Matching the method to the question
Qualitative analysis is not one technique. The right choice depends on what you want to know and how you understand knowledge itself.
- Reflexive thematic analysis (Braun and Clarke) suits questions about patterns of meaning across a data set and gives you flexibility over theory.
- Interpretative phenomenological analysis (IPA) focuses on how a small, fairly homogeneous group makes sense of a shared experience, case by case before looking across cases.
- Grounded theory aims to build a theory from data, using constant comparison and theoretical sampling.
- Framework analysis works well in applied health and policy research, where a matrix of cases by themes helps teams compare systematically.
- Content analysis can be qualitative or quantitative and is often used for documents or open-ended responses.
We help you pick one, explain why, and describe it accurately in your methodology chapter.
Sampling in qualitative research
Many qualitative projects run into trouble before analysis begins, because the sample does not fit the method. Random sampling is rare in qualitative work, since the aim is depth and relevance rather than statistical generalisation. Purposive sampling is the most common approach, choosing participants who have the experience you are studying. Snowball sampling helps reach hidden or hard-to-access groups, criterion sampling sets explicit inclusion rules, and theoretical sampling belongs to grounded theory, where later participants are chosen to develop emerging concepts.
Sample size is better justified through information power or the depth your method requires than through a blanket claim of saturation. IPA studies often involve three to ten participants; thematic analysis projects vary more widely. Our guide to sampling methods covers the quantitative side for comparison.
How a typical engagement runs
- Orientation. You send your research questions, interview guide and two or three transcripts. We discuss the approach and agree how you will code.
- Early coding review. You code a first batch, and we comment on code labels, level of abstraction and consistency.
- Software support. If you use NVivo or ATLAS.ti, we help you structure nodes or codes, cases and attributes so queries and matrices actually work.
- Theme development. We meet to test candidate themes: is each one a story about meaning, or just a heading for a topic? Is it supported across the data, and are contradictions acknowledged?
- Write-up feedback. We review your findings chapter for argument, balance of quotation and analysis, and links back to the literature.
Problems we see often
Themes named after interview questions are the most frequent issue: "Experiences of training" describes a topic rather than saying something about it. Over-coding is another, where hundreds of codes make patterns harder to see. We also help with quotes that carry the argument instead of supporting it, unclear reflexivity statements, and mixed-methods chapters where the two strands never actually meet. Another quiet problem is losing the link between a theme and the excerpts behind it, which makes it hard to show an examiner how you got there. For projects that also involve survey numbers, our statistics help can sit alongside this work.
Why you stay the analyst
In qualitative research, the researcher is part of the instrument. Examiners expect you to explain how your position shaped what you saw, and they can tell when the analysis was not done by the person defending it. That is why our support is built around coaching, review and challenge rather than doing the coding for you. Your transcripts are handled confidentially and never reused. Read more in our academic integrity statement, or request a quote describing your data and deadline.
What you get
- A recommendation on the analytic approach that fits your question and epistemology
- Feedback on your codebook and early coding, with worked examples from your own data
- Help setting up and organising an NVivo, ATLAS.ti, MAXQDA or Taguette project
- Theme review sessions that test each theme against the data set
- Comments on your findings chapter covering structure, quotation use and analytic depth
- Guidance on writing up rigour: reflexivity, audit trail and trustworthiness
What we don't do
- Coding your entire data set on your behalf for a thesis; interpretation is the core of qualitative work and must be yours
- Transcription of recordings, though we can suggest tools and conventions
- Promising a particular number of themes or a particular finding
How it works
Describe your project
Level, deadline and what you need help with.
Agree a written scope
What's included, the price and the timeline.
Work in stages
Check-ins so you can steer the work.
Review and ask
Explanations and follow-up questions included.