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Conducting thematic analysis on brief texts: The structured tabular approach

Conducting thematic analysis on brief texts: The structured tabular approach

Robinson, Oliver ORCID: 0000-0002-6758-2223 (2021) Conducting thematic analysis on brief texts: The structured tabular approach. Qualitative Psychology. ISSN 2326-3598 (Print), 2326-3601 (Online) (In Press) (doi:https://doi.org/10.1037/qup0000189)

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Abstract

In this article I present a structured approach to thematic analysis that is designed for working with brief texts. It is grounded in both the ecumenical thematic analysis of Boyatzis and the reflexive thematic analysis of Braun and Clarke. The process of structured tabular TA (ST-TA) is best conducted in spreadsheet software such as Microsoft Excel. As with other forms of thematic analysis, it permits inductive, deductive or hybrid approaches to theme development and analysis. Its logistical processes are well suited to working with the large samples that can be achieved when gathering brief text data. It can be used to conduct purely qualitative analyses, and can also elicit frequency data that can, in principle, be analysed quantitatively too. The process of checking agreement between analysts is an integral feature of the method. I discuss the practical implications of the approach and its applicability to various qualitative and mixed-methods research designs.

Item Type: Article
Uncontrolled Keywords: thematic analysis; brief texts, short stories, flexibility, qualitative psychology, mixed methods
Subjects: B Philosophy. Psychology. Religion > BF Psychology
Faculty / Department / Research Group: Faculty of Education, Health & Human Sciences
Faculty of Education, Health & Human Sciences > Department of Psychology, Social Work & Counselling
Faculty of Education, Health & Human Sciences > Institute for Lifecourse Development
Faculty of Education, Health & Human Sciences > Institute for Lifecourse Development > Centre for Mental Health
Last Modified: 26 Aug 2021 09:50
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
Selected for REF2021: None
URI: http://gala.gre.ac.uk/id/eprint/30842

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