Latent Class Analysis and Finite Mixture Modeling

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ID: 296637
2013
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Abstract
Finite mixture models, which are a type of latent variable model, express the overall distribution of one or more variables as a mixture of a finite number of component distributions. In direct applications, one assumes that the overall population heterogeneity with respect to a set of manifest variables results from the existence of two or more distinct homogeneous subgroups, or latent classes, of individuals. This chapter presents the prevailing "best practices" for direct applications of basic finite mixture modeling, specifically latent class analysis (LCA) and latent profile analysis (LPA), in terms of model assumptions, specification, estimation, evaluation, selection, and interpretation. In addition, a brief introduction to structural equation mixture modeling in the form of latent class regression is provided as well as a partial overview of the many more advanced mixture models currently in use. The chapter closes with a cautionary note about the limitations and common misuses of latent class models and a look toward promising future developments in mixture modeling.
Reference Key
openalex_W2466908408 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Katherine E. Masyn
Journal Oxford University Press eBooks
Year 2013
DOI
10.1093/oxfordhb/9780199934898.013.0025
URL
https://doi.org/10.1093/oxfordhb/9780199934898.013.0025
Keywords Keywords not found

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