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Using Model-Based Clustering to Improve Qualitative Inquiry: Computer-Aided Qualitative Data Analysis, Latent Class Analysis, and Interpretive Transparency

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Abstract

A combination of computer-aided qualitative data analysis (CAQDAS) and latent class analysis (LCA) can substantially augment the qualitative analysis of textual data sources used in third-sector studies. This article explains how to employ both techniques iteratively to capture often implicit ideas and meaning-making by third-sector leaders, donors, and other stakeholders. CAQDAS facilitates the coding, organization, and quantification of qualitative data, effectively creating parallel qualitative and quantitative data structures. LCA facilities the discovery of latent concepts, document classification, and the identification of exemplary qualitative evidence to aid interpretation. For third-sector research, CAQDAS and LCA are particularly promising because diverse stakeholders usually do not share homogenous views about core issues such as organizational effectiveness, collaboration, impact measurement, or philanthropic approaches, for example. The procedure explained here provides a rigorous method for discovering and understanding diversity in perspectives and is especially useful in medium-n research settings common to third-sector scholarship.

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Notes

  1. LCA identifies and summarizes patterns by positing a latent variable that explains the statistical associations among the underlying codes. In other words, conditional on the latent variable, the residual statistical association among the codes is essentially minimized. This condition is known as local independence. LCA thus generates a categorical latent variable that explains the information (coding patterns) found in the documents.

  2. Intractability is an exponential function of the number of codes involved (two to the power of the number of codes). For example, while ten codes yields 1024 possible patterns, 20 codes yields 1,048,576 possible patterns, 30 codes yields 1,073,741,824 possible patterns, and so on.

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Acknowledgements

For valuable feedback, we would like to thank the editors of this special issue as well as the anonymous reviewers.

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Authors and Affiliations

  1. Marxe School at Baruch College, One Bernard Baruch Way, New York, NY, 10010, USA

    George E. Mitchell

  2. University of San Diego, 5998 Alcalá Park, San Diego, CA, 92110, USA

    Hans Peter Schmitz

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  1. George E. Mitchell
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  2. Hans Peter Schmitz
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Correspondence to Hans Peter Schmitz.

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Mitchell, G.E., Schmitz, H.P. Using Model-Based Clustering to Improve Qualitative Inquiry: Computer-Aided Qualitative Data Analysis, Latent Class Analysis, and Interpretive Transparency. Voluntas 34, 162–169 (2023). https://doi.org/10.1007/s11266-021-00409-8

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