The Guide to Thematic Analysis

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Want to know all about thematic analysis? Read this guide to get a foundational understanding of thematic analysis and its contribution to qualitative research.
Jörg Hecker
Neringa Kalpokas
Director, Training & Partnership Development
  1. What is Thematic Analysis?
  2. Advantages of Thematic Analysis
  3. Disadvantages of Thematic Analysis
  4. Thematic Analysis Examples
  5. How to Do Thematic Analysis
    1. Introduction
    2. What are the steps in thematic analysis?
    3. Inductive thematic analysis
    4. Deductive thematic analysis
    5. Reflexive thematic analysis
  6. Thematic Coding
  7. Collaborative Thematic Analysis
  8. Thematic Analysis Software
  9. Thematic Analysis in Mixed Methods Approach
  10. Abductive Thematic Analysis
  11. Deductive Thematic Analysis
  12. Inductive Thematic Analysis
  13. Reflexive Thematic Analysis
  14. Thematic Analysis in Observations
  15. Thematic Analysis in Surveys
  16. Thematic Analysis for Interviews
  17. Thematic Analysis for Focus Groups
  18. Thematic Analysis for Case Studies
  19. Thematic Analysis of Secondary Data
  20. Thematic Analysis Literature Review
  21. Thematic Analysis vs. Phenomenology
  22. Thematic vs. Content Analysis
  23. Thematic Analysis vs. Grounded Theory
  24. Thematic Analysis vs. Narrative Analysis
  25. Thematic Analysis vs. Discourse Analysis
  26. Thematic Analysis vs. Framework Analysis
  27. Thematic Analysis in Social Work
  28. Thematic Analysis in Psychology
  29. Thematic Analysis in Educational Research
  30. Thematic Analysis in UX Research
  31. How to Present Thematic Analysis Results
  32. Increasing Rigor in Thematic Analysis
  33. Peer Review in Thematic Analysis

How to Do Thematic Analysis

Thematic analysis is a widely used qualitative research method that allows qualitative researchers to identify, analyze, and report patterns (themes) within data. It offers a flexible and accessible approach to analyzing qualitative data, enabling researchers to uncover rich and detailed insights. The method involves the organization and interpretation of information to highlight important aspects, connections, and patterns that emerge from the data.

This introduction to thematic analysis provides a clear and concise overview of the basic process involved, as well as steps for conducting variations of the thematic analysis process.

Thematic analysis follows a mainly straightforward process to gathering insights from data.

What are the steps in thematic analysis?

Thematic analysis is a methodical process that involves several key steps to identify and analyze qualitative data to identify patterns or themes useful to the research question or objectives. Thematic analysis focuses on systematically breaking down the data so that researchers can interpret significant aspects of the content, providing insightful analysis relevant to their research questions.

This section delineates the fundamental steps in thematic analysis, offering a structured approach to understand and implement this research method.

Familiarization with the data

The initial step in thematic analysis is to become thoroughly acquainted with the data. This means immersively reading and re-reading the data, such as interview transcripts or survey responses, to gain a deep understanding of its breadth and depth.

During this phase, researchers should start noting in memos initial ideas, impressions, and potential patterns that merit further exploration.

Familiarization with the data is a key component of any qualitative analysis. Photo by Eliott Reyna.

Generating initial codes

After familiarizing themselves with the data, researchers proceed to generate initial codes. This involves systematically working through the data set and coding segments of text that are relevant to the research question.

Codes are concise labels that categorize important features of the data that may form the basis of emerging themes. This step requires meticulous attention to detail and an organized approach to segment and codify the data.

Searching for themes

Once the data have been coded, the next step is to collate codes into potential themes. This involves examining the codes and the data extracts associated with them to identify significant broader patterns that capture something important about the data in relation to the research question.

Themes are not always just clusters of codes; they represent a level of patterned response or meaning within the data set.

Reviewing themes

After identifying potential themes, it's crucial to review them. This step involves two levels of review: first, checking the themes against the coded extracts to ensure they form a coherent pattern, and second, ensuring each theme is distinct and meaningful in relation to the other identified themes.

During this phase, some themes might be split, combined, or discarded as the analysis refines and sharpens the thematic map of the data.

Defining and naming themes

Having reviewed the themes, the next task is to define and refine them, giving each one a clear and informative name. Defining themes involves articulating what each theme captures about the data and why it is significant in relation to the research question.

Naming a theme should succinctly convey the essence of what it represents, allowing readers to understand the core of what was found.

Producing the report

The final step in thematic analysis is to produce a coherent and compelling report. A typical thematic analysis report involves weaving together the thematic analysis with the research question and the literature, providing vivid data extracts to illustrate each theme, and interpreting the significance of the themes in the broader context of the research.

The report should tell a coherent story about the data, clearly linking the analysis to the research question and the existing literature.

Inductive thematic analysis

Inductive thematic analysis is a method where themes are developed directly from the data, without any predetermined framework or theory guiding what is significant. This approach prioritizes the emergence of themes from the data itself, allowing researchers to capture the depth and diversity of meanings present in their dataset.

It’s particularly suited for exploratory studies where the aim is to uncover valuable insights or understandings without the constraints of existing theories.

