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Statistics & Research Methods II > Thematic Analysis (TA) > Flashcards

Thematic Analysis (TA) Flashcards

1
Q

ATTRIBUTES

A
  • popular method for analysing qualitative data
  • helps researchers make sense of their qualitative data by capturing patterns across data sets (may come from dif sources ie. interviews/focus groups/recordings/online chat)
  • can be inductive/deductive; underpinned by dif epistemological stances (aka. how you believe knowledge is created) ie. realist VS constructivist
  • dif approaches share some degree of theoretical flexibility BUT can differ greatly in procedures used to identify themes/underlying philosophy
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2
Q

RESEARCH AIMS

A
  • develop in-depth understanding of pps experiences/context influences
  • develop/extend theory by drawing on pps’ lived experiences
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3
Q

STEP-BY-STEP (PHASES 1-6)

A

BRAUN & CLARKE (2006)
1) FAMILIARISING YOURSELF W/YOUR DATA
2) GENERATING INITIAL CODES
3) SEARCHING FOR THEMES
4) REVIEWING THEMES
5) DEFINING & NAMING THEMES
6) PRODUCING REPORT

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4
Q

PHASE 1: FAMILIARISING YOURSELF W/YOUR DATA

A

BRAUN & CLARKE (2006)
- transcription
- reading/re-reading data
- noting down initial ideas

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5
Q

PHASE 2: GENERATING INITIAL CODES

A

BRAUN & CLARKE (2006)
- coding interesting features of data in systematic fashion across entire data set
- collating data relevant to each code

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6
Q

PHASE 3: SEARCHING FOR THEMES

A

BRAUN & CLARKE (2006)
- collating codes into potential themes
- gathering all data relevant to each potential theme

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7
Q

PHASE 4: REVIEWING THEMES

A

BRAUN & CLARKE (2006)
- checking if themes “work” in relation to coded extracts (LVL 1) & entire data set (LVL 2)
- generating thematic map of analysis

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8
Q

PHASE 5: DEFINING & NAMING THEMES

A

BRAUN & CLARKE (2006)
- ongoing analysis to refine specifics of each theme & overall “story” told by analysis
- generating clear definitions/names for each theme

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9
Q

PHASE 6: PRODUCING REPORT

A

BRAUN & CLARKE (2006)
- selection of vivid/compelling extracts
- final analysis of selected extracts
- relating analysis to research q/lit
- producing scholarly report

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10
Q

DEFINITION

A
  • method NOT methodology (theoretically informed framework)
  • range of approaches differing in philosophy/procedure
  • used to identify/analyse/synthesise patterns in data set
  • used in many academic disciplines ie. psych/business/clinical research in policy contexts
  • theoretically/methodologically flexible
  • accessible to researchers/audiences
  • common language to talk about qualitative research
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11
Q

BACKGROUND

A
  • unclear origins; examples in 1930s musicology/1940s psychotherapy aka. approach for analysing patterns of meaning
  • interest exploded in 1990s; growing egs in social sciences; oft discussed themes as emerging w/little insight into methodological procedures
  • “poorly demarcated & rarely acknowledged procedure yet widely used qualitative analytic method” (Braun & Clarke (2006))
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12
Q

BRAUN ET AL. (2019)

A
  • not uncommon to see researchers cite sources/follow procedures for approaches to TA that don’t align conceptually/in practice
  • not grasping said distinctions can result in published papers where:
    1) TA approach used = unclear
    2) procedures/assumptions = misattributed/mixed
    3) underlying conceptual clashed between dif approaches aren’t recognised
  • does disservice to TA
  • avoiding such errors requires understanding of conceptual/procedural difs in terrain
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13
Q

KEY TERMS

A

DATA CORPUS
- all data collected for project (ie. interviews/websites/diaries)
DATA SET
- all data from corpus used for particular analysis (ie. interviews)
DATA ITEM
- piece of collected data (ie. an interview)
DATA EXTRACT
- identified chunk of data item (ie. quote from interview)

