Auxiliary Variables in Mixture Modeling: 3-Step Approaches Using Mplus

Auxiliary Variables in Mixture Modeling: A 3-Step Approach Using Mplus

Auxiliary Variables in Mixture Modeling: Using the BCH Method in Mplus to Estimate a Distal Outcome Model and an Arbitrary Secondary Model

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...

An Introduction to Latent Class Growth Analysis and Growth Mixture Modeling

In recent years, there has been a growing interest among researchers in the use of latent class and growth mixture modeling techniques for applications in the social and psychological sciences, in part due to advances in and availability of computer software designed for this purpose (e.g., Mplus and SAS Proc Traj). Latent growth modeling approaches, such as latent class growth analysis (LCGA) ...

Relating LCA Results 1 Running Head: Relating LCA Results Relating Latent Class Analysis Results to Variables not Included in the Analysis

An important interest in mixture modeling is the investigation of what types of individuals belong to each latent class by relating classes to covariates, concurrent outcomes and distal outcomes, also known as auxiliary variables. This article presents results from real data examples and simulations to show how various factors, such as the degree to which people are classified correctly into la...