Coding with openness

In inductive thematic analysis, coding begins with an open and exploratory mindset. Researchers approach the data without preconceived categories, allowing codes to emerge organically from the content.

This initial coding phase is crucial for laying the foundation of the thematic framework. Each code is generated based on the intrinsic qualities of the data, ensuring that the qualitative analysis is grounded in the actual information provided by the participants.

Theme development from codes

The transition from codes to themes is a critical step in inductive thematic analysis. This process involves the aggregation of related codes into potential themes, paying close attention to how these codes collectively represent a broader pattern or idea in the data.

The aim is to identify themes that encapsulate significant aspects of the data, reflecting the core ideas expressed by participants. This stage requires a careful balance between being true to the data and making interpretative leaps that contribute to a deeper understanding of the research question.

Finalizing themes through iteration

The finalization of themes in inductive thematic analysis is an iterative process. It involves revisiting the coded data and the emerging themes multiple times, refining and redefining themes as needed. This may include merging overlapping themes, subdividing broad themes into more precise sub-themes, or discarding themes that lack sufficient evidence in the data.

Through this process, researchers ensure that the final set of themes accurately and comprehensively reflects the data, capturing the richness and complexity of the studied phenomenon.

Identifying themes is an iterative process. Photo by Ivan Pergasi.

Deductive thematic analysis

Deductive thematic analysis operates from a different starting point compared to its inductive counterpart, being guided by predetermined codes or themes that stem from existing theories or the researcher's conceptual framework. This method is particularly valuable when the research aims to verify theoretical propositions or explore specific aspects of the data in relation to a pre-established theoretical framework.

In deductive thematic analysis, the researcher approaches the data with a set of expectations about what they will search for, which influences the coding process and the identification of themes.

Applying theoretical frameworks to coding

In a deductive approach, the initial coding process is shaped by the researcher’s theoretical framework. Codes are defined before engaging with the data, based on concepts derived from theory or previous studies.

As the researcher works through the data, they apply these pre-defined codes, looking for instances that match the conceptual categories. This approach ensures that the analysis remains focused on the theoretical interests guiding the study.

Refining themes from theoretical insights

Once initial coding is complete, the process of refining themes involves examining how the data aligns with or diverges from the theoretical framework. This may involve adjusting the predefined codes or themes to better fit the data while maintaining the theoretical orientation of the analysis.

The aim is to develop themes that not only arise from the data but also reflect the researcher's theoretical perspective, enhancing the dialogue between data and theory.

Integrating data with theory

The final step in deductive thematic analysis is integrating the themes with the theoretical framework to enrich understanding and provide insights into the research question. This involves a careful consideration of how the identified themes support, challenge, or extend theoretical propositions.

The researcher must articulate the relationship between the data and theory, demonstrating how the thematic analysis contributes a deeper understanding of the theoretical constructs under investigation. Through this integration, deductive thematic analysis offers a structured way to apply theoretical perspectives to qualitative data, fostering a nuanced exploration of the research topic.

Deductive thematic analysis requires connecting themes to existing theory. Photo by Thomas Couillard.

Reflexive thematic analysis

Reflexive thematic analysis emphasizes the active and interpretive role of the researcher in analyzing data, focusing on how their reflections, assumptions, and interactions with the data shape the analysis process. This approach acknowledges that data interpretation is inherently subjective and influenced by the researcher's perspectives.

Reflexive thematic analysis encourages an ongoing dialogue between the researcher and the data, facilitating a deeper understanding that is co-constructed rather than discovered. It is well-suited for studies aiming to capture complex, nuanced insights into participants' experiences and perceptions.

Engaging with reflexivity

The core of reflexive thematic analysis lies in the engagement with reflexivity throughout the analytical process. Researchers are encouraged to reflect on their preconceptions, motivations, and the impact of their background on the analysis.

This involves acknowledging how personal experiences, theoretical leanings, and the research context influence the interpretation of data. Reflexivity is not a one-time activity but a continuous process that occurs at every stage of analysis, from coding to theme development and reporting.

Iterative analysis and theme development

In reflexive thematic analysis, theme development is an iterative and dynamic process. Researchers move back and forth between the dataset and emerging themes, allowing for a fluid interaction that can lead to the evolution of themes over time.

This non-linear approach enables researchers to remain open to new insights and to reconsider their initial interpretations in light of deeper engagement with the data. The iterative nature of this process ensures that themes are deeply grounded in the data while also being shaped by the researcher's reflective insights.

Emphasizing the narrative

The final stage of reflexive thematic analysis involves crafting a coherent narrative that integrates the themes with the researchers' reflections and theoretical insights. This narrative is not just about reporting findings; it's about telling a story that reflects the complex interplay between the data, the researcher's interpretation, and the broader theoretical and contextual influences.

By weaving together the thematic analysis with reflective commentary, researchers provide a rich, nuanced account that highlights the subjective nature of qualitative research and the co-construction of knowledge. This approach fosters a deep, contextually informed understanding of the research topic, showcasing the value of reflexivity in thematic analysis.

Reflexivity involves understanding how the researcher closely examines their data. Photo by Marc-Olivier Jodoin.