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14
Q

THEME: DEFINITION

A
  • building blocks = codes
  • TA captures something important about data in relation to research question
  • more instances (prevalence) does NOT necessarily mean more crucial (nor amount of time spent on each data item)
  • key = significant/meaningfulness of theme in relation to research question
  • AKA. data extract -> code -> theme
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15
Q

KETAMINE EXAMPLE: DATA EXTRACT

A

QUOTES
- “sort of almost like I was looking through a GLASS WINDOW…”
- “…couldn’t actually move my body due to the DETACHMENT…”
- “…just going along flying feeling like you were FLOATING…”

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16
Q

KETAMINE EXAMPLE: CODES

A
  • dissociative like experiences -> detachment/floating
  • positive experiences -> awe/amazement/calmness
  • paranoia & fear
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17
Q

KETAMINE EXAMPLE: THEME

A
  • inherent contradictions of acute ketamine experiences
18
Q

3 APPROACHES/SCHOOLS OF TA (BRAUN ET AL. (2019))

A

CODING RELIABILITY
- small Q
CODEBOOK APPROACH
- biggish Q
REFLEXITIVE APPROACH
- big Q

19
Q

CODING RELIABILITY (SMALL Q)

A

BRAUN ET AL. (2019)
- shares values w/quantitative approaches BUT uses qualitative methods
- data goes into pre-determined codes/themes aka. consensus coding
- coders preferring to work independently have little knowledge/experience of topic; determine statistical score of agreement
- themes oft dominate summaries; oft derived from data collection questions

20
Q

CODEBOOK APPROACH (BIGGISH Q)

A

BRAUN ET AL. (2019)
- sits between coding reliability & reflexive TA
- shares structured approach to coding w/coding reliability
- codes can be pre-determined BUT broadly qualitative philosophy of reflexive TA
- some themes = determined in advance of full analysis aka. conceptualised as domain summaries of what pps said in relation to particular topic/data collection question
- incl. Framework analysis (Ritchie & Spencer (1994))

21
Q

REFLEXIVE/ORGANIC APPROACH (BIG Q)

A

BRAUN ET AL. (2019)
- themes = meaning based patterns evident in explicit (semantic)/conceptual (latent) ways
- results from considerable analytic work on part of researcher to explore/develop an understanding of patterned meaning across the dataset
- coding organic/iterative; NOT pre-set
- aim = provide coherent/compelling interpretation of data grounded in it; researcher actively this via lens of own cultural membership/social positionings/theoretical assumptions/ideological commitments/scholarly knowledge

22
Q

INDUCTIVE

A
  • bottom-up
  • NOT driven by researcher’s theoretical interests/background
  • themes linked to data
  • research q might evolve during analysis
  • little resemblance to interview qs
  • no pre-defined coding frame
23
Q

DEDUCTIVE/THEORETICAL

A
  • top-down
  • driven by researcher’s theoretical/analytic interests
  • themes linked to theory/analytic interests/research qs/interview qs
  • specific research question
  • might use pre-defined coding frame
24
Q

THEMES: TERMINOLOGY

A

SEMANTIC THEME
DOMAIN SUMMARIES
BUCKET THEME
STORY BOOK THEME
LATENT THEME
FULLY REALISED THEME

25
Q

PERFORMING TA: DEFINING

A
  • defining research q/nature of research investigation aka:
    1) is it based on a theoretical framework/perspective?
    2) is it exploratory?
    3) are you aiming to explore the social reality of a particular group?
26
Q

PERFORMING TA: OUTLINING

A
  • outlining qualitative method of data collection
  • aka: which is most appropriate given your perspective/research question/resources
27
Q

PERFORMING TA: THINKING

A
  • thinking about method of analysis aka. school of thematic analysis you’ll be using
  • ie. expectations & whether its big Q/small Q
28
Q

PHASES OF TA

A

1) FAMILIARISING YOURSELF W/DATA
2) GENERATING INITIAL CODES
3) SEARCHING FOR THEMES
4) REVIEWING THEMES
5) DEFINING/NAMING THEMES
6) PRODUCING REPORT

