Review of Three Latent Class Cluster Analysis Packages: Latent Gold, poLCA, and MCLUST

This article reviews three software packages that can be used to perform latent class cluster analysis, namely, Latent Gold®, MCLUST, and poLCA and shows that each results in a different cluster solution and suggest some rationales for deciding which package to use.

Abstract

This article reviews three software packages that can be used to perform latent class cluster analysis, namely, Latent Gold®, MCLUST, and poLCA. Latent Gold® is a product of Statistical Innovations whereas MCLUST and poLCA are packages written in R and are available through the web site http://www.r-project.org. We use a single dataset and apply each software package to develop a latent class cluster analysis for the data. This allows us to compare the features and the resulting clusters from each software package. Each software package has its strengths and weaknesses and we compare the software from the perspectives of usability, cost, data characteristics, and performance. Whereas each software package utilizes the same methodology, we show that each results in a different cluster solution and suggest some rationales for deciding which package to use.