Work

On Inclusion of Covariates for Class Enumeration of Growth Mixture Models

Year: 2011

Type: article

Abstract: In this article, we directly questioned the common practice in growth mixture model (GMM) applications that exclusively rely on the fitting model without covariates for GMM class enumeration. We provided theoretical and simulation evidence to demonstrate that exclusion of covariates from GMM class enumeration could be problematic in many cases. Based on our findings, we provided recommendations for examining the class enumeration by the fitting model without covariates and discussed the potential of covariate inclusion as a remedy for the weakness of GMM class enumeration without including covariates. A real example on the development of children's cumulative exposure to risk factors for adolescent substance use was provided to illustrate our methodological developments.

Source: Multivariate Behavioral Research

DOI: 10.1080/00273171.2011.556549

Authors: Libo Li, Yih-Ing Hser

Institutions: University of California, Los Angeles, University of California System

Language: English

Topic: Psychometric Methodologies and Testing

Subfield: Management Science and Operations Research

Field: Decision Sciences

Domain: Social Sciences

Open Access status: green

Funders: National Institutes of Health, National Institute on Drug Abuse

Awards: G1665651649, Center for Advancing Longtiudinal Drug Abuse Research, Drug Abuse: Epidemiology, Treatment Process, & Outcomes