Using thematic analysis in qualitative research

Sirwan Khalid Ahmed , Ribwar Arsalan Mohammed, Abdulqadir J. Nashwan, Radhwan Hussein Ibrahim, Araz Qadir Abdalla, Barzan Mohammed M. Ameen, Renas Mohammed Khdhir
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Abstract

Thematic analysis (TA) is one of the most widely utilized methods for analyzing qualitative data, offering a structured yet flexible framework for identifying, analyzing, and interpreting patterns of meaning within datasets. This paper provides a comprehensive overview of Braun and Clarke's six-phase thematic analysis framework, which includes (1) familiarization with data, (2) generating initial codes, (3) searching for themes, (4) reviewing themes, (5) defining and naming themes, and (6) writing the report. Additionally, it presents a 16-item checklist to ensure adherence to the established steps of thematic analysis, enhancing the rigor and reliability of the study. Each phase is explored in-depth, highlighting its purpose, key activities, reflexive considerations, challenges, and significance. Emphasis is placed on the iterative and reflexive nature of TA, where researchers actively engage with data and acknowledge their theoretical positioning and biases throughout the process. Challenges such as data overwhelm, coding inconsistencies, and balancing thematic depth and breadth are addressed, alongside practical strategies for overcoming these obstacles. The importance of transparency, reflexivity, and methodological rigor is underscored as central to producing trustworthy and insightful qualitative research. This article serves as both an academic reference and a practical guide for researchers aiming to apply thematic analysis effectively, ensuring that their findings are presented in a coherent, compelling, and analytically sound manner.
在定性研究中运用主题分析
主题分析(TA)是应用最广泛的定性数据分析方法之一,它为识别、分析和解释数据集中的意义模式提供了一个结构化但灵活的框架。本文全面概述了Braun和Clarke的六阶段主题分析框架,包括(1)熟悉数据,(2)生成初始代码,(3)搜索主题,(4)审查主题,(5)定义和命名主题,(6)撰写报告。此外,它还提出了一份16项清单,以确保遵守既定的专题分析步骤,提高研究的严谨性和可靠性。每个阶段都进行了深入探讨,突出了其目的、关键活动、反思性考虑、挑战和意义。重点放在技术分析的迭代性和反思性上,研究人员在整个过程中积极参与数据,并承认他们的理论定位和偏见。解决了数据过剩、编码不一致以及平衡主题深度和广度等挑战,并提出了克服这些障碍的实用策略。透明度、反思性和方法严谨性的重要性被强调为产生值得信赖和富有洞察力的定性研究的核心。本文既是学术参考,也是研究人员的实践指南,旨在有效地应用主题分析,确保他们的发现以连贯、引人注目和分析合理的方式呈现。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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