Mixture Models: Latent Profile and Latent Class Analysis

  • Daniel Oberski
Part of the Human–Computer Interaction Series book series (HCIS)


Latent class analysis (LCA) and latent profile analysis (LPA) are techniques that aim to recover hidden groups from observed data. They are similar to clustering techniques but more flexible because they are based on an explicit model of the data, and allow you to account for the fact that the recovered groups are uncertain. LCA and LPA are useful when you want to reduce a large number of continuous (LPA) or categorical (LCA) variables to a few subgroups. They can also help experimenters in situations where the treatment effect is different for different people, but we do not know which people. This chapter explains how LPA and LCA work, what assumptions are behind the techniques, and how you can use R to apply them.


Posterior Probability Latent Class Latent Class Analysis Class Membership Latent Dirichlet Allocation 
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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Tilburg UniversityTilburgThe Netherlands

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