PSY301 Lecture 3: Reflexive Thematic Analysis

Types of thematic analysis - ‘Coding reliability’ TA:

• neopositivist approaches with core concerns about ‘objective’ and ‘unbiased’ coding- the use of a codebook for ‘accurate’ and ‘reliable’ coding

• use inter-rater reliability (coding agreement) as a key measure of coding quality.

• often deductive in orientation, in the sense that themes are developed early on in, or even prior to, analysis

Types of thematic analysis - ‘Codebook’ TA:

• a cluster of methods that use some kind of structured coding framework for developing and documenting the analysis

• themes are typically initially developed early on, but in some methods, they can be refined, or new themes developed through inductive data engagement and the analytic process

Types of thematic analysis - ‘Reflexive’ TA:

• approaches that fully embrace qualitative research values and the subjective skills the researcher brings to the process – a research team is not required or even desirable for quality.

• Analysis, whether it is more inductive or more theoretical/deductive, is a situated interpretative reflexive process

• coding is open and organic, with no use of any coding framework

• themes should be the final ‘outcome’ of data coding and iterative theme development

Reasons to focus of reflexive thematic analysis

• Flexible method - Compatible with different theoretical positions and most types of data

• Organises and describes data in terms of themes

• Interprets how these themes address the research question

What is a theme?

-Captures something important in relation to your research question

-What ‘size’ does a theme need to be?

• More instances (prevalence) doesn’t necessarily mean more crucial

• Nor the amount of time spent on it in each data item

• Key is ‘significance’ or meaningfulness of the theme in relation to the research question

-However, something isn’t a theme if only one participant mentions it once

• Need to give some indication of how prevalent your themes were

-Thematic analysis (like all forms of qualitative analysis) is an active process:

• Themes do not just ‘emerge’ from the data

Before analysis begins - 3 decisions to make before you start

1. Where will the themes derive from?

2. At what level will themes be identified?

3. What theoretical position will inform your analysis?

Inductive vs. Theoretical - Where will the themes derive from?

• The analyst or the participants/texts?

• From prior theoretical considerations or from the data itself?

Theoretical Thematic Analysis

• ‘Top-down’

• Analysis driven by researcher’s theoretical or analytic interest

• Less of a rich description of overall data, more of a detailed analysis of one aspect

• Coding for a specific research question

Inductive Thematic Analysis

• ‘Bottom-Up’

• Themes strongly linked to data itself

• Try for reasonably comprehensive coverage

• Code data without trying to fit it to researcher’s preconceptions (e.g., research question, past literature etc)

• But, impossible to ever totally free oneself of preconceptions

At what level will the themes be identified? - Semantic level

Themes directly observable in the data

• E.g. could be direct reference to the beach and the bush which is the outdoor lifestyle

At what level will the themes be identified? - Latent level

Underlying phenomenon that you infer from your data

• E.g. could be: connection to nature or the value of leisure time

What theoretical position will inform your analysis?

It is important to make sure that you understand whether your approach is:

• realist vs relativist (or in the middle)

• inductive vs theoretical

• sematic vs latent

Make this explicit and transparent

Steps of the analysis process for Thematic Analysis:

1. Familiarising yourself with the data

2. Generating initial codes

3. Searching for themes

4. Reviewing Themes

5. Defining and naming themes

6. Producing the report

Steps of the analysis process for Reflexive Thematic Analysis:

1. Data familiarization and writing familiarisation notes

2. Systematic data coding

3. Generating initial themes from coded and collated data

4. Developing and reviewing themes

5. Refining, defining and naming themes

6. Writing the report

Data familiarization and writing familiarisation notes

• Transcribing data (if necessary)

• Reading and re-reading the data (immersion)

• Jotting down initial ideas

Systematic data coding

• Coding interesting features of the data systematically across whole data set

• Codes identify features of data that appears interesting to the analyst

• ‘the most basic segment, or element, of the raw data…that can be assessed in a meaningful way regarding the phenomenon (under investigation)’ (Boyatzis, 1998: 63)

• Process can be data-driven or theory-driven

• You are not coding for ‘themes’ at this stage rather, anything of interest

• Collate all data that fit under each code (cut-n-pasting or NUDIST or NVIVO software packages)

• Begin to think about the relationships between different codes

Codes VS Themes?

In reflexive TA, a code is conceptualised as an analytic unit or tool, used by researcher to develop (initial) themes.

• Codes can be thought of as entities that capture (at least) one observation or display (usually just) one facet.

• Themes, in contrast, are like multi-faceted crystals – they capture multiple observations or facets

• occasionally rich, complex and multifaceted codes might be ‘promoted’ to themes

Generating initial themes from coded and collated data

• Sort different codes into potential themes

• Analysing codes, how do different codes combine to form overarching themes

Developing and reviewing themes

• Some potential themes will prove to not really be themes (not enough data to support them)

• Some themes may collapse together into one theme

• Some themes may need to be split into two separate themes

• Review all coded extracts in each theme (do they fit?)

• Review your entire data set in relation to your identified themes (Does it represent the data? Did you miss anything?)

Refining, defining and naming themes

• Need to identify the ‘essence’ of what each theme is about

• What aspects of your data capture that theme (evidence)

• Begin to write a ‘story’ about your data, using your data extracts to support your ‘story’

• Don’t just paraphrase the content of the extracts - Identify what’s interesting about them, and WHY

• Provide a detailed analysis of each theme, and how the themes fit together to form an overall ‘story

Writing the report

Needs an introduction and method section

The results section will be analysis and discussion (or similar)

Analysis and discussion section needs to:

• tell the story of your data in a way that convinces the reader of your analysis

• be clear, coherent and interesting (non-repetitive)

• provide evidence for your themes (in form of suitable data extracts and analysis of them)

• go beyond just describing your data

Must make an argument in relation to research question!

Writing up your analysis

• Introduction

• Method

• Results or Result and Discussion or Analysis and Discussion

• Conclusion

• References

• Appendice

Reflexive thematic analysis - Method (subsections might have different names)

• Approach to analysis (what the method is and why it is appropriate)

• Data collection

• Participants (if relevant)

• Positionality/reflexive statement

• Steps of the analysis process

Reflexive thematic analysis - Results/Analysis and Discussion

Overview of your results (such as a list of themes, or a summary table/model)

Sub-section for each theme (using the name of the theme as the sub-heading)

• A description of the theme

• Extracts as evidence of the themes

• Analysis of the extracts that links to previous research/theory

• Summary of the theme, link to the next theme

Reflexive thematic analysis - Conclusion

• Summary of main results, how they answer the research question

• Implications

• Strengths and limitations

• Future research

• What your study has contributed