Latent Growth and Dynamic Structural Equation Models

Posted: 15 Jun 2018

See all articles by Kevin J. Grimm

Kevin J. Grimm

Arizona State University (ASU) - Department of Psychology

Nilam Ram

German Institute for Economic Research (DIW Berlin); Pennsylvania State University

Date Written: May 2018

Abstract

Latent growth models make up a class of methods to study within-person change—how it progresses, how it differs across individuals, what are its determinants, and what are its consequences. Latent growth methods have been applied in many domains to examine average and differential responses to interventions and treatments. In this review, we introduce the growth modeling approach to studying change by presenting different models of change and interpretations of their model parameters. We then apply these methods to examining sex differences in the development of binge drinking behavior through adolescence and into adulthood. Advances in growth modeling methods are then discussed and include inherently nonlinear growth models, derivative specification of growth models, and latent change score models to study stochastic change processes. We conclude with relevant design issues of longitudinal studies and considerations for the analysis of longitudinal data.

Suggested Citation

Grimm, Kevin J. and Ram, Nilam, Latent Growth and Dynamic Structural Equation Models (May 2018). Annual Review of Clinical Psychology, Vol. 14, pp. 55-89, 2018, Available at SSRN: https://ssrn.com/abstract=3195840 or http://dx.doi.org/10.1146/annurev-clinpsy-050817-084840

Kevin J. Grimm

Arizona State University (ASU) - Department of Psychology ( email )

950 S. McAllister Ave
P. O. Box 871104
Tempe, AZ 85287-1104
United States

Nilam Ram (Contact Author)

German Institute for Economic Research (DIW Berlin) ( email )

Mohrenstraße 58
Berlin, 10117
Germany

Pennsylvania State University ( email )

University Park
State College, PA 16802
United States

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Abstract Views
121
PlumX Metrics