliability analysis.

Code reliability analysis

Code reliability analysis emphasizes the importance of the codes given to themes in the research data being as accurate as possible. It takes a technical or pragmatic view, and places value on codes being replicable between different researchers during the coding process. Codes are based on domain summaries, which often link back to the questions in a structured research interview.

Researchers using a code reliability approach may use a codebook. A codebook is a detailed list of codes and their definitions, with exclusions and examples of how the codes should be applied.

Reflexive thematic analysis (Braun & Clarke)

Reflexive thematic analysis was developed by Braun & Clarke in 2006 for use in the psychology field. In contrast to code reliability analysis, it isn’t concerned with consistent codes that are agreed between researchers. Instead, it acknowledges and finds value in each researcher’s interpretation of the thematic content and how it influences the coding process.

The codes assigned in reflexive thematic analysis are specific to the researcher and exist within a unique context comprised of:

1. The data set
2. The assumptions made during the setup of the analysis process
3. The researcher’s skills and resources

This doesn’t mean that reflexive thematic analysis should be unintelligible to anyone other than the researcher. It means that the researcher’s personal subjectivity and uniqueness is made part of the process, and is expected to have an influence on the findings. Reflexive thematic analysis is a flexible method, and initial codes may change during the process as the researcher’s understanding evolves.

Reflexive thematic analysis is an inductive approach to qualitative research. With an inductive approach, the final analysis is based entirely on the data set itself, rather than from any preconceived themes or structures from the research team.

Source: Delve

Other important thematic analysis approaches

Beyond the code reliability and reflexive approaches, several other significant methodologies deserve attention:

Boyatzis’ approach

Boyatzis developed a hybrid approach that combines elements of both data-driven (inductive) and theory-driven (deductive) thematic analysis. His method emphasizes codebook development and focuses on reliability and validity in the coding process while still acknowledging the interpretive nature of qualitative research.

Attride-Stirling’s thematic networks

This approach organizes themes into three levels: basic themes (lowest-order premises), organizing themes (categories of basic themes grouped together), and global themes (super-ordinate themes that encompass the principal metaphors in the data). This hierarchical structure helps researchers visualize the relationships between themes and understand their relative importance.

Template analysis (King)

Template analysis uses a coding template, which is developed and refined as the analysis progresses. It’s more structured than reflexive thematic analysis but more flexible than many content analysis approaches, allowing for hierarchical organization of themes and the integration of a priori themes based on theoretical perspectives.

Framework analysis

Particularly useful for applied policy research, framework analysis follows a systematic five-step process: familiarization, identifying a thematic framework, indexing, charting, and mapping and interpretation. It’s especially valuable for team-based research projects and when working with large datasets.

Inductive vs. deductive approaches

Thematic analysis can be conducted through either inductive or deductive approaches:

• Inductive analysis is data-driven, with themes emerging from the data without preconceived categories. Researchers approach the data with an open mind, allowing patterns to emerge organically.
• Deductive analysis is theory-driven, with themes predetermined based on existing theories, models, or concepts. The analysis seeks to test or refine these pre-existing frameworks within a new context.

Many researchers adopt a hybrid approach, combining elements of both inductive and deductive methods to leverage the strengths of each.

Thematic analysis vs. other qualitative research methods

Thematic analysis sits within a whole range of qualitative analysis methods which can be applied to social sciences, psychology and market research data.

Thematic analysis vs. content analysis

Both content analysis and thematic analysis use data coding and themes to find patterns in data. However, thematic analysis is always qualitative, but researchers agree there can be quantitative and qualitative content analysis, with numerical approaches to the frequency of codes in content analysis data.

Content analysis tends to be more systematic and often involves quantifying the occurrence of codes, whereas thematic analysis focuses more on the meaning and interpretation of patterns across the data.

Thematic analysis vs. discourse analysis

Unlike discourse analysis, which is a type of qualitative research that focuses on spoken or written conversational language, thematic analysis is much more broad in scope, covering many kinds of qualitative data.

Discourse analysis examines how language constructs social reality, focusing on power dynamics, rhetoric, and linguistic features. Thematic analysis, in contrast, is concerned with identifying patterns of meaning across datasets regardless of their linguistic properties.

Thematic analysis vs. narrative analysis

Narrative analysis works with stories — it aims to keep information in a narrative structure, rather than allowing it to be fragmented, and often to study the stories from participants’ lives. Thematic analysis can break narratives up as it allocates codes to different parts of a data source, meaning that the narrative context might be lost and even that researchers might miss nuanced data.

