Qualitative Data Analysis Approaches: A Practical Comparison
One of the most common difficulties in qualitative research is deciding how to analyse the data once it has been collected. Terms such as thematic analysis, grounded theory, discourse analysis, and narrative analysis are often used interchangeably, despite representing very different analytical traditions. This post clarifies the main qualitative data analysis approaches, compares their core features, and explains when each approach is most—and least—appropriate.
1. Thematic Analysis (TA)
What it is:
A flexible approach focused on identifying, analysing, and interpreting patterns of meaning (themes) across qualitative data.
Key characteristics:
- Can be inductive (data-driven) or deductive (theory-driven)
- Not tied to a specific epistemology
- Widely used across disciplines
Typical data:
Interviews, focus groups, open-ended survey responses, documents
Why it’s popular:
Accessible to beginners, adaptable, and suitable for many research questions
Key source: Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa (seminal and widely cited overview of the method)
2. Content Analysis
What it is:
A systematic approach to analysing text by categorising content, often with an emphasis on frequency or presence of concepts.
Key characteristics:
- Can be qualitative, quantitative, or mixed
- Often more structured than thematic analysis
- Uses predefined or emergent coding categories
Variants:
- Conventional (inductive)
- Directed (theory-driven)
- Summative (counts + interpretation)
Typical data:
Policy documents, media texts, interview transcripts
Key source: Erlingsson, C., & Brysiewicz, P. (2017). A hands-on guide to doing content analysis. African Journal of Emergency Medicine, 7(3), 93–99. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6234169/ (practical, highly cited guide for qualitative content analysis)
3. Grounded Theory
What it is:
A method aimed at generating theory directly from data through iterative data collection and analysis.
Key characteristics:
- Strongly inductive
- Constant comparative method
- Analysis begins as soon as data collection starts
Typical data:
In-depth interviews, observations
Key output:
A conceptual or theoretical model explaining a process or phenomenon
Key source: Glaser, B. G., & Strauss, A. L. (1967). The discovery of grounded theory: Strategies for qualitative research. Aldine. (The foundational text defining grounded theory methodology)
(Classic reference if you want to cite a book edition)
4. Interpretative Phenomenological Analysis (IPA)
What it is:
An approach focused on understanding how individuals make sense of their lived experiences.
Key characteristics:
- Idiographic (small, homogeneous samples)
- Strong interpretive element
- Researcher plays an active role in meaning-making
Typical data:
In-depth, semi-structured interviews
Common fields:
Psychology, health sciences, education
Brocki, J. M., & Wearden, A. J. (2006). A critical evaluation of the use of interpretative phenomenological analysis (IPA) in health psychology. Psychology and Health, 21(1), 87–108. https://eprints.hud.ac.uk/id/eprint/10368/ (widely referenced evaluation and application of IPA)
5. Narrative Analysis
What it is:
An approach that examines stories—how people construct meaning through narratives over time.
Key characteristics:
- Focuses on structure, content, and context of stories
- Emphasises temporality and identity
- Less about themes across cases, more about individual accounts
Typical data:
Life histories, autobiographical interviews, diaries
Key source: Holley, K. A., & Smyth, N. J. (2009). Narrative and the construction of qualitative research. Journal of Research Practice, 5(1), Article D1. (Discusses narrative analysis as a qualitative method)
6. Discourse Analysis
What it is:
An approach that analyses how language is used to construct meaning, power, identities, and social realities.
Key characteristics:
- Language is not neutral
- Strong theoretical grounding
- Multiple traditions (e.g. critical, Foucauldian, conversation analysis)
Typical data:
Interviews, policy texts, media, institutional documents
Key Source: Tsindos, T. (2023). Discourse analysis (Chapter 23). In Qualitative Research Methods. OER Collective. https://oercollective.caul.edu.au/qualitative-research/chapter/23/ (academic overview of discourse analysis and its aims)
7. Framework Analysis
What it is:
A structured matrix-based approach developed for applied and policy research.
Key characteristics:
- Combines deductive and inductive analysis
- Transparent and auditable
- Works well in team-based research
Typical data:
Interviews and focus groups linked to predefined objectives
Key source: Ritchie, J., Spencer, L., & O’Connor, W. (2003). Carrying out qualitative analysis. In J. Ritchie & J. Lewis (Eds.), Qualitative Research Practice: A Guide for Social Science Students and Researchers (pp. 219–262). Sage. (Introduces framework analysis as a structured method) (note: foundational chapter, typical reference text)
8. Ethnographic Analysis
What it is:
An approach embedded in ethnography, focusing on understanding culture, practices, and social interactions in context.
Key characteristics:
- Long-term engagement with the field
- Iterative and reflexive
- Combines observation, interviews, and artefacts
Typical data:
Field notes, observations, interviews, visual data
Key Source: Reeves, S., Kuper, A., & Hodges, B. D. (2008). Qualitative research: Ethnography. BMJ, 337, a1020. https://doi.org/10.1136/bmj.39506.688162.BE (classic academic overview of ethnographic research and analysis)
Qualitative Data Analysis Approaches: Comparison Summary
The following table provides a concise comparison of key qualitative data analysis approaches. It is designed as a quick-reference guide, allowing researchers to see at a glance how different approaches vary in purpose, structure, and typical use cases.
🧠 Theory
👤 Experience
🛠️ Applied
| Approach | Focus | Best Used When | Avoid When | Structure | Level | Orientation |
|---|---|---|---|---|---|---|
| Thematic Analysis | Patterns across data | Flexible analysis of meanings | Theory building or language focus needed | Low–Moderate | Beginner → Inter. | 👤 🛠️ |
| Content Analysis | Categories & trends | Comparing topics across texts | Deep interpretation is central | Moderate–High | Beginner → Inter. | 🛠️ |
| Grounded Theory | Theory development | Explaining processes | Time or flexibility is limited | High | Advanced | 🧠 |
| IPA | Lived experience | In-depth sense-making | Large samples required | Moderate | Inter. → Adv. | 👤 |
| Narrative Analysis | Stories over time | Identity or life stories | Cross-case comparison | Low | Advanced | 👤 |
| Discourse Analysis | Language & power | Social construction focus | Applied or experiential aims | High | Advanced | 🧠 |
| Framework Analysis | Applied themes | Policy or evaluation work | Generating new theory | Very High | Beginner → Inter. | 🛠️ |
| Ethnographic Analysis | Culture in context | Studying practices in situ | Immersion not feasible | Low–Moderate | Advanced | 👤 🛠️ |
Each qualitative data analysis approach reflects different assumptions about knowledge, meaning, and the role of the researcher. Rather than asking which approach is “best,” researchers should consider which approach best aligns with their research questions, data, epistemological stance, and practical constraints.
The following posts explore each approach in detail, outlining their analytical steps, tools, and common pitfalls.
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