How does latent class regression analysis compare with traditional regression modeling?
A. There are 2 primary kinds of differences. First, the particular regression is automatically determined according to the scale type of the dependent variable. For continuous, the traditional linear regression is employed; for dichotomous, logistic regression; for ordinal, the baseline/adjacent category logit extension; for nominal, multinomial logit; for count, Poisson regression. models are used. For example, for dichotomous dependent variables, the logistic regression model is used. Second, LC Regression is a mixture model and hence is more general than traditional regression. The special case of 1-class corresponds to the homogeneous population assumption made in traditional regression. In LC regression, separate regressions are estimated simultaneously for each latent class. I need a mixture modeling program that can handle dependent variables that are dichotomous as well as continuous. Does Latent GOLD® handle this? A. Yes.
