Thematic analysis

Thematic analysis is used in qualitative research and focuses on examining themes or patterns of meaning within data. This method can emphasize both organization and rich description of the data set and theoretically informed interpretation of meaning. In some thematic analysis approaches coding follows theme development and is a deductive process of allocating data to pre-identified themes (this approach is common in coding reliability and code book approaches), in other approaches – notably Braun and Clarke's reflexive approach – coding precedes theme development and themes are built from codes. 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 and secondary sources. Data-sets can range from short, perfunctory response to an open-ended survey question to hundreds of pages of interview transcripts. Thematic analysis can be used to analyse both small and large data-sets. A phenomenological approach emphasizes the participants' perceptions, feelings and experiences as the paramount object of study. 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. Thematic analysis is sometimes erroneously assumed to be only compatible with phenomenology or experiential approaches to qualitative research. Braun and Clarke argue that their reflexive approach is equally compatible with social constructionist, poststructuralist and critical approaches to qualitative research. They emphasise the theoretical flexibility of thematic analysis and its use within realist, critical realist and relativist ontologies and positivist, contextualist and constructionist epistemologies. Like most research methods, the process of thematic analysis of data can occur both inductively or deductively. This form of analysis tends to be more interpretative because analysis is explicitly shaped and informed by pre-existing theory and concepts (ideally cited for transparency in the shared learning). Deductive approaches can involve seeking to identify themes identified in other research in the data-set or using existing theory as a lens through which to organise, code and interpret the data. Sometimes deductive approaches are misunderstood as coding driven by a research question or the data collection questions. A thematic analysis can also combine inductive and deductive approaches, for example in foregrounding interplay between a priori ideas from clinician-led qualitative data analysis teams and those emerging from study participants and the field observations. == Different approaches ==

Coding reliability which combine the use of qualitative data with data analysis processes and procedures based on the research values and assumptions of (quantitative) positivism – emphasising the importance of establishing coding reliability and viewing researcher subjectivity or 'bias' as a potential threat to coding reliability that must be contained and 'controlled for' to avoiding confounding the 'results' (with the presence and active influence of the researcher). Boyatzis Braun and Clarke (citing Yardley) argue that all coding agreement demonstrates is that coders have been trained to code in the same way not that coding is 'reliable' or 'accurate' with respect to the underlying phenomena that is coded and described. template analysis and matrix analysis centre on the use of structured code books but – unlike coding reliability approaches – emphasise to a greater or lesser extent qualitative research values. Both coding reliability and code book approaches typically involve early theme development – with all or some themes developed prior to coding, often following some data familiarisation (reading and re-reading data to become intimately familiar with its contents). Once themes have been developed the code book is created – this might involve some initial analysis of a portion of or all of the data. The data is then coded. Coding involves allocating data to the pre-determined themes using the code book as a guide. The code book can also be used to map and display the occurrence of codes and themes in each data item. Themes are often of the shared topic type discussed by Braun and Clarke. They argue that this failure leads to unthinking 'mash-ups' of their approach with incompatible techniques and approaches such as code books, consensus coding and measurement of inter-rater reliability. ==Theme==

There is no one definition or conceptualisation of a theme in thematic analysis. 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. cannot be developed prior to coding (because they are built from codes), so are the output of a thorough and systematic coding process. Braun and Clarke have been critical of the confusion of topic summary themes with their conceptualisation of themes as capturing shared meaning underpinned by a central concept. Some qualitative researchers have argued that topic summaries represent an under-developed analysis or analytic foreclosure. There is controversy around the notion that 'themes emerge' from data. Braun and Clarke are critical of this language because they argue it positions themes as entities that exist fully formed in data – the researcher is simply a passive witness to the themes 'emerging' from the data. This can be confusing because for Braun and Clarke, and others, the theme is considered the outcome or result of coding, not that which is coded. In approaches that make a clear distinction between codes and themes, the code is the label that is given to particular pieces of the data that contributes to a theme. For example, "SECURITY can be a code, but A FALSE SENSE OF SECURITY can be a theme." ==Methodological issues==

