When to use factor analysis for latent variables?

When to use factor analysis for latent variables?

Factor Analysis – Because the term “latent variable” is used, you might be tempted to use factor analysis since that is a technique used with latent variables. However, factor analysis is used for continuous and usually normally distributed latent variables, where this latent variable, e.g., alcoholism, is categorical.

What’s the difference between mixture and latent class analysis?

A mixture model with categorical variables is called latent class analysis, whereas a mixture model with only continuous variables is called a latent profile analysis (Oberski, 2016). Note: Mplus version 8 was used for these examples.

How to do a latent class analysis ( LCA )?

We will illustrate a simple latent class analysis (LCA) using the mplus73recode.dat dataset and see if we can identify two classes based on four binary variables.

What does the usevariables statement in Mplus mean?

The Mplus Program. The usevariables statement indicates which variables we will use for this analysis. The categorical statement indicates that the specified variables are categorical variables. The classes statement indicates that there is one categorical latent variable (which we will call c ), and it has 3 levels.

Which is the best method for latent class analysis?

Before we show how you can analyze this with Latent Class Analysis, let’s consider some other methods that you might use: Cluster Analysis – You could use cluster analysis for data like these. However, cluster analysis is not based on a statistical model.

How many latent classes are there in high school?

Determine whether three latent classes is the right number of classes (i.e., are there only two types of drinkers or perhaps are there as many as four types of drinkers). High school students vary in their success in school.

Is the degree of success in high school a latent variable?

A traditional way to conceptualize this might be to view “degree of success in high school” as a latent variable (one that you cannot directly measure) that is normally distributed.