29
Q

PHASE 1) FAMILIARISING YOURSELF W/DATA

A
  • transcribing data (if necessary)
  • reading/re-reading data
  • jotting down initial ideas
30
Q

PHASE 2) GENERATING INITIAL CODES

A
  • coding interesting features of data systematically across whole data set
  • codes identify feature of data that appears interesting to analyst
  • process can be data/theory driven
  • you are NOT coding for themes here; rather anything of interest
  • collate all data that fits under each code (cut-n-pasting)
  • NUDIST/NVIVO software packages
  • begin to think about relations between dif codes
31
Q

PHASE 3) SEARCHING FOR THEMES

A
  • sort dif codes into potential themes
  • analysing codes aka. how dif codes combine to form overarching themes
  • phase ends when you have collection of potential themes/host of data extracts coded in relation to them
32
Q

PHASE 4) REVIEWING THEMES

A
  • some potential themes will prove to not really be themes (not enough supporting data)
  • some themes:
    1) may collapse together into 1
    2) may need to be split into 2 separate themes
  • review:
    1) all coded extracts in each theme aka. do they fit?
    2) entire data set in relation to identified themes aka. does it represent data? did you miss anything?
33
Q

PHASE 5) DEFINING & NAMING THEMES

A
  • need to identify essence of what each theme is about & what aspects of data capture said theme
  • begin to write “story” about data using data extracts to support it
  • don’t just paraphrase content of extracts; identify what’s interesting about them & WHY
  • provide detailed analysis of each theme & how themes fit together to form overall “story”
  • begin to think about what “names” you’ll give themes in final analysis
34
Q

PHASE 6) PRODUCING REPORT

A
  • need to tell story of data in a way that convinces reader of their merit/validity of analysis s
  • should be clear/coherent/interesting aka. non-repetitive
  • need to provide evidence for themes in form of suitable data extracts & analysis for them
  • need to go beyond just describing your data
  • must make argument in relation to research q
35
Q

GOING BEYOND DATA SURFACE

A
  • ask yourself:
    1) what does this theme mean?
    2) what are the assumptions underpinning it?
    3) what are the implications of this theme?
    4) what conditions are likely to have given rise to it?
    5) why do people talk about this in a particular way as opposed to other ways?
    6) what is overall story that dif themes reveal about topic/research q?
36
Q

POTENTIAL PITFALLS

A
  • failing to actually analyse data
  • using interview questions as themes
  • weak/unconvincing analysis aka. themes don’t work/make sense
  • mismatching between data & analytic claims aka. data examples don’t support themes compellingly
  • mismatching between theoretical/epistemological position & analytic claims ie. claiming constructionist position then talking in realist terms
37
Q

CRITERIA: TRANSCRIPTION

A

1) Data’s been transcribed to appropriate lvl of detail & transcripts have been checked against tapes for accuracy.

38
Q

CRITERIA: CODING

A

2) Each data item’s been given equal attention in coding process.
3) Themes haven’t been generated from a few vivid egs (ie. anecdotal approach); instead coding process’ been thorough/inclusive/comprehensive.
4) All relevant extracts for each theme have been collated.
5) Themes’ve been checked against each other & back to original data set.
6) Themes are internally coherent/consistent/distinctive.

39
Q

CRITERIA: ANALYSIS

A

7) Data have been analysed/interpreted/made sense of > paraphrased/described.
8) Analysis/data match; extracts illustrate analytic claims.
9) Analysis tells convincing/well-organised story about data/topic.
10) Good balance between analytic narrative/illustrative extracts is provided.

40
Q

CRITERIA: OVERALL

A

11) Enough time’s been allocated to complete all phases of analysis adequately w/o rushing phase/giving it once-over-lightly.

41
Q

CRITERIA: WRITTEN REPORT

A

12) Assumptions about/specific approach to TA = clearly explicated.
13) Good fit between what you claim to do VS what you show you’ve done (ie. described method & reported analysis = consistent).
14) Language/concepts used in report = consistent w/epistemological position of analysis.
15) Researcher is positioned as active in research process aka. themes don’t just emerge.

Statistics & Research Methods II (8 decks)

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