Work

An SEM Approach for the Evaluation of Intervention Effects Using Pre