IB Psychology HLTopic 5 — Data AnalysisPaper 3 & IAHL only~10 min read
Thematic Analysis of Qualitative Data
Not all data are numbers. Interview transcripts, diaries, letters, social media posts and newspaper archives all contain psychological information, and thematic analysis is the method for pulling meaning out of them systematically rather than just reading and forming an impression.
📚 What you need to know
Thematic analysis identifies patterns of meaning — themes — across a set of qualitative data.
It works on transcripts, diaries, letters, texts, film scripts, adverts and other written or spoken material.
The process runs raw data → codes → sub-themes → themes.
It is usually inductive: themes emerge from the data rather than being decided in advance.
It can be used with primary data you collected or secondary data that already existed.
Strengths: rich meaning, good external validity, and ethical access to public-domain material.
Limitations: interpretation is subjective, so researcher bias and reflexivity matter enormously.
From transcript to theme
The process narrows steadily. You start with everything anybody said, attach short labels to meaningful chunks, group similar labels together, and end with a handful of big ideas that capture what the data are really about.
The final layer is deliberately small. Three or four well-supported themes say far more than a dozen thin ones.
Coding, in practice
Coding means reading through the data and attaching a short descriptive label to each meaningful chunk. If a participant says they stopped telling their friends how they felt because they did not want to be a burden, you might code that as “hiding distress” and “fear of burdening others”. The same code will reappear across different participants, and that repetition is what eventually builds a theme.
Because the analysis is normally inductive, you do not decide the codes in advance. They come out of the material itself. That is a strength, because you find things you did not expect — and a risk, because what you notice depends partly on who you are, which is exactly why reflexivity belongs in this method.
Two directions of analysisInductive: the data come first, and the themes emerge. Deductive: the theory comes first, and you look for it in the data.
Primary and secondary data
Primary data
Secondary data
What it is
Collected by the researcher for this study
Already existed, produced for some other purpose
Examples
Interview transcripts, focus group recordings, open questionnaire responses
Diaries, letters, newspaper archives, film scripts, adverts, public social media posts
Advantage
Targeted precisely at the research question
Fast to obtain, and free of demand characteristics
Drawback
Time-consuming to collect, and participants may manage their self-presentation
Not designed to answer your question, so may be incomplete
There is a neat ethical point here. Material already in the public domain — a published memoir, a newspaper archive — can be analysed without seeking informed consent, because nobody is being asked to do anything. That makes secondary data an unusually accessible route into sensitive topics.
Judging the method
Strengths
Limitations
Produces rich, detailed data about meaning that numbers cannot capture
Interpretation is subjective, so two researchers may reach different themes
Good external validity, since the material often comes from real life rather than a lab
Vulnerable to researcher bias, especially confirmation bias during coding
Public-domain secondary data can be analysed without informed consent
Reflexivity is not always practised, and without it the bias goes unexamined
Flexible — works across interviews, documents, media and archives
Time-consuming and repetitive; transcripts must be read many times over
Can generate new theory rather than only testing existing ideas
Used less often than quantitative methods, so there are fewer studies to compare against
The subjectivity criticism has a standard answer, and it is worth having ready: researcher triangulation and a documented reflexive journal. Neither removes subjectivity, but together they make it visible and checkable, which is what credibility requires.
🧩 Running a thematic analysis
Transcribe and read. Go through the whole data set at least twice before coding anything.
Code systematically. Attach short labels to every meaningful chunk, across all the data.
Group codes into sub-themes where they clearly belong together.
Build themes from the sub-themes, and check each one against the original data.
Review and refine. Merge themes that overlap, and discard any without enough support.
Report with evidence. Name each theme and back it with quoted extracts, plus a reflexive account of your own position.
Worked examples
WORKED EXAMPLE
Explain how a study would be carried out
A researcher wants to investigate how mental illness is portrayed in national newspapers. Explain how thematic analysis could be used, and state one ethical advantage of this design.
Step 1: Identify the data
Newspaper articles are secondary data already in the public domain.
Step 2: Describe the coding
Read the articles repeatedly and attach codes to meaningful chunks, for example “danger to others”, “personal tragedy framing” or “recovery narrative”.
Step 3: Build the themes
Group related codes into sub-themes, then into a small number of overarching themes, checking each against the original articles.
Step 4: State the ethical advantage
Because the material is already public, informed consent is not required and no participant is exposed to any risk.
Step 5: Add a credibility measure
A second researcher codes a sample of articles independently, providing researcher triangulation.
Inductive coding of public secondary data; no consent needed“explain how” questions want the procedure in order, not just the name of the method
WORKED EXAMPLE
Evaluate the method
A student argues that thematic analysis is unscientific because “the researcher just decides what the themes are”. Evaluate this claim.
Step 1: Concede the valid part
Interpretation genuinely is subjective, and confirmation bias can shape which codes a researcher notices.
Step 2: Correct the overstatement
The process is systematic: every piece of data is coded, themes must be supported by extracts, and the procedure is reported in full.
Step 3: Name the safeguardsResearcher triangulation and reflexivity make the researcher’s influence visible and checkable.
Step 4: Reframe the standard
Qualitative research is judged by credibility and transferability, not by reliability. It answers a different question from quantitative work.
Step 5: Give a balanced conclusion
The criticism identifies a real vulnerability but applies the wrong standard. Subjectivity is managed and reported, not ignored.
Partly fair, but it judges the method by criteria it was never designed to meetconceding the strong part of a criticism before answering it reads as far more confident
💡 Exam tip
Give the stages in order: data, codes, sub-themes, themes. That sequence is the backbone of any answer here.
Say inductive and explain what it means. It is the word that distinguishes this from testing a preset framework.
Use the consent point for public secondary data. It is a strong, specific ethical strength.
Pair every subjectivity criticism with triangulation and reflexivity as the response.
Judge it by credibility and transferability, never by reliability and generalisability.
Support themes with extracts. “Evidence from the data” is what makes a theme defensible.
⚠ Common mix-up
Confusing themes with word counts. Counting how often a word appears is content analysis, not thematic analysis.
Treating codes and themes as the same thing. Codes are small labels; themes are patterns built from many codes.
Assuming inductive means unsystematic. The themes emerge from the data, but the process is highly structured.
Thinking all secondary data is consent-free. Only genuinely public material is, and privacy still has to be respected.
Producing too many themes. A long list usually means the sub-themes were never properly grouped.
Reporting themes without extracts. Unsupported themes are just the researcher’s opinion.
That is the whole of Topic 5. Look back over it as one argument rather than nineteen separate pages: a method is chosen, a design is built to protect validity, a sample is selected, data are collected, and then statistics decide whether any of it means anything. Every page you have read is one link in that chain, and exam questions almost always ask you to connect two of them.
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