Can you use continuous variables in latent class analysis?

Can you use continuous variables in latent class analysis?

2.0 Using both categorical and continuous predictor variables. When modeling latent variables, you can use any combination of categorical and continuous variables. In this example, we will use both categorical and continuous variables.

How many variables are there in latent class analysis?

When we estimated the latent class model based on all thirteen variables, BIC selected a two-class model. Since we simulated the data and hence know the actual membership of each point, we can compare the correct classification with that produced by the model estimated using all the variables.

What is a categorical latent variable?

The latent variable (classes) is categorical, but the indicators may be either categorical or continuous. The term latent class analysis is often used to refer to a mixture model in which all of the observed indicator variables are categorical.

What is LCA in statistics?

Latent Class Analysis (LCA) is a statistical method for identifying unmeasured class membership among subjects using categorical and/or continuous observed variables. For example, you may wish to categorize people based on their drinking behaviors (observations) into different types of drinkers (latent classes).

What is Mendell Rubin?

The Lo-Mendell-Rubin likelihood ratio test (LMR; also known as Vong-Lo-Mendell-Rubin test) and the ad hoc adjusted LMR (Lo, Mendell, & Rubin, 2001) were derived to use the adjusted asymptotic distribution of the likelihood ratio statistics to compare a K0-class normal mixture distribution model against an alternative K …

How do you model latent variables?

A latent variable model is a statistical model that relates a set of observable variables (so-called manifest variables) to a set of latent variables….Latent variable model.