What is qualitative data analysis?

• The process of interpreting and making sense of non-numerical data — text, images, audio, video — to understand concepts, experiences, and social phenomena
• The raw material is usually talk and text: interview transcripts, focus-group recordings, open-ended survey comments, field notes, documents
• Organisations generate this material constantly: exit interviews, customer complaints, appraisal comments, meeting minutes
• The task is to travel from raw talk to defensible interpretation — a claim about what the data mean that you could justify to a critic
• Data never speak for themselves: someone must decide what counts as a pattern, and that someone is you

Why analysis is the hard part

• Data overload — twelve one-hour interviews produce well over a hundred pages of transcript; richness becomes overwhelming without a system
• Subjectivity — the analyst’s own perspective shapes what they notice; two readers can honestly see different things in the same page
• Context dependence — the same sentence can mean different things depending on who said it, when, and in response to what
• No single algorithm — there is no formula that turns text into findings the way a regression turns numbers into coefficients
• The answer to all four problems is the same: explicit, systematic, documented procedure — the theme of this whole course, applied to words

The analysis spiral

• Qualitative analysis moves through recognisable stages — but as a spiral, not a straight line: you loop back constantly
  1. Familiarisation — read and re-read the material; note first impressions
  2. Coding — attach short labels to meaningful segments of the data
  3. Categorising — group related codes together
  4. Theming — identify the broader patterns the categories point to
  5. Interpreting — say what the patterns mean for the research question
• Each pass through the data refines the one before: a theme discovered late sends you back to re-code earlier transcripts
• This looping is not inefficiency — it is how the method achieves depth; the analysis is finished when new passes stop changing the picture

What is a code?

• A code is a word or short phrase that captures the essence of a segment of data — a label saying “this bit is about that”
• Codes are the bridge between raw data and analysis: they let you find, compare, and count every place where the same thing comes up
• A segment can be a phrase, a sentence, or a whole paragraph — and one segment can carry several codes at once
• Good codes are close enough to the data to be checkable, and abstract enough to apply across more than one transcript

Descriptive and interpretive codes

• Descriptive codes stay close to the surface of what was said — they summarise topic and content
  • mentions pay, talks about rota, praises supervisor
• Interpretive codes name what the analyst takes the passage to mean — they add a layer of inference
  • perceived pay inequity, loss of control over time, loyalty to person not firm
• Neither type is superior: descriptive codes are safer and easier to agree on; interpretive codes carry more analytical value but need more justification
• A common workflow: code descriptively first, then re-code interpretively once you know the data well — the spiral again

Open coding

• Open coding (a term inherited from grounded theory) means working through the data line by line, labelling freely, without a predetermined list
• Generate as many codes as the data demand — do not force segments into a scheme you brought with you
• Stay open to every theoretical direction: the point of open coding is discovery, letting the data surprise you
• Expect mess: early open coding produces dozens of overlapping labels — that is normal, and the next stage tidies it
• Compare constantly: each newly coded segment is checked against segments already carrying the same code — do they really belong together?

Building a codebook

• A codebook turns a private coding habit into a shared, checkable instrument — essential the moment more than one person codes
• Each entry needs four things:

• The codebook is a living document: codes are merged, split, and redefined as analysis proceeds — but every change is recorded and dated
• A codebook another analyst can apply without asking you questions is the qualitative equivalent of a reliable measurement instrument

One passage, two coders

• Both codings are defensible — they operate at different altitudes above the data
• Coder A’s labels are easier to agree on; Coder B’s carry more explanatory promise but need evidence from elsewhere in the data
• The disagreement is productive: discussing it forces the team to decide what the study is actually trying to capture — and to write the decision into the codebook

Thematic analysis: the workhorse

• Thematic analysis identifies, analyses, and reports patterns (themes) within qualitative data — the most widely used qualitative method in organisational research
• It examines the perspectives of different participants, highlighting commonalities and differences and generating unanticipated insights
• It captures collective or shared meanings: not what one person said, but what recurs across the dataset
• Flexible: works with interviews, focus groups, open survey comments, documents — any textual material
• Its outputs are exactly what a decision-maker needs: a small number of well-evidenced themes, each with a clear definition and supporting extracts

The six steps of thematic analysis

• The standard sequence (associated with Braun and Clarke) runs:
  1. Familiarisation — immerse yourself; read, re-read, note initial ideas
  2. Generating initial codes — systematically code the whole dataset, collating segments under each code
  3. Searching for themes — sort codes into candidate themes; gather all data relevant to each
  4. Reviewing themes — check each theme against the coded extracts and the full dataset; draw a thematic map
  5. Defining and naming themes — write a clear definition and a telling name for each; refine the overall story
  6. Producing the report — select vivid, compelling examples and relate the themes back to the research question
• Steps 3–5 loop: themes that fail review are split, merged, or abandoned — the spiral inside the method

What makes a good theme?

