Social and Behavioral Sciences Commons^™

Mixture Latent Markov Modeling: Identifying And Predicting Unobserved Heterogeneity In Longitudinal Qualitative Status Change, Mo Wang, David Chan

Research Collection School of Social Sciences

There are many areas of organizational research where we may be concerned with subgroup differences in status change profiles. The purpose of this article is to illustrate, using a real data set on retirees' postretirement employment statuses (PES), how mixture latent Markov modeling may be applied to substantive research in organizational settings to identify population subgroups with varying status change profiles and examine their correlates, by modeling unobserved heterogeneity in longitudinal qualitative changes. Steps in the modeling process are highlighted and limitations, cautions, recommendations, and extensions of the technique are discussed.