poLCA: Polytomous Variable Latent Class Analysis Version 1.2

Abstract

poLCA is a software package for the estimation of latent class and latent class regression models for polytomous outcome variables, implemented in the R statistical computing environment. Both models can be called using a single simple command line. The basic latent class model is a finite mixture model in which the component distributions are assumed to be multi-way cross-classification tables with all variables mutually independent. The latent class regression model further enables the researcher to estimate the effects of covariates on predicting latent class membership. poLCA uses expectation-maximization and NewtonRaphson algorithms to find maximum likelihood estimates of the model parameters. This user’s guide to the poLCA software package draws extensively from Linzer and Lewis (Forthcoming).

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@inproceedings{Linzer2007poLCAPV, title={poLCA: Polytomous Variable Latent Class Analysis Version 1.2}, author={Drew A. Linzer and Jeffrey B. Lewis}, year={2007} }