• A theme is not just a topic: “pay” is a topic; “pay grievances are really fairness grievances” is a theme — it claims something
• A good theme captures something important in relation to the research question — importance is not the same as frequency
  • Something said by few people can still be a theme if it is analytically central; something said by everyone can be trivial
• Themes must be grounded: every theme needs multiple supporting extracts, ideally from multiple participants
• Themes should be distinct but connected — minimal overlap between them, and together they tell a coherent overall story

Qualitative content analysis

• Content analysis systematically analyses the content of communication — documents, media coverage, open-ended answers — using an explicit category scheme
• The workflow is more structured than thematic analysis:
  1. Define the research question and select the material
  2. Develop categories and a coding scheme — often in advance, from theory
  3. Pre-test the scheme on a small sample; revise it
  4. Code the full sample with the fixed scheme
  5. Analyse — including counting: frequencies, co-occurrences, comparisons
• The key differences from thematic analysis: categories are typically fixed before full coding, and results are often quantified — content analysis sits closest to the quantitative border

Content analysis in action

• A classic study: how are women in leadership roles portrayed in the media?
• Sample: 100 newspaper articles — 50 discussing men in the workplace, 50 discussing women
• Coding categories fixed in advance: titles, competence descriptors, emotional descriptors, personal-life references, leadership language, appearance
• Selected findings:

• Conclusion: media descriptions of professionals reproduce gender stereotypes — competence and leadership for men; emotion, family, and appearance for women
• Note the form of the finding: counted categories, not interpreted themes — that is content analysis’s signature

Memo-writing

• A memo is a note the analyst writes to themselves during analysis — the running commentary of the project
• Memos capture:
  • Hunches — “pay complaints always seem to follow a comparison with a named competitor”
  • Decisions — “merged rota chaos and shift swaps into scheduling unpredictability, because…”
  • Emerging interpretations — first drafts of what a theme might mean
• Write memos from day one — the best analytical ideas arrive during coding and are lost if not captured immediately
• Memos are not overhead: they become the first draft of your findings and a key part of the audit trail

Quotations as evidence

• In the final report, quotations are your evidence — they show the reader the data behind each claim
• A good quotation is vivid (it lands) and representative (it stands for a pattern, not just itself)
• The great danger is cherry-picking: choosing the one dramatic quote that supports your story while ignoring the ninety pages that complicate it
• Defences against cherry-picking:
  • State how many participants the quoted view represents (“seven of twelve leavers…”)
  • Quote the counter-voices too — the participant who disagrees
  • Never let a single quotation carry a theme alone
• A quotation illustrates a finding; it is the pattern, honestly reported, that is the finding

Can qualitative analysis be rigorous?

• The standard objection: “this is just your opinion about what people said”
• The answer is not to imitate statistics but to make the interpretive process transparent, systematic, and checkable
• Four practical disciplines do most of the work:
  1. Intercoder comparison — do independent analysts see the same things?
  2. Negative-case analysis — have you hunted for the evidence against yourself?
  3. The audit trail — can an outsider trace how you got from data to claims?
  4. Description–interpretation discipline — is it always clear what participants said versus what you concluded?
• Together these turn “trust me” into “check me” — which is what rigour means in any method

Intercoder comparison

• Two (or more) analysts independently code the same material, then compare
• Where they agree, confidence rises; where they disagree, something valuable surfaces:
  • a vague codebook definition that needs tightening
  • a genuinely ambiguous passage worth discussing
  • a difference in interpretive stance the team must resolve
• The process: code separately → compare segment by segment → discuss every disagreement → revise the codebook → re-code
• In quantitative content analysis this is formalised as intercoder reliability statistics; in thematic work the structured discussion matters more than the number
• Disagreement is not failure — undiscussed disagreement is

Negative-case analysis

• Once a theme starts to form, the temptation is to see it everywhere — confirmation bias works on coders too
• Negative-case analysis is the deliberate hunt for data that contradicts your emerging interpretation
• Ask of every theme: who in the dataset does this not fit — and why?
• Three possible outcomes, all good:
  • the exception leads you to refine the theme’s boundaries (“this holds for store staff, not warehouse staff”)
  • it reveals a rival explanation you must now address
  • the theme survives scrutiny — and your claim is stronger for the test

The audit trail

• The audit trail is the documented record of the whole analytical journey: raw data → codes → categories → themes → claims
• It contains:
  • all codebook versions, with dates and reasons for changes
  • the memos recording hunches, decisions, and dead ends
  • the mapping from each theme to its supporting extracts
• Purpose: an outsider could reconstruct and challenge every analytical decision — the qualitative counterpart of sharing your data and code
• It also protects you: six weeks later, “why did we merge those two codes?” has a written answer
• This is our course’s oldest theme again: systematic and transparent procedure is what separates research from impression

Description versus interpretation

• The final discipline: keep what the data say and what you conclude visibly separate
• Description: “Nine of twelve leavers compared their pay with a named competitor, unprompted.”
• Interpretation: “Pay dissatisfaction at Meridian is driven by external benchmarking rather than absolute pay levels.”
• Both belong in the report — but the reader must always be able to tell which is which
• Blurring them is how weak analyses smuggle opinions in as findings; separating them is how strong analyses invite scrutiny
• A practical habit: for every claim you write, ask “could a participant have said this?” — if yes, it is description; if only the analyst could say it, it is interpretation and needs its evidence shown

Software helps, thinking decides

• Dedicated packages — MAXQDA, NVivo, Atlas.ti — support qualitative analysis at scale
• What they do well: store and organise transcripts, attach codes, retrieve every segment under a code instantly, visualise code co-occurrence, keep the audit trail tidy
• What they do not do: decide what a code means, judge whether a theme is grounded, or interpret anything
• The software manages the clerical burden; every analytical decision remains human
• A useful rule: if your analysis would be weak on paper, it will be weak in NVivo — only faster

Conclusion

• Qualitative analysis is the disciplined journey from raw talk to defensible interpretation — a spiral of familiarising, coding, categorising, theming, and interpreting
• Codes are the working unit: descriptive or interpretive, generated openly, governed by a shared codebook
• Thematic analysis is the workhorse — six steps from immersion to report; content analysis is its more structured, countable cousin
• Quotations are evidence, not decoration — vivid, representative, and honest about prevalence
• Rigour is earned through intercoder comparison, negative cases, the audit trail, and the description–interpretation discipline — turning “trust me” into “check me”
• Software carries the files; you carry the thinking
• Questions and discussion are welcome

Exercise

Study guide