How to analyse interview data for your dissertation UK

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Robert Clark

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Interview data analysis differs from survey analysis. You're not analysing numbers. You're analysing words, meanings, and narratives. The process is interpretive. Your understanding develops gradually as you engage deeply with data.

The satisfaction that comes from completing your dissertation is directly proportional to the effort you put into it, and students who engage fully with the process almost always feel a strong sense of accomplishment at the end.

Your introduction sets the tone for everything that follows. If it's unclear what you're arguing and why it matters, your reader will struggle to follow your logic even if it's perfectly sound. We've seen introductions that buried the research question on page four and introductions that were so broad they didn't commit to any particular argument. We'll help you craft an opening that's clear, purposeful, and sets up everything that comes after.

Interview analysis involves immersion, coding, and interpretation. You read interviews repeatedly. You notice patterns. You develop understanding. This journey is central to qualitative research. Don't rush it.

#### Preparing Interview Data

First, transcribe interviews word-for-word. Or outsource transcription. Either way, you must then check transcripts against recordings. Errors slip in. Correct them. Complete, accurate transcripts prevent misinterpretation.

Some researchers include nonverbal cues in transcripts. [laughs], [pauses], [angry tone]. These cues matter. They provide context. They reveal emotion and meaning beyond words alone. Include them if analysing nonverbal aspects.

Decide on anonymisation. Replace names with codes (P1, P2) or pseudonyms. Keep a key linking participants to codes. This protects confidentiality while letting you reference individual perspectives.

#### Approaches to Interview Analysis

Thematic analysis identifies recurring themes. You read transcripts. You code passages. You group codes into themes. You write about each theme. This approach is popular, practical, and accessible.

Narrative analysis examines how participants tell their stories. What narrative structure do they use? How do they position themselves? Narrative analysis is deep but time-consuming.

Interpretive phenomenological analysis (IPA) explores lived experience. What's it like to be this person in this situation? IPA is rigorous but appropriate for small samples (5-15 interviews).

Contrary to what many students believe, critical thinking requires more patience than what you might first assume. You'll notice the impact when you read back your draft, and your supervisor can help you identify where things need tightening. Starting with this approach prevents common structural problems.

Grounded theory builds theory from data. You code. You identify conceptual categories. You develop relationships between categories. You generate theory grounded in data. Grounded theory is rigorous, thorough, and demanding.

Your choice depends on research questions. "What are experiences like?" suggests IPA or phenomenology. "What themes emerge?" suggests thematic analysis. "What theory explains this?" suggests grounded theory.

#### Coding Interview Data

Read interviews completely before coding. Understand each person's perspective whole-person. Then code systematically. Multiple passes develop sophistication.

Initial coding (first pass) identifies interesting passages. No need for consistency. Get data down. What catches attention? Code it.

Focused coding (second pass) refines initial codes. Merge similar codes. Discard unused codes. Develop code definitions. Create hierarchy if helpful.

Theoretical coding (later passes) connects codes. How do codes relate? What patterns emerge? Theory develops through connection.

Write memos throughout. After each interview, summarise themes. After coding several interviews, reflect on patterns. These reflective notes guide your analysis. Include them as appendices if helpful.

#### Identifying Themes

Themes are patterns across interviews. Multiple participants mentioning similar ideas suggest themes. One participant mentioning something once might not be a theme. Multiple participants is stronger evidence.

Your examiner is looking for evidence of original thought, which does not mean you have to discover something entirely new but rather that you have engaged with your sources and data in a way that reflects independent thinking.

But frequency isn't everything. A single powerful quote illustrating important concept might be thematic. Trust your judgement. Themes should be meaningful and interpretable.

Check theme coherence. A theme should be internally consistent. If you're grouping code A, B, and C as one theme, they should fit together meaningfully. If they don't quite fit, split them.

#### Analysing Within and Across Interviews

Within-interview analysis examines each interview deeply. How does this person make sense of experience? What's their narrative arc? What values emerge? This individual perspective matters.

Across-interview analysis compares perspectives. Do all participants share experiences? Do some differ? Where do perspectives align and diverge? Comparison reveals patterns.

Balance both. Individual richness (within) and broader patterns (across) together create thorough understanding.

One of the advantages of starting your writing early is that it gives you the chance to discover gaps in your knowledge while you still have time to fill them through additional reading or further data collection.

#### Interpretation and Writing Up

Interpretation is analytical work. You're making sense. You're generating meaning. You're connecting data to theory. Don't just summarise interviews. Analyse them.

Write thematically. Dedicate sections to major themes. Use quotes supporting each theme. Explain what quotes show. Connect to literature. Show how your findings relate to existing knowledge.

Include participant diversity. If most participants are white and middle-class, note this. Limitations matter. Your analysis applies most clearly to similar populations.

Make your analytical process visible. How did you arrive at these themes? Readers seeing your process trust your conclusions. Transparency strengthens qualitative research.

#### Integration with Your Dissertation

The intellectual growth that comes from completing a dissertation often surprises students, who discover that the sustained effort of research and writing has changed the way they read, think, and engage with ideas in their field.

Dissertation Homework supports students analysing interview data rigorously. Your qualitative analysis should feel principled and transparent. Readers should understand your analytical journey.

Universities like University of Manchester, University of Cambridge, Imperial College London, Durham University, and London School of Economics teach qualitative methods. Your supervisor guides your analytical approach. They review your coding. They check your interpretation. Engage with your supervisor throughout analysis.

FAQ

Q1: How many interviews should I conduct for my dissertation?

Qualitative research doesn't use sample size calculations. Aim for saturation (new interviews add no new information). This typically occurs after 10-20 interviews for interviews lasting 30-60 minutes. Longer interviews (90+ minutes) might reach saturation sooner. Shorter interviews might need more. Your interview focus affects saturation. Exploring one narrow topic might saturate at 8 interviews. Exploring multiple complex topics might need 25. Your supervisor guides this. Start interviewing. Reflect after each interview. Stop when saturation appears. Interview slightly longer to confirm.

Q2: Should I analyse interviews as I conduct them, or wait until all interviews are complete?

Analyse as you go. After each interview, listen to recording, check transcript accuracy, write preliminary notes. Early analysis guides later interviews. It highlights emerging themes. It shapes follow-up questions. It makes analysis more efficient. Complete early analysis. Then conduct remaining interviews. Then do thorough analysis. This iterative approach is stronger than waiting.

Q3: What should I do if I've a very long interview transcript?

Long interviews are rich but analysable. Some are 15,000+ words. Break into sections. Analyse section by section. Or code the entire interview, then group codes into analysable chunks. Use software like ATLAS.ti. It handles long documents easily. Read carefully. Long interviews reward close reading. There's more material. More nuance. More richness. Embrace this.

Q4: How do I handle controversial or sensitive topics that emerge in interviews?

Treat sensitively. Listen without judgement. Secure data carefully. Use pseudonyms consistently. Don't quote directly if it might identify them. Analyse thoughtfully. Show respect for their experience. Your ethics approval guides you. Follow protocols. Interview data is often sensitive. Handle with care. Your supervisor helps work through difficult material.

Q5: Can I count code frequencies and report numbers in qualitative analysis?

Yes, but cautiously. If all participants mention theme X, that's noteworthy. "All 12 participants discussed barriers" is informative. But don't overstress frequencies. A theme mentioned by one participant might be profoundly important. Numbers suggest some democratisation of importance (all voices equal). But some voices might carry more weight. Use frequencies to give overview. Then focus on depth. Numbers and narrative work together.