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Thematic Analysis defined and When to Use it?

Thematic Analysis is a qualitative method for analyzing data that involves identifying recurring themes, such as topics, ideas, and patterns, within a dataset. It is employed to discern patterns of meaning across data sets, providing insights into the research questions at hand. Particularly well-suited for exploring perceptions, views, and experiences, as well as understanding the construction of meaning, thematic analysis is widely used in various disciplines, including social, behavioral, and applied sciences. The method consists of six sequential steps, initially developed for psychology research by Braun and Clarke in 2006. These steps involve an iterative process, with movement back and forth between them.

Approaches to Thematic Analysis

Approaches to Thematic Analysis vary based on the study’s purpose.

Inductive or deductive approach

Researchers may adopt an inductive or deductive approach, where coding and theme development are driven by the data or preconceived concepts, respectively.

Semantic approach

The semantic approach focuses on explicit content, while the latent approach delves into underlying concepts and assumptions.

Realist and constructivist approaches

Realist and constructivist approaches differ in whether the analysis reports an assumed reality in the data or constructs a certain reality based on it.

The Steps in Doing Thematic Analysis are as follows:

1. Familiarization: Get to know the data thoroughly by reading and re-reading, or transcribing if necessary, to gain a comprehensive understanding. Take notes to mark initial ideas for codes.

2. Coding: Generate initial codes by identifying important information in the data relevant to research questions. Codes can be notes on the transcript, in a Word document table, or using dedicated software.

3. Generating Initial Themes: Examine codes and collated data to identify patterns and group related codes into broader themes. This iterative process involves combining, refining, or discarding codes to form coherent themes.

4. Reviewing Themes: Check and refine themes to ensure they accurately represent the data. Address contradictions, overlapping, or broadness by combining, splitting, creating, or discarding themes.

5. Defining and Naming Themes: Provide a detailed analysis of each final theme, describing what it is about, its scope, focus, and how it relates to other themes and research questions. Define themes clearly and give them succinct names.

6. Writing Up: Begin with an introduction to the research project, including the problem, aims, objectives, and questions. Provide background information on the research methodology and analysis. Summarize findings using the defined themes, presenting them in an organized manner, supported by participant quotes (using pseudonyms or numbers). Conclude by explaining the main takeaways and how the analysis addresses the research questions.

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