Thematic analysis vs. comparative analysis

Comparative analysis and thematic analysis are closely related, since they both look at relationships between multiple data sources. Comparative analysis is a form of qualitative research that works with a smaller number of data sources. It focuses on causal relationships between events and outcomes in different cases, rather than on defining themes.

Thematic analysis vs. grounded theory

Both thematic analysis and grounded theory involve coding and identifying patterns, but grounded theory aims to develop a theoretical framework grounded in the data, using theoretical sampling and constant comparison. Thematic analysis is typically less focused on theory generation and more concerned with rich description of patterns.

Thematic analysis vs. phenomenological analysis

Phenomenological analysis focuses specifically on understanding lived experiences from the perspective of participants. While thematic analysis can be used for this purpose, phenomenological analysis (like Interpretative Phenomenological Analysis or IPA) has specific procedures designed to capture the essence of experiences rather than just identifying patterns.

How to ensure trustworthiness in thematic analysis

Quality and rigor in qualitative research, including thematic analysis, are crucial for establishing the credibility and value of findings. Unlike quantitative research, which relies on statistical measures of validity and reliability, qualitative methods require different criteria for evaluating trustworthiness.

Key criteria for data trustworthiness

Researchers typically consider four main criteria when establishing the trustworthiness of thematic analysis:

1. Credibility — The confidence in the ‘truth’ of the findings
   • Implement member checking (participant validation)
   • Use data triangulation from multiple sources
   • Engage in peer debriefing with colleagues
   • Practice prolonged engagement with the data
2. Transferability — The applicability of findings to other contexts
   • Provide thick, rich descriptions of contexts and participants
   • Clearly document research processes and decision-making
   • Use purposive sampling strategies
3. Dependability — The consistency and repeatability of findings
   • Create a detailed audit trail of methodological decisions
   • Maintain organized records of data collection and analysis
   • Document changes in the research process
4. Confirmability — The degree of neutrality in findings
   • Practice reflexivity through research journals
   • Acknowledge researcher biases and assumptions
   • Demonstrate how findings emerge from the data

Practical strategies for enhancing rigor

To implement these criteria effectively, researchers can employ several practical strategies:

• Transparent Codebook Development: Create clear definitions of codes with examples and non-examples of their application.
• Multiple Coders: Having more than one researcher code the data independently can enhance the depth of analysis and identification of themes.
• Reflexive Journaling: Maintain detailed notes about methodological decisions, coding processes, and evolving interpretations.
• Data Saturation: Continue collecting data until no new themes emerge, indicating comprehensive coverage of the topic.
• Negative Case Analysis: Actively seek out data that contradicts emerging patterns to refine and strengthen the analysis.
• Clear Documentation: Provide detailed explanations of how themes were derived from codes and how interpretations connect to the raw data.

Common challenges in thematic analysis and how to overcome them

Despite its flexibility and widespread use, thematic analysis presents several challenges that researchers must navigate. Understanding these difficulties and their potential solutions can significantly improve the quality of analysis.

Coding inconsistencies

Challenge: Maintaining consistent coding across large datasets, especially when multiple researchers are involved.

Solutions:

• Develop a comprehensive codebook with clear definitions and examples
• Conduct regular coding comparison meetings
• Use inter-coder reliability checks and resolve discrepancies through discussion
• Implement a staged approach to coding, with preliminary testing on a subset of data

Managing large datasets

Challenge: Feeling overwhelmed by the volume of data and extracting meaningful patterns without losing important nuances.

Solutions:

• Use qualitative data analysis software (QDAS) to organize and manage data
• Develop a clear analysis plan before beginning coding
• Consider a phased approach, analyzing subsets of data sequentially
• Create data summary sheets for each data source to maintain overview

Theme development difficulties

Challenge: Distinguishing between codes and themes, and developing themes that accurately reflect patterns in the data.

Solutions:

• Remember that themes are broader than codes and represent patterns of meaning
• Use visual mapping techniques to explore relationships between codes
• Regularly review themes against the original data to ensure fidelity
• Seek peer feedback on theme development

Team-based analysis coordination

Challenge: Coordinating analysis across team members with different perspectives and interpretations.

Solutions:

• Establish clear protocols for communication and decision-making
• Schedule regular team meetings to discuss emerging findings
• Use collaborative coding platforms where possible
• Document disagreements and their resolution
• Leverage diverse perspectives as an analytical strength

Avoiding analytical bias

Challenge: Preventing researcher preconceptions from unduly influencing the analysis.

Solutions:

• Practice reflexivity through journaling
• Actively search for contradictory evidence
• Engage in peer debriefing
• Consider member checking with participants
• Maintain awareness of how theoretical frameworks may shape interpretations

Strengths and limitations of thematic analysis

Like any kind of qualitative analysis, thematic analysis has strengths and weaknesses. Whether it’s right for you and your research project will depend on your priorities and preferences.

