Latent Variable Modeling of Differences and Changes with Longitudinal Data

• Published in: Annual review of psychology Vol. 60; no. 1; pp. 577 - 605
• Main Author: MCARDLE, John J
• Format: Journal Article
• Language: English
• Published: Palo Alto, CA Annual Reviews 2009; Annual Reviews, Inc
• Subjects: Biological and medical sciences; Changes; Data analysis; Data Interpretation, Statistical; Differences; Factor Analysis, Statistical; Fundamental and applied biological sciences. Psychology; Humans; Longitudinal Studies; Measurement errors; Models, Statistical; Psychology; Psychology. Psychoanalysis. Psychiatry; Psychology. Psychophysiology; Psychometrics. Statistics. Methodology; Regression Analysis; Research Design - statistics & numerical data; Statistics. Mathematics; Human; Statistical method; Follow up study; linear structural equations; Repeated measurement; Latent variable model; Review; repeated measures
• ISSN: 0066-4308; 1545-2085
• DOI: 10.1146/annurev.psych.60.110707.163612

This review considers a common question in data analysis: What is the most useful way to analyze longitudinal repeated measures data? We discuss some contemporary forms of structural equation models (SEMs) based on the inclusion of latent variables. The specific goals of this review are to clarify basic SEM definitions, consider relations to classical models, focus on testable features of the new models, and provide recent references to more complete presentations. A broader goal is to illustrate why so many researchers are enthusiastic about the SEM approach to data analysis. We first outline some classic problems in longitudinal data analysis, consider definitions of differences and changes, and raise issues about measurement errors. We then present several classic SEMs based on the inclusion of invariant common factors and explain why these are so important. This leads to newer SEMs based on latent change scores, and we explain why these are useful.