This Article Full Text (PDF) References Alert me when this article is cited Alert me if a correction is posted Services Email this article to a friend Similar articles in this journal Alert me to new issues of the journal Add to Saved Citations Download to citation manager Request Permissions Request Reprints Add to My Marked Citations Citing Articles Citing Articles via Scopus Google Scholar Articles by Weakliem, D. L. Articles by Entner Wright, B. R. Social Bookmarking                         What's this?

Robustness of Group-Based Models for Longitudinal Count Data

University of Connecticut, Storrs, David.Weakliem{at}uconn.edu

Bradley R. Entner Wright

University of Connecticut, Storrs

In recent years, there have been efforts to develop latent class models for trajectories. The semiparametric mixed Poisson regression (SMPR) model has been used in many empirical studies, but there have been few attempts to evaluate the robustness of the estimates from this model. The authors use simulated data to evaluate the performance of the SMPR model under a variety of assumptions. They find that estimates are sensitive to the conditional distribution of counts and misspecification of the shape of the trajectory. When there is only one underlying trajectory and overdispersion is present, the SMPR model frequently finds multiple groups, which often appear to differ in shape as well as level. The tendency can be substantially reduced by use of the zero-inflated Poisson distribution in conjunction with top-coding of large counts. The article concludes with a discussion of other extensions and alternatives to the standard SMPR model that might provide more robust estimates.

Key Words: semiparametric mixed Poisson regression • overdispersion • robustness • latent class models

Sociological Methods & Research, Vol. 38, No. 1, 147-170 (2009)
DOI: 10.1177/0049124109339368


CiteULike    Complore    Connotea    Del.icio.us    Digg    Reddit    Technorati    Twitter    What's this?