Abstract

Latent class analysis (LCA) is a statistical procedure used to identify qualitatively different subgroups within populations who often share certain outward characteristics. The assumption underlying LCA is that membership in unobserved groups (or classes) can be explained by patterns of scores across survey questions, assessment indicators, or scales. The application of LCA is an active area of research and continues to evolve. As more researchers begin to apply the approach, detailed information on key considerations in conducting LCA is needed. In the present article, we describe LCA, review key elements to consider when conducting LCA, and provide an example of its application.

Keywords

Latent class modelClass (philosophy)Key (lock)PsychologyStatistical analysisData scienceManagement scienceRisk analysis (engineering)EconometricsComputer scienceStatisticsMachine learningArtificial intelligenceMathematicsEngineeringBusiness

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Publication Info

Year
2020
Type
article
Volume
46
Issue
4
Pages
287-311
Citations
1703
Access
Closed

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Cite This

Bridget E. Weller, Natasha K. Bowen, Sarah J. Faubert (2020). Latent Class Analysis: A Guide to Best Practice. Journal of Black Psychology , 46 (4) , 287-311. https://doi.org/10.1177/0095798420930932

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DOI
10.1177/0095798420930932

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Data completeness: 81%