Overview
This lecture explains thematic analysis as a widely used, flexible qualitative method for identifying and interpreting patterns (themes) in psychological data, covering its process, advantages, disadvantages, and practical guidelines.
Introduction to Thematic Analysis
- Thematic analysis is a foundational method for qualitative analysis in psychology for identifying, analyzing, and reporting patterns within data.
- It is flexible and not tied to any specific theoretical framework or epistemological position.
- Thematic analysis is suitable for researchers new to qualitative methods due to its accessibility.
Relationship to Other Methods
- Unlike grounded theory or interpretative phenomenological analysis (IPA), thematic analysis is not theoretically bounded.
- It can be used in both essentialist/realist and constructionist paradigms.
- Thematic analysis is distinct from content analysis; it focuses on broader themes rather than micro-level frequency counts.
Decisions in Thematic Analysis
- A theme captures an important aspect of the data in relation to the research question and patterns of meaning.
- Theme prevalence can be determined in various ways and does not have fixed quantitative thresholds.
- Researchers can aim for a rich description of the whole data set or a detailed account of a particular aspect.
Approaches and Levels
- Inductive (data-driven) analysis allows themes to emerge from the data without pre-existing coding frameworks.
- Theoretical (deductive) analysis is analyst-driven, focusing on specific research questions or interests.
- Themes can be identified at a semantic (explicit) or latent (interpretative) level.
Six Phases of Thematic Analysis
- Phase 1: Familiarize yourself with the data through repeated reading and note-taking.
- Phase 2: Generate initial codes by systematically coding interesting features across the data set.
- Phase 3: Search for themes by collating codes into potential themes and sub-themes.
- Phase 4: Review themes, refining and ensuring coherence within and between themes.
- Phase 5: Define and name themes, identifying the essence of each and any sub-themes.
- Phase 6: Produce the report, ensuring a compelling analytic narrative supported by data extracts.
Common Pitfalls and Good Practice
- Avoid simply paraphrasing data or using data collection questions as themes.
- Ensure themes are coherent, distinct, and supported by sufficient data extracts.
- Make the researcher's theoretical stance and methodological steps explicit in the report.
Advantages and Disadvantages
- Advantages: Methodological flexibility, ease of learning, accessibility, usefulness for summarizing large data sets, and generating new insights.
- Disadvantages: Potential lack of interpretative depth if not anchored in theory, risk of weak or anecdotal analysis, and less kudos compared to branded qualitative methods.
Key Terms & Definitions
- Thematic analysis — A method for identifying and analyzing patterns or themes within qualitative data.
- Theme — A pattern that captures something important about the data in relation to the research question.
- Code — A label identifying a feature of the data relevant to the analysis.
- Data corpus — All data collected for a research project.
- Data extract — A specific chunk of coded data.
- Inductive approach — Generating themes from the data itself.
- Deductive (theoretical) approach — Coding guided by pre-existing research questions or theoretical interests.
- Semantic theme — A theme related to explicit data content.
- Latent theme — A theme interpreting underlying ideas or assumptions.
Action Items / Next Steps
- Practice the six phases of thematic analysis on a sample qualitative data set.
- Reflect on and explicitly state your theoretical stance when conducting thematic analysis.
- Review examples of published thematic analyses to understand reporting standards.