Supported by Grant K02 AA 00230 from NIAAA, by Grant R21 AA10948 from NIAAA, by Grant 016636 from ABMRF, and by SBIR Contract No. N43AA42008 from NIAAA.
Integrating Person-Centered and Variable-Centered Analyses: Growth Mixture Modeling With Latent Trajectory Classes
Article first published online: 11 APR 2006
Alcoholism: Clinical and Experimental Research
Volume 24, Issue 6, pages 882–891, June 2000
How to Cite
Muthén, B. and Muthén, L. K. (2000), Integrating Person-Centered and Variable-Centered Analyses: Growth Mixture Modeling With Latent Trajectory Classes. Alcoholism: Clinical and Experimental Research, 24: 882–891. doi: 10.1111/j.1530-0277.2000.tb02070.x
Presented at the Annual Meeting of the Research Society on Alcoholism, June 26–July 1, 1999, Santa Barbara, California.
The statistical program (Mplus) used for the analysis of data in this paper was developed by the authors and is commercially available through a private company, Muthén & Muthén, Los Angeles, CA, which they own.
- Issue published online: 11 APR 2006
- Article first published online: 11 APR 2006
- Received for publication November 5, 1999; accepted February 15, 2000.
- Latent Variables;
- Latent Trajectory Classes;
- Unobserved Heterogeneity;
- Developmental Pathways
Background: Many alcohol research questions require methods that take a person-centered approach because the interest is in finding heterogeneous groups of individuals, such as those who are susceptible to alcohol dependence and those who are not. A person-centered focus also is useful with longitudinal data to represent heterogeneity in developmental trajectories. In alcohol, drug, and mental health research the recognition of heterogeneity has led to theories of multiple developmental pathways.
Methods: This paper gives a brief overview of new methods that integrate variable- and person-centered analyses. Methods discussed include latent class analysis, latent transition analysis, latent class growth analysis, growth mixture modeling, and general growth mixture modeling. These methods are presented in a general latent variable modeling framework that expands traditional latent variable modeling by including not only continuous latent variables but also categorical latent variables.
Results: Four examples that use the National Longitudinal Survey of Youth (NLSY) data are presented to illustrate latent class analysis, latent class growth analysis, growth mixture modeling, and general growth mixture modeling. Latent class analysis of antisocial behavior found four classes. Four heavy drinking trajectory classes were found. The relationship between the latent classes and background variables and consequences was studied.
Conclusions: Person-centered and variable-centered analyses typically have been seen as different activities that use different types of models and software. This paper gives a brief overview of new methods that integrate variable- and person-centered analyses. The general framework makes it possible to combine these models and to study new models serving as a stimulus for asking research questions that have both person- and variable-centered aspects.