Thematic Analysis & IPA Lecture Notes

Thematic Analysis (TA) & Interpretative Phenomenological Analysis (IPA)

Introduction

• Interviews are a widely used tool for data collection in qualitative research.
• They are useful for exploring people's experiences, perspectives, knowledge, feelings, opinions, and views.
• Interview transcripts can be analyzed using various qualitative data analysis approaches.

Overview of Topics

• Introduction to Thematic Analysis (TA).
  • Procedure.
  • Advantages and disadvantages.
• Introduction to Interpretative Phenomenological Analysis (IPA).
  • Theoretical assumptions.
  • Design and procedure.
  • Strengths and weaknesses of IPA.
• Similarities & differences between TA and IPA.

Approaches to Qualitative Data Analysis

• Grounded Theory
• Content Analysis
• Discourse Analysis
• Conversational analysis
• Narrative Analysis
• Thematic Analysis
• Interpretative Phenomenological Analysis (IPA)
• And many others

Thematic Analysis

• Thematic Analysis is an umbrella term for a set of approaches focused on identifying themes (patterns of meaning) within data.
• It minimally organizes and describes data in rich detail (Braun & Clarke, 2006).
• Thematic analysis underpins many other forms of qualitative analysis.

What is a Theme?

• A theme is a pattern of meaning that captures something important about the material.
• It represents a shared implicit or underlying meaning.
• Emphasis is on meaning in relation to research questions, not necessarily prevalence.

Where do Themes Come From?

• Data-driven/Inductive: Coding and theme development are data-driven (bottom-up).
• Theory-driven/Deductive: Shaped by existing theoretical constructs, providing a 'lens' to code and develop themes (top-down).
• Most likely, it's a combination of both.

Six Phases of TA (Braun & Clarke, 2006)

1. Familiarization with the data.
2. Generating codes.
3. Searching for themes.
4. Reviewing potential themes.
5. Defining and naming themes.
6. Producing the report.
   • Note: Braun and Clarke now prefer the expression “generating” themes (Braun & Clarke, 2019; 2022).

(1) Familiarizing Oneself with the Data

• Start by reading all transcripts and taking initial notes.
• Read (read, read & read)!
• Consider questions such as:
  • What sort of assumptions are being made?
  • How are certain groups characterized?
  • What ideas are being drawn on?

(2) Initial Coding

• Two types of coding (Braun & Clarke, 2013, p. 206):
  • Selective coding – identify relevant material.
  • Complete coding – line by line.
• Codes = basic units of meaning.
• A piece of coded text varies from a few words to a multi-sentence chunk (Miles & Huberman, 1994).

Managing the Coding Process

• Use highlighters, pens, post-it notes, or the comments function in word processors.
• Make use of CAQDAS: Computer-Assisted Qualitative Data Analysis Software (e.g., Nvivo, Atlas.ti., MAXQDA).

(3) 'Searching'/Generating Themes

• The process of clustering together similar codes – which belong together?
• Organize codes into initial themes – a code can be promoted to a theme.
• Start to think about the relationship between themes – what is the overall story?

(4) Reviewing Themes

• Read all the collated extracts for each theme and consider whether they appear to form a coherent pattern.
• Decide:
  • Some candidate themes are not really themes (e.g., if there is not enough data to support them or the data are too diverse) – drop them!
  • Others might be merged into each other (e.g., two apparently separate themes might form one theme).
  • Other themes need to be split into separate themes.

Themes or Topic Summaries?

• In TA, themes are conceptualized as patterns of shared meaning underpinned by a central concept.
• Multi-faceted – perhaps cutting across several topics – and telling a story about the data.
• Different from topic summaries – buckets that collect together everything or the main things participants communicated about a topic – a shared topic.

(5) Defining and Naming Themes and (6) Writing Up

• Write a definition: a short description for each theme.
• Do not just paraphrase the extracts presented – identify what is interesting about them and why.
• Writing (interpretation, commentary) is an integral part of analysis, not something that occurs just at the end (as it does with statistical analyses).

Problems of Thematic Analysis

• 'Themes emerged' – themes don’t passively emerge from the data!
• Howitt (2013): at its worst, the analyst ‘sees’ five or six themes and then just looks for examples:
  • Implies that the themes are there without researcher input.
  • No justification or explanation was given for the themes.
  • No criteria – no effort.

Advantages of TA

• It can be used to address most types of qualitative research questions.
  • 'How is race constructed in workplace diversity training?'
  • 'What do people think of women who play traditionally male sports?'
• It can be used to analyse most types of qualitative data:
  • Interviews
  • Newspaper materials
  • Naturally occurring conversations
  • Websites
• It is not tied to a particular theoretical framework.
• TA is theoretically flexible!
• Techniques have many features in common with other qualitative methods (IPA, Grounded Theory).

