‘Membership matters’: applying Membership Categorisation Analysis (MCA) to qualitative data using Computer‐Assisted Qualitative Data Analysis (CAQDAS) Software

Abstract

This paper introduces and outlines a methodology that may be unfamiliar to some qualitative researchers: Membership Categorisation Analysis (MCA). The first section of the paper explains the basic principles of MCA and why it is a valid method for exploring the power of categorisations in texts and talk. Additionally, it explains why MCA differs from other forms of qualitative data analysis. The second section begins with a discussion of why researchers might or might not use Computer‐Assisted Qualitative Data Analysis (CAQDAS) Software. Subsequently, a detailed description of how MCA was applied to qualitative data using the CAQDAS software package NVivo is outlined. To provide examples, this paper draws on a project that used MCA to analyse the interview accounts of 25 young people who had taken a Gap Year between leaving school and beginning university. The paper concludes that qualitative researchers should consider using MCA and that CAQDAS is a useful tool to aid its application.

Keywords:

• Membership categorisation analysis
• CAQDAS
• NVivo
• Qualitative
• Gap Year

Acknowledgements

The author would like to thank the editors and the anonymous reviewers for their comments on earlier drafts of this paper. Additionally, the author would like to thank the following for their support in developing this paper and the research that it was based upon: Jo Moran‐Ellis, Geoff Cooper, Sara Arber, Ruth Rettie and Rob Meadows. Finally, thanks also to Ann Lewins and Christina Silver for teaching me how to use NVivo.

Notes

1. Sacks follows the ethnomethodological principle of referring to the co‐participants in social interaction as members.

2. This should not be taken to suggest that NVivo is the only package capable of undertaking MCA, merely that the author has used it to this effect.

3. In NVivo terminology, codes are termed nodes.