Comprehensive strengths of thematic analysis

Easy to learn — Whether done manually or assisted by technology, the thematic analysis process is easy to understand and conduct, without the need for advanced statistical knowledge

Flexible — Thematic analysis allows qualitative researchers flexibility throughout the process, particularly if they opt for reflexive thematic analysis

Broadly applicable — Thematic analysis can be used to address a wide range of research questions.

Additional strengths include:

• Accessible to researchers and audiences: The findings from thematic analysis can be communicated clearly to diverse audiences, including those without specialized research knowledge.
• Compatible with various theoretical frameworks: Thematic analysis can be conducted within different epistemological positions (e.g., constructionist, realist, or contextualist approaches).
• Suitable for participatory research: The method can incorporate participant involvement in the analysis process, supporting collaborative and empowering research designs.
• Effective for pattern recognition: Thematic analysis excels at identifying patterns across large and diverse datasets, making it valuable for complex research questions.
• Adaptable to different data types: Can be applied to various forms of qualitative data, including interviews, focus groups, social media content, and visual data.

Limitations and challenges of thematic analysis

As well as the benefits, there are some disadvantages thematic analysis brings up.

Broad scope — In identifying patterns on a broad scale, researchers may become overwhelmed with the volume of potential themes, and miss outlier topics and more nuanced data that is important to the research question.

Themes or codes? — It can be difficult for novice researchers to feel confident about the difference between themes and codes

Language barriers — Thematic analysis relies on language-based codes that may be difficult to apply in multilingual data sets, especially if the researcher and / or research team only speaks one language.

Other important limitations include:

• Risk of decontextualization: When extracting codes and themes, there’s a risk of removing data from its context, potentially altering its meaning.
• Interpretive limitations: The quality of thematic analysis depends heavily on the researcher’s interpretive skills and theoretical sensitivity.
• Potential for superficial analysis: Without depth of engagement, thematic analysis can result in descriptive rather than interpretive findings.
• Challenges with temporal aspects: Thematic analysis may not adequately capture changes over time or process-related aspects of phenomena.
• Theoretical ambiguity: The flexibility of thematic analysis can sometimes lead to lack of coherence in the theoretical framework underlying the analysis.
• Reliability concerns: Particularly with reflexive approaches, different researchers may develop different themes from the same dataset.

Business applications of thematic analysis: From theory to practice

Thematic analysis, and other forms of qualitative research, are highly valuable to businesses who want to develop a deeper understanding of the people they serve, as well as the people they employ. Thematic analysis can help your business get to the ‘why’ behind the numerical information you get from quantitative research.

An easy way to think about the interplay between qualitative data and quantitative data is to consider product reviews. These typically include quantitative data in the form of scores (like ratings of up to 5 stars) plus the explanation of the score written in a customer’s own words. The word part is the qualitative data. The scores can tell you what is happening — lots of 3 star reviews indicate there’s some room for improvement for example — but you need the addition of the qualitative data, the review itself, to find out what’s going on.

Qualitative data is rich in information but hard to process manually. To do qualitative research at scale, you need methods like thematic analysis to get to the essence of what people think and feel without having to read and remember every single comment.

Qualitative analysis is one of the ways businesses are borrowing from the world of academic research, notably social sciences, statistical data analysis and psychology, to gain an advantage in their markets.

Implementing thematic analysis in business settings

For businesses looking to incorporate thematic analysis into their research toolkit, consider these practical steps:

1. Start with clear research questions that align with business objectives
2. Collect diverse data sources for a comprehensive understanding
3. Consider using mixed methods approaches to complement quantitative data
4. Involve cross-functional team members in reviewing themes
5. Translate findings into actionable recommendations
6. Measure the impact of changes implemented based on the analysis

Advanced techniques in thematic analysis

As qualitative research methodologies continue to evolve, thematic analysis has expanded to incorporate advanced techniques that enhance its application and effectiveness across diverse research contexts.

