[15] Thematic analysis can be used to analyse most types of qualitative data including qualitative data collected from interviews, focus groups, surveys, solicited diaries, visual methods, observation and field research, action research, memory work, vignettes, story completion[16] and secondary sources.
Data-sets can range from short, perfunctory response to an open-ended survey question to hundreds of pages of interview transcripts.
Rooted in humanistic psychology, phenomenology notes giving voice to the "other" as a key component in qualitative research in general.
This approach allows the respondents to discuss the topic in their own words, free of constraints from fixed-response questions found in quantitative studies.
But inductive learning processes in practice are rarely 'purely bottom up'; it is not possible for the researchers and their communities to free themselves completely from ontological (theory of reality), epistemological (theory of knowledge) and paradigmatic (habitual) assumptions – coding will always to some extent reflect the researcher's philosophical standpoint, and individual/communal values with respect to knowledge and learning.
Some qualitative researchers are critical of the use of structured code books, multiple independent coders and inter-rater reliability measures.
[25] Braun and Clarke and colleagues have been critical of a tendency to overlook the diversity within thematic analysis and the failure to recognise the differences between the various approaches they have mapped out.
[27] For some thematic analysis proponents, including Braun and Clarke, themes are conceptualised as patterns of shared meaning across data items, underpinned or united by a central concept, which are important to the understanding of a phenomenon and are relevant to the research question.
Gender, Support) or titles like 'Benefits of...', 'Barriers to...' signalling the focus on summarising everything participants said, or the main points raised, in relation to a particular topic or data domain.
[37] Meaning saturation – developing a "richly textured" understanding of issues – is thought to require larger samples (at least 24 interviews).
[39] Some quantitative researchers have offered statistical models for determining sample size in advance of data collection in thematic analysis.
This description of Braun and Clarke's six phase process also includes some discussion of the contrasting insights provided by other thematic analysis proponents.
[2] For others, including Braun and Clarke, transcription is viewed as an interpretative and theoretically embedded process and therefore cannot be 'accurate' in a straightforward sense, as the researcher always makes choices about how to translate spoken into written text.
[2] The second step in reflexive thematic analysis is tagging items of interest in the data with a label (a few words or a short phrase).
This systematic way of organizing and identifying meaningful parts of data as it relates to the research question is called coding.
[48] For more positivist inclined thematic analysis proponents, dependability increases when the researcher uses concrete codes that are based on dialogue and are descriptive in nature.
However, Braun and Clarke urge researchers to look beyond a sole focus on description and summary and engage interpretatively with data – exploring both overt (semantic) and implicit (latent) meaning.
[1] Coding sets the stage for detailed analysis later by allowing the researcher to reorganize the data according to the ideas that have been obtained throughout the process.
[2] Throughout the coding process, full and equal attention needs to be paid to each data item because it will help in the identification of otherwise unnoticed repeated patterns.
Themes consist of ideas and descriptions within a culture that can be used to explain causal events, statements, and morals derived from the participants' stories.
Thematic analysis allows for categories or themes to emerge from the data like the following: repeating ideas; indigenous terms, metaphors and analogies; shifts in topic; and similarities and differences of participants' linguistic expression.
Connections between overlapping themes may serve as important sources of information and can alert researchers to the possibility of new patterns and issues in the data.
For Guest and colleagues, deviations from coded material can notify the researcher that a theme may not actually be useful to make sense of the data and should be discarded.
It is imperative to assess whether the potential thematic map meaning captures the important information in the data relevant to the research question.
If the map does not work it is crucial to return to the data in order to continue to review and refine existing themes and perhaps even undertake further coding.
Braun and Clarke recommend caution about developing many sub-themes and many levels of themes as this may lead to an overly fragmented analysis.
For coding reliability proponents Guest and colleagues, researchers present the dialogue connected with each theme in support of increasing dependability through a thick description of the results.
[1] A clear, concise, and straightforward logical account of the story across and with themes is important for readers to understand the final report.
[15] As well as highlighting numerous practical concerns around member checking, they argue that it is only theoretically coherent with approaches that seek to describe and summarise participants' accounts in ways that would be recognisable to them.
Quality is achieved through a systematic and rigorous approach and through the researcher continually reflecting on how they are shaping the developing analysis.