Modeling Latent Change Score Analysis and Extensions in Mplus: A Practical Guide for Researchers. Issue 1 (2nd January 2020)

• Record Type: Journal Article
• Authors: Klopack, Eric T.; Wickrama, Kandauda (K.A.S.)
• Is Part Of: Structural equation modeling. Volume 27:Issue 1(2020)
• Page Start: 97
• Page End: 110
• Publication Date: 2020-01-02
• Subjects: Growth modeling -- latent change scores -- life course -- longitudinal data analysis -- Mplus software; Multivariate analysis -- Periodicals; Social sciences -- Statistical methods -- Periodicals; 519.535
• DOI: 10.1080/10705511.2018.1562929
• Languages: English
• ISSNs: 1070-5511

Abstract

Abstract : Many developmental and life course researchers are interested in modeling dynamic developmental processes. Latent change score (LCS) modeling is a potentially powerful modeling technique that can be used to assess complex life course processes, as well as the direction of longitudinal bivariate associations. Advances in modeling software, like Mplus, as well as widening adoption of software by researchers has made LCS modeling simpler. Thus, in the present paper, we provide (1) a theoretical overview of LCS analysis, (2) information on the interpretation of these models, (3) a practical guide for estimating these models in Mplus (including example syntax), (4) illustrative examples of LCS analysis, and (5) potential caveats for researchers.