Analyzing visual and audio data

Traditional thematic analysis often focuses on textual data, but advanced applications extend to visual and audio content:

• Visual thematic analysis: Applies thematic approaches to photographs, videos, artwork, and other visual media, identifying patterns in visual representations
• Audio data analysis: Beyond transcription, this approach examines acoustic features like tone, pace, emphasis, and emotional qualities
• Multimodal analysis: Integrates textual, visual, and audio elements to develop a comprehensive understanding of complex communications

Cross-cultural considerations in thematic analysis

When conducting research across different cultural contexts, researchers must adapt their thematic approaches:

• Cultural sensitivity in coding: Develop codes that acknowledge cultural nuances and avoid imposing external frameworks
• Translation challenges: Address issues of conceptual equivalence when working with data in multiple languages
• Collaborative analysis: Involve researchers or participants from the cultures being studied to validate interpretations
• Contextual framing: Situate themes within their specific cultural, historical, and social contexts

Mixed Methods Integration

Combining thematic analysis with quantitative approaches offers powerful insights:

• Sequential designs: Use thematic analysis findings to inform quantitative instrument development, or use quantitative results to guide qualitative inquiry
• Concurrent triangulation: Apply both methodologies simultaneously to cross-validate findings
• Quantifying qualitative data: Transform qualitative themes into quantifiable variables for statistical analysis
• Visual data displays: Create joint displays that integrate qualitative themes with quantitative results

Longitudinal Thematic Analysis

Examining how themes evolve over time provides unique insights:

• Temporal mapping: Track the emergence, development, and resolution of themes across multiple time points
• Change process analysis: Identify patterns in how phenomena transform over time
• Repeated interviews: Analyze how individual narratives shift through different life stages or organizational phases

Tools and software for thematic analysis

Carrying out thematic analysis manually may be time-consuming and painstaking work, even with a large research team. Fortunately, machine learning and other technologies are now being applied to data analysis of all kinds, including thematic analysis, taking the manual work out of some of the more laborious thematic analysis steps.

The latest iterations of machine learning tools are able not only to analyze text data, but to perform efficient analysis of video and audio files, matching the qualitative coding and even helping build out the thematic map, while respecting the researcher’s theoretical commitments and research design.

Overview of available analysis tools

The landscape of qualitative data analysis software has evolved significantly, offering researchers powerful tools to streamline the thematic analysis process:

Traditional QDAS (Qualitative Data Analysis Software)

• NVivo: Offers comprehensive coding features, visualization tools, and supports various data formats
• ATLAS.ti: Provides powerful network views and relationship mapping capabilities
• MAXQDA: Features mixed methods functionality and intuitive coding systems
• Dedoose: Cloud-based platform with collaborative features and mixed methods capabilities
• QDA Miner: Includes text mining and statistical analysis features

AI-enhanced qualitative analysis tools

• Automated coding assistants: Machine learning tools that suggest potential codes based on text content
• Theme extraction algorithms: Systems that identify potential themes based on semantic patterns
• Sentiment analysis integration: Tools that incorporate emotional valence into thematic coding
• Natural language processing: Advanced linguistic analysis capabilities that complement human coding

Qualtrics® UX Research tools for thematic analysis

Qualtrics offers a comprehensive software solution specifically designed for identifying and analyzing themes in qualitative research. The User Experience Research solution leverages advanced AI capabilities to transform qualitative data analysis:

• Automated theme detection: Identifies recurring patterns and themes across diverse data sources
• Multi-format analysis: Processes text, audio, and video data through a unified platform
• Real-time insights: Generates immediate thematic summaries from ongoing research
• Collaborative analysis: Enables team-based coding and theme development
• Integrated mixed methods: Seamlessly combines qualitative themes with quantitative metrics
• Customizable visualization: Creates compelling visual representations of thematic relationships
• Scalable processing: Handles large datasets that would be impractical for manual analysis

Qualtrics’ solution stands out by maintaining the interpretive rigor of traditional thematic analysis while significantly reducing the time and effort required. This allows researchers to focus on interpretation and application rather than mechanical coding tasks.

Learn more about Qualtrics UX Research tools

The future of thematic analysis

Thematic analysis continues to evolve as a vital methodology in qualitative research, adapting to new data types, research contexts, and technological capabilities. As researchers increasingly navigate complex, multimodal datasets and cross-disciplinary questions, thematic analysis offers a flexible yet robust framework for extracting meaningful patterns and insights.

The integration of artificial intelligence and machine learning tools promises to enhance the efficiency and scale of thematic analysis without sacrificing the interpretive depth that makes this methodology so valuable. However, the fundamental principles of rigorous, reflective, and contextually sensitive analysis remain essential regardless of the tools employed.

Whether conducted in academic research, business intelligence, policy development, or product design, thematic analysis provides a bridge between raw qualitative data and meaningful insights that can inform theory and practice. By systematically identifying patterns of meaning across diverse data sources, researchers can illuminate the complex ways humans experience and make sense of their world.

For organizations seeking to understand their customers, employees, or markets more deeply, thematic analysis offers a powerful approach to uncovering the “why” behind behaviors, preferences, and experiences. When implemented with attention to quality and rigor, it transforms diverse qualitative data into actionable insights that drive innovation and improvement.