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Definition
Latent class analysis (LCA) is a latent variable modeling technique that used for identifying subgroups of individuals with unobserved but distinct patterns of responses to a set of observed categorical indicators (Lanza et al. 2007).
Introduction
Introduced by Lazarsfeld (1950) and further developed and extended by many methodologists later (e.g., Goodman, Haberman, Hagenaars, and Vermunt), LCA has been increasingly utilized in various research fields (e.g., psychology, education, management, and health sciences) as a useful technique for grouping individuals.
LCA is distinct from traditional clustering approaches (e.g., k-means cluster analysis), as LCA offers researchers a model-based (or probability-based) clustering. Because of this, an obvious advantage of LCA over traditional clustering approaches is that the choice of the cluster criterion relies on rigorous statistical tests; as a result, LCA is considered more objective than other approaches.
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References
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He, J., Fan, X. (2019). Latent Class Analysis. In: Zeigler-Hill, V., Shackelford, T. (eds) Encyclopedia of Personality and Individual Differences. Springer, Cham. https://doi.org/10.1007/978-3-319-28099-8_2313-1
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DOI: https://doi.org/10.1007/978-3-319-28099-8_2313-1
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