Interpretative Phenomenological Analysis (IPA)

• A form of thematic analysis that makes a number of psychological assumptions.
• IPA: interviews are used for the study of experience (phenomenology).

(Lived) Experience of What?

• People’s 'life-worlds'.
• The state of affairs in which the world is lived and experienced (i.e., the subjective [not ‘behaviour’]).
• What matters to participants.

Assumptions About Knowledge in IPA

• 1st assumption: People interpret the world of phenomena (things).
  • What we, researchers, study, therefore, are their interpretations of their world.
• 2nd assumption: Researchers interpret the world, too.
  • When we study people’s sense-making, we bring our own sense-making to this enterprise.
  • Researchers interpret people’s interpretations.
  • Therefore, reflexivity (self- awareness in our activity as researchers) is built into IPA.

You Would Do IPA If…

• Idiographic vs nomothetic: You are interested in the particular case(s), not general statistical (average) tendencies in the population (the notional ‘average person’ – who doesn’t actually exist!).
• Meanings vs causal relations: You are interested in what things mean to people.
  • If you want to make claims about causal relations (e.g., the mechanisms in people’s heads that make them interpret in these ways) you should do experiments instead.
• Quality vs quantity: You are interested in the quality or types of experiences, not in measuring amounts or strength of experiences (or anything else).

Aims and Research Questions

• Experiences/events with major significance to the person.
• People’s experiences and/or understandings of particular phenomena.
• Perceptions and views of particular participants.
• Research questions are open and exploratory and tend to focus on the process rather than the outcome:
  • How does someone make sense of a major event/transition in their life?
  • How does someone make an important decision?

Data Collection and Samples in IPA

• Detailed examination of a particular case or a small number of cases:
  • Small samples (usually no more than 10; 6-8 as standard).
  • Typically, semi-structured interviews.
  • Diaries can be used too (NOT things like newspaper articles, etc.).

Sample Size for an IPA Interview Study

• Sample type is more important than size (since the interest in the shared experience of a given situation or thing).
• Hence homogeneous rather than representative sample (idiographic not nomothetic).

Analytical Stages (Smith et al., 2009)

1. Read through transcript 1
2. Identify keywords or phrases
3. Identify themes
4. Clustering of themes
5. Integration of cases

• Idiographic commitment:
  • Understanding the participant’s point of view.
  • Psychological focus on personal meaning-making.
• Set of common processes:
  • Case by case: start with one, then move on to a second case, and so on.
  • From the particular to the shared/common.
  • From description to interpretation.

(1) Read Through Case 1

• IPA always begins with a detailed reading and analysis of a single case.
• Read through transcript 1 (several times) – 'Immersion'.
• Insights come from knowing your data.

(2) Identify Keywords or Phrases

• Equivalent to ‘coding’ in thematic analysis:
  • Put them in the margin or highlight them.
• What are keywords?
  • Words that seem reflections of the speaker’s experience.
  • You have to make a judgement about this (remember the two assumptions about knowledge.)

(3) Identify Themes

• The ‘keywords’ you have highlighted may be indicative of possible themes (but there are not yet themes).

(4) Clustering Themes

• Establish any connections between themes
  • Cluster 1, Psychological states: Excitement, hunger for experiences.
  • Cluster 2, Psychological changes: Maturity, less risk-taking.
• Clusters are superordinate themes: Not all themes may fit the clusters.
• When you move on to analyse other cases, some of the themes might be dropped.

(5) Integration of Cases

• Integration of cases – if you have more than one case.
• Use the themes you have derived from the first interview as 'hypotheses' for the organisation of the second and third interviews.

Validation (in TA and IPA)

• How can I be confident in the interpretations that make up my analysis?
• How can others have confidence in my interpretations?
• Why is one interpretation better than another?
• The validity of your analytic claims is ultimately a matter of their plausibility.

Validation (During the Analysis)

• Iterative reading: Stick to the data and return to the data.
• As you start to think about a possible theme, you 'test' it as you go back and forth from transcript to margin ('Is this ‘excitement’? Is it like my other examples?’).
• Ask yourself:
  • Does this theme reflect an important and distinctive aspect of the speaker’s experience?
  • Are these different instances similar – do they make up a theme (then merge)?
  • Is this theme (‘excitement’) really different from the other theme (‘hunger for experience’) (then split)?

Validation (In the Write Up)

• Present illustrative quotes for each of your themes.
• Sufficient extracts from participants are presented to make a plausible case.
• Then other people can judge for themselves whether your reading is plausible.

Key Similarities and Differences Between TA and IPA