Reflexivity journals Given that qualitative work is inherently interpretive research, the positionings, values, and judgments of the researchers need to be explicitly acknowledged so they are taken into account in making sense of the final report and judging its quality. This type of openness and reflection is considered to be positive in the qualitative community. Researchers shape the work that they do and are the instrument for collecting and analyzing data. In order to acknowledge the researcher as the tool of analysis, it is useful to create and maintain a reflexivity journal. The reflexivity process can be described as the researcher reflecting on and documenting how their values, positionings, choices and research practices influenced and shaped the study and the final analysis of the data. Reflexivity journals are somewhat similar to the use of analytic memos or memo writing in grounded theory, which can be useful for reflecting on the developing analysis and potential patterns, themes and concepts. Once data collection is complete and researchers begin the data analysis phases, they should make notes on their initial impressions of the data. The logging of ideas for future analysis can aid in getting thoughts and reflections written down and may serve as a reference for potential coding ideas as one progresses from one phase to the next in the thematic analysis process.). Some coding reliability and code book proponents provide guidance for determining sample size in advance of data analysis – focusing on the concept of saturation or information redundancy (no new information, codes or themes are evident in the data). These attempts to 'operationalise' saturation suggest that code saturation (often defined as identifying one instances of a code) can be achieved in as few as 12 or even 6 interviews in some circumstances. Meaning saturation – developing a "richly textured" understanding of issues – is thought to require larger samples (at least 24 interviews). There are numerous critiques of the concept of data saturation – many argue it is embedded within a realist conception of fixed meaning and in a qualitative paradigm there is always potential for new understandings because of the researcher's role in interpreting meaning. Some quantitative researchers have offered statistical models for determining sample size in advance of data collection in thematic analysis. For example, Fugard and Potts offered a prospective, quantitative tool to support thinking on sample size by analogy to quantitative sample size estimation methods. Lowe and colleagues proposed quantitative, probabilistic measures of degree of saturation that can be calculated from an initial sample and used to estimate the sample size required to achieve a specified level of saturation. Their analysis indicates that commonly used binomial sample size estimation methods may significantly underestimate the sample size required for saturation. All of these tools have been criticised by qualitative researchers (including Braun and Clarke) for relying on assumptions about qualitative research, thematic analysis and themes that are antithetical to approaches that prioritise qualitative research values. == Braun and Clarke's six phases of thematic analysis==

Phase 1: Becoming familiar with the data This six-phase process for thematic analysis is based on the work of Braun and Clarke and their reflexive approach to thematic analysis. This six phase cyclical process involves going back and forth between phases of data analysis as needed until the researchers are satisfied with the final themes. Analyzing data in an active way will assist researchers in searching for meanings and patterns in the data set. At this stage, it is tempting to rush this phase of familiarisation and immediately start generating codes and themes; however, this process of immersion will aid researchers in identifying possible themes and patterns. Reading and re-reading the material until the researcher is comfortable is crucial to the initial phase of analysis. While becoming familiar with the material, note-taking is a crucial part of this step in order begin developing potential codes. Quality transcription of the data is imperative to the dependability of analysis. Criteria for transcription of data must be established before the transcription phase is initiated to ensure that dependability is high. Data reduction (Coffey and Atkinson) Source: Researchers must then conduct and write a detailed analysis to identify the story of each theme and its significance. By the end of this phase, researchers can (1) define what current themes consist of, and (2) explain each theme in a few sentences. It is important to note that researchers begin thinking about names for themes that will give the reader a full sense of the theme and its importance. Failure to fully analyze the data occurs when researchers do not use the data to support their analysis beyond simply describing or paraphrasing the content of the data. Researchers conducting thematic analysis should attempt to go beyond surface meanings of the data to make sense of the data and tell an accurate story of what the data means. Phase 6: Producing the report After final themes have been reviewed, researchers begin the process of writing the final report. While writing the final report, researchers should decide on themes that make meaningful contributions to answering research questions which should be refined later as final themes. 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. The goal of this phase is to write the thematic analysis to convey the complicated story of the data in a manner that convinces the reader of the validity and merit of your analysis. A clear, concise, and straightforward logical account of the story across and with themes is important for readers to understand the final report. The write up of the report should contain enough evidence that themes within the data are relevant to the data set. Extracts should be included in the narrative to capture the full meaning of the points in analysis. The argument should be in support of the research question. For some thematic analysis proponents, the final step in producing the report is to include member checking as a means to establish credibility, researchers should consider taking final themes and supporting dialog to participants to elicit feedback. However, Braun and Clarke are critical of the practice of member checking and do not generally view it as a desirable practice in their reflexive approach to thematic analysis. 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. Given their reflexive thematic analysis approach centres the active, interpretive role of the researcher – this may not apply to analyses generated using their approach. == Advantages and disadvantages ==

A technical or pragmatic view of research design centres researchers conducting qualitative analysis using the most appropriate method for the research question. for their reflexive approach. For coding reliability thematic analysis proponents, the use of multiple coders and the measurement of coding agreement is vital. Thematic analysis has several advantages and disadvantages, it is up to the researchers to decide if this method of analysis is suitable for their research design. Advantages • The theoretical and research design flexibility it allows researchers – multiple theories can be applied to this process across a variety of epistemologies. • Well suited to large data sets. • Code book and coding reliability approaches are designed for use with research teams. • Interpretation of themes supported by data. • Applicable to research questions that go beyond an individual's experience. • Allows for inductive development of codes and themes from data. Disadvantages • Thematic analysis may miss nuanced data if the researcher is not careful and uses thematic analysis in a theoretical vacuum. • Flexibility can make it difficult for novice researchers to decide what aspects of the data to focus on. • Limited interpretive power of analysis is not grounded in a theoretical framework. • Difficult to maintain sense of continuity of data in individual accounts because of the focus on identifying themes across data items. • Does not allow researchers to make technical claims about language usage (unlike discourse analysis and narrative analysis). ==See also==