Thematic Analysis Flashcards

1
Q

What is analysis?

A

-Process of making meaning
-Researchers role to ‘uncover’ / interpret meaning from the data (e.g. field notes, interview transcripts, documents)
-Active process of meaning-making – it is your job is to actively create
meaning out of your raw material

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2
Q

What are some approaches to analysis?

A
Thematic analysis (overview of all)
Content analysis
Phenomenological
Ethnographic analysis
Grounded theories
Computer assisted
Narrative analyses
Discourse analyses
Conversational analysis
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3
Q

Give an overview of thematic analysis

A

-Most commonly used qualitative analysis technique
-Used for identifying, analysing and reporting patterns in the data
(themes)
-Organises and describes the data set meaningfully in relation to the
research question
-Requires involvement and interpretation from the researcher
-Involves identifying and describing explicit and implicit ideas within the
data

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4
Q

What is the process of thematic analysis?

A

• Familiarisation with the data-transcribe and read
• Code the data.-interesting features
• Search for themes-codes into categories
• Review the themes-check against data set as whole
• Define and name themes. Determine what each theme is telling the reader
about and label it accordingly.
• Write up the report. This involves selecting appropriat

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5
Q

What is a code?

A

A code is a label / description that is attached to a

selection of text which can later be related to a theme (core concept)

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6
Q

What is a theme?

A

Themes are identified through coding. These themes become the building blocks of theory

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7
Q

How can themes be identified?

A

• Deductive (Top down / A priori). Driven by a specific
research question.
• Inductive (Bottom up / Emergent). Themes are linked to the data (data driven)

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8
Q

Explain deductive/confirmatory thematic analysis?

A

• Tests a pre-determined idea / theory / model
• Largely depends on deductive coding approaches (e.g. Content Analysis, Template Analysis)
• More structured procedure – development of a priori themes
• Researcher seeks out concepts relating to the model/theory they are testing
e.g. colour coding data to fit ecological systems theory

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9
Q

Explain exploratory/inductive analysis

A
  • Emphasis is on the researcher’s interpretation of the data
  • The researcher will code anything they interpret as interesting / relevant /meaningful in the data
  • Requires interpretative skill on the part of the researcher – “what might the participant mean by this?”
  • If the data lack richness – the analysis will be
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10
Q

Explain themes in thematic analysis

A
  • Theme is a way of organising and categorising the codes – described the patterns within data
  • Commonly recurring topics
  • ‘A Theme represents some level of patterned response or meaning within the data set
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11
Q

How can themes be found?

A
  • Tables grouping codes into particular themes

* Thematic (concept) mapping

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12
Q

Explain thematic mapping

A
  • Thematic maps can assist the analysis by organising and representing knowledge
  • Mind map of themes and sub-themes
  • Provides a summary of themes and important elements
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13
Q

What are analytical notes?

A

Notes during data collection and early analysis

  • procedural memos (how you did coding)
  • analytic memos (hunches, ideas, best guesses about theory, connections etc)
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14
Q

Explain analytical notes

A
  • It is easy to forget initial ideas during data collection and early stages of analysis
  • Analytical notes help remind the researcher of the ideas and thoughts present during early analysis
  • Can help to develop greater scope and conceptual density-quality upgrading
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15
Q

Explain interpretation

A

It is the role of the researcher to interpret the data

Interpretations are made from the descriptive codes

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16
Q

Why should the data be managed effectively before thematic analysis

A

• Better overview and familiarity of the data
• Not get overwhelmed by the data

17
Q

Explain the transcription importance

A

• Starts process of interpretation not just reproduction of data observed
helps construct research

18
Q

familiarisation stage

A

reflect, segment and reduce the data to prevent being overwhelmed with data
• get a gestalt view

19
Q

coding stage

A
  • codes need to grounded in evidence from the data •showing credibility of the findings
  • codes must be distinct to one another
  • e.g. behaviours, events, meanings, relationships, consequences
  • similar codes become categories
20
Q

searching for themes

A
  • major Theme is something that occurs regularly in the data

* more holistic than coding and looks for core concepts

21
Q

What is CAQDAS

A
  • computer-aided analysis of qualitative data
  • saves time, helps organise
  • can take researcher away from the data
22
Q

problems with analysis

A
  • Premature closure-too early inferences and not enough analysis
  • not enough evidence
23
Q

what does a good analysis do?

A

Resonates with the reader which helps rigour, contributes to a new understanding
This is achieved with the appropriate writing style