Finding latent groups in observed data: A primer on latent profile analysis in Mplus for applied researchers

Conducting Confirmatory Latent Class Analysis Using Mplus

This article may be used for research, teaching, and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form to anyone is expressly forbidden. The publisher does not give any warranty express or implied or make any representation that the contents will be complete or accurate or up to date...

Plausible Values for Latent Variables Using Mplus

Modeling Interactions Between Latent and Observed Continuous Variables Using Maximum-Likelihood Estimation In Mplus

Modeling with random slopes is used in random coefficient regression, multilevel regression, and growth modeling. Random slopes can be seen as continuous latent variables. Recently, a flexible modeling framework has been implemented in the Mplus program to do modeling with such latent variables combined with modeling of psychometric constructs, typically referred to as factors, measured by mult...

Bayesian Analysis of Latent Variable Models using Mplus

Mixture models: latent profile and latent class analysis

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 conti...