Thematic analysis and interpretative phenomenological analysis

Question 1

approaches to qualitative data and analysis

Answer 1

• Grounded theory
  
  • Content analysis
  • Discourse analysis
  • Conversational analysis
  • Narrative analysis
  • Thematic analysis
  • Interpretative phenomenological analysis (IPA)

Question 2

thematic analysis

Answer 2

• An umbrella term for a set of approaches that share a focus on identifying themes within data
  
  • It minimally organises and describes your data in detail
  • Thematic analysis underpins many other forms of qualitative analysis.

Question 3

what is a theme?

Answer 3

• A theme is a pattern of meaning that captures:
  
  • Something important about the material
  • A shared implicit or underlying meaning
  • Emphasis is on meaning, not prevalence

Question 4

where do themes come from?

Answer 4

• Date-driven/inductive - coding and theme development are data-driven (bottom-up)
  
  • Theory-driven/deductive - shaped by existing theoretical constructs, which provide the ‘lens’ to code and develop themes (top-down)
  • Most likely it’s a combination of both

Question 5

six phases of TA (Braun and Clarke, 2006)

Answer 5

1. Familiarisation oneself with the data
   
   2. Generating codes
   3. Searching for themes
   4. Reviewing potential themes
   5. Defining and naming themes
   6. Producing the report

Question 6

familiarisation oneself with the data

Answer 6

• We start by reading all transcripts and taking initial notes
  
  • You may ask things like?:
  • What sort of assumptions are being made?
  • How are certain groups characterised?
  • What ideas are being drawn on?

Question 7

initial coding

Answer 7

· Two types of coding (Braun and Clarke, 2013, p. 206):
- Selective coding - identifying 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 and Huberman, 1994)

Question 8

managing the coding process - from low to high-tech

Answer 8

· Use highlighters, pens, post-it notes, or comments function in word
· Make use of CAQDAS - computer-assisted qualitative data analysis software e.g., Nvivo

Question 9

reviewing themes

Answer 9

· Read all the collated extracts for each theme, and consider whether they appear to form a coherent pattern
· You decide:
- Some candidate themes are not really themes (e.g., if there are 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

Question 10

searching for themes

Answer 10

· Process of clustering together similar codes - which belong together
· Organise your 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?

Question 11

themes or topic summaries?

Answer 11

· In TA, themes are conceptualised 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.

Question 12

defining and naming themes and writing up

Answer 12

· 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).

Question 13

problems of thematic analysis

Answer 13

· “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

Question 14

advantages of TA

Answer 14

· 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)

Question 15

IPA

Answer 15

· A form of thematic analysis that makes a number of psychological assumptions
· IPA - interviews are for the study of experience (phenomenology)

Question 16

(lived) experiences of what?

Answer 16

· Answer - 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

Question 17

assumptions about knowledge in IPA

Answer 17

· 1st assumption:
- People interpret the world of phenomena (things)
- What we, researchers, study, therefore, are their interpretation (s) 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

Question 18

you would do IPA if

Answer 18

· Idiographic vs nomothetic:
- You are interested in the particular cases, not general statistical (average) tendencies in the population (the notional ‘average person’ - who doesn’t actually exist)
· Meanings vs casual relations:
- You are interested in what things mean to people
- If you want to make claims about casual 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)

Question 19

aims and research questions

Answer 19

· Experiences/events with major significance to the person
· People’s experiences and/or understandings of particular participants
· 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?

Question 20

data collection and samples in IPA

Answer 20

· 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)

Question 21

sample size for an IPA interview study

Answer 21

· 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)

Question 22

analytical satges (Smith et al, 2009)

Answer 22

· 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

Question 23

read through case 1

Answer 23

· 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

Question 24

identifying keywords or phrases

Answer 24

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

Question 25

identify themes

Answer 25

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

Question 26

clustering themes

Answer 26

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

Question 27

integration of cases

Answer 27

· 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

Question 28

validation

Answer 28

· 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

Question 29

validation (during the analysis)

Answer 29

· 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)?

Question 30

validation (in the write up)

Answer 30

· 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