Education 231C

Applied Categorical & Nonnormal Data Analysis

Latent Class Analysis Example

Latent Class Analysis is a type of latent variable analysis in which the observed predictor variables are categorical and the latent (unobserved) response variable is also categorical. More formally, latent class analysis is a statistical method for finding subtypes of related cases (latent classes) from multivariate categorical data. In a sense, latent class analysis is like cluster analysis, in that, it attempts to find groups or classes of observations that are similar to one another.

MI Example

Four diagnostic criteria binary indicators

The more indicators present the greater the likelihood on an MI.

Now that the data are organized the way we want, we can begin the latent class analysis. The option nip(2) indicates that we want two latent classes. Class 2 is the class most likely to have an MI (Pr = .4578) with an expected count of 43.0332.

If you wish to see the latent class coefficients along with their standard errors, try this code:

We can obtain the predicted probability for each pattern of being in a class, Pr(c=1|yj) and Pr(c=2|yj), using the gllapred command. We can classify the observation into the latent classes based upon which class has the larger probability. We can also use gllapred to compute the conditional response probabilities, in particular, Pr(yij=1|c=2), also know as the sensitivity. Next, we will use gllapred to obtain the expected counts for each of the patterns.

Categorical Data Analysis Course

Phil Ender