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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