Multivariate Latent Growth Modeling: Issues on Preliminary Data Analyses

Author

David CHAN, Singapore Management UniversityFollow

Publication Type

Book Chapter

Publication Date

1-2005

Abstract

Multivariate latent growth modeling (multivariate LGM) provides a flexible data analytic framework for representing and assessing cross-domain (i.e., between-constructs) relationships in intraindividual changes over time, which also allows incorporation of multiple levels of analysis. Using the chapter by Cortina, Pant, and Smith-Darden (this volume) as a point of departure, this chapter discusses important preliminary data analysis and interpretation issues prior to performing multivariate LGM analyses.

Discipline

Quantitative Psychology

Research Areas

Psychology

Publication

Multi-Level Issues in Strategy and Methods

Volume

4

Editor

Dansereau, Fred; Yammarino, Francis J.

First Page

319

Last Page

334

ISBN

9780762311842

Identifier

10.1016/s1475-9144(05)04014-2

Publisher

Emerald

City or Country

Bingley

Citation

CHAN, David. (2005). Multivariate Latent Growth Modeling: Issues on Preliminary Data Analyses. In Multi-Level Issues in Strategy and Methods (pp. 319-334). Bingley: Emerald.

Available at: https://ink.library.smu.edu.sg/soss_research/88

Additional URL

https://doi.org/10.1016/s1475-9144(05)04014-2