Scale and preference heterogeneity will be confounded in a simple latent class MNL model. Two approaches for handling this are • Scale-adjusted latent class (SALC) which estimates scale classes and preference classes independently, so that each respondent is a member both of a preference class and a scale class. • Sawtooth Software’s scale-constrained latent class (where we use the standard deviation across the vector of utilities for each group as a proxy for scale) attempts to equalize scale during latent class MNL estimation to unconfound scale and preference heterogeneity Examining two artificial data sets, both with known preference heterogeneity (one with known continuous scale heterogeneity and one with known scale classes) we’ll test which approach does a better job of identifying the known preference classes and of putting the right respondents into the right preference classes.
