Is there any “stepwise” inclusion feature in the LC regression module?
A. No. Since the latent classes may be highly dependent on the predictors that are included, stepwise features have not been implemented in the latent class regression module. Q. I have a binary dependent variable and five categorical independent variables. I am using Latent GOLD® to find 3 segments among the respondents. The Parameters output shows separate estimates for each segment. However, there appears to be both intercepts as well as betas for dependent variable. I am confused about how to use both of them in terms of predicting. A. The ‘gamma’ parameters labeled Intercept (and other gamma parameters that would appear if you have covariates) refer to the model to predict the latent variable classes as a function of the covariates. If no covariates are included in the model only the Intercept appears under the label (gamma). Beneath the gamma parameters, the parameters labeled ‘beta’ appear. These refer to the model to predict the dependent variable (which including the dependent
