Latent Class Analysis (LCA) is a statistical technique that is used in factor, cluster, and regression techniques; it is a subset of structural equation modeling (SEM). LCA is a technique where constructs are identified and created from unobserved, or latent, subgroups, which are usually based on individual responses from multivariate categorical data. These constructs are then used for r further analysis. LCA models can also be referred to as finite mixture models.
What subtypes of disease exist within a given test?
What domains are found to exist among the different categorical symptoms?
Assumptions in latent class analysis:
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Key concepts and terms in LCA:
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Hagenaars, J. A. (1993). Loglinear models with latent variables. Newbury Park, CA: Sage Publications.
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Vermunt, J. K., & Magidson, J. (2002). Latent class cluster analysis. In J. A. Hagenaars & A. L. McCutcheon (Eds.), Applied latent class models (pp. 89-106). Cambridge, UK: Cambridge University Press.