Thematic analysis

Thematic analysis is one of the most common forms of analysis within qualitative research. It emphasizes pinpointing, examining, and recording patterns of meaning (or 'themes') within data. There is no one definition of a theme. For some thematic analysis proponents, themes are patterns of shared meaning across data items, underpinned by a central concept, that are important to the understanding of a phenomenon and are associated with a specific research question. For others, themes are simply summaries of information related to a particular topic or data domain, there is no requirement for shared meaning organised around a central concept. Although these two conceptualisations are often associated with particular approaches to thematic analysis they are often confused and conflated. Thematic analysis is best thought of as an umbrella term for a variety of different approaches, rather than a singular method. Different versions of thematic analysis are underpinned by different philosophical and conceptual assumptions and are divergent in terms of procedure. Leading thematic analysis proponents, psychologists Virginia Braun and Victoria Clarke, distinguish between three main types of thematic analysis - coding reliability, code book and reflexive. They describe their own widely used approach as reflexive thematic analysis. Thematic analysis is one of the most common forms of analysis within qualitative research. It emphasizes pinpointing, examining, and recording patterns of meaning (or 'themes') within data. There is no one definition of a theme. For some thematic analysis proponents, themes are patterns of shared meaning across data items, underpinned by a central concept, that are important to the understanding of a phenomenon and are associated with a specific research question. For others, themes are simply summaries of information related to a particular topic or data domain, there is no requirement for shared meaning organised around a central concept. Although these two conceptualisations are often associated with particular approaches to thematic analysis they are often confused and conflated. Thematic analysis is best thought of as an umbrella term for a variety of different approaches, rather than a singular method. Different versions of thematic analysis are underpinned by different philosophical and conceptual assumptions and are divergent in terms of procedure. Leading thematic analysis proponents, psychologists Virginia Braun and Victoria Clarke, distinguish between three main types of thematic analysis - coding reliability, code book and reflexive. They describe their own widely used approach as reflexive thematic analysis. Thematic analysis is used in qualitative research and focuses on examining themes within data. This method emphasizes organization and rich description of the data set. Thematic analysis goes beyond simply counting phrases or words in a text and moves on to identifying implicit and explicit ideas within the data. Coding is the primary process for developing themes within the raw data by recognizing important moments in the data and encoding it prior to interpretation. The interpretation of these codes can include comparing theme frequencies, identifying theme co-occurrence, and graphically displaying relationships between different themes. Most researchers consider thematic analysis to be a very useful method in capturing the intricacies of meaning within a data set. There is a wide range as to what a 'data set' entails (see qualitative data). Texts can range from a single-word response to an open-ended question or as complex as a body of thousands of pages. As a consequence, data analysis strategies will likely vary according to size. Most qualitative researchers analyze transcribed in-depth interviews that can be 2-hours in length, resulting in nearly 40 pages of transcribed data per respondent. Also, it should be taken into consideration that complexity in a study can vary according to different data types. Thematic analysis is also related to phenomenology in that it focuses on the human experience subjectively. This 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 allows the respondents to discuss the topic in their own words, free of constraints from fixed-response questions found in quantitative studies. Like most research methods, this process of data analysis can occur in two primary ways—inductively or deductively. In an inductive approach, the themes identified are strongly linked to the data because assumptions are data-driven. This means that the process of coding occurs without trying to fit the data into a pre-existing model or frame. It is important to note that throughout this inductive process, it is not possible for the researchers to free themselves from their theoretical epistemological responsibilities. Deductive approaches, on the other hand, are theory-driven. This form of analysis tends to be less descriptive overall because analysis is limited to the preconceived frames. The result tends to focus on one or two specific aspects of the data that were determined prior to data analysis. The choice between these two approaches generally depends on the researchers' epistemologies (see epistemology). A theme represents a level of patterned response or meaning from the data that is related to the research questions at hand. Determining what can be considered a theme can be used with deciding prevalence. This does not necessarily mean the frequency at which a theme occurs, but in terms of space within each data item and across the data set. It is ideal that the theme will occur numerous times across the data set, but a higher frequency does not necessarily mean that the theme is more important to understanding the data. A researcher's judgement is the key tool in determining which themes are more crucial. A potential data analysis pitfall occurs when researchers use the research question to code instead of creating codes and fail to provide adequate examples from the data. Eventually, themes need to provide an accurate understanding of the 'big picture'. There are also different levels at which themes can be identified—semantic and latent. A thematic analysis generally focuses wholly or mostly on one level. Semantic themes attempt to identify the explicit and surface meanings of the data. The researcher does not look beyond what the participant said or wrote. In this instance, the researcher wishes to give the reader a sense of the important themes. Thus, some depth and complexity is lost. However, a rich description of the entire data set is represented. Conversely, latent themes identify underlying ideas, patterns, and assumptions. This requires much interpretation of the data, so researchers might focus on one specific question or area of interest across the majority of the data set. A theme is different from a code. Several texts recommend that researchers 'code for themes'. This can be misleading because the theme is considered the outcome or result of coding, not that which is coded. The code is the label that is given to particular pieces of the data that contribute to a theme. For example, 'SECURITY can be a code, but A FALSE SENSE OF SECURITY can be a theme.' Given that qualitative work is inherently interpretive research, the biases, values, and judgments of the researchers need to be explicitly acknowledged so they are taken into account in data presentation. This type of openness is considered to be positive in the qualitative community. Researchers shape the work that they do and work as the instrument for collecting and analyzing data. In order to acknowledge the researcher as the tool of analysis, it is necessary for one to create and maintain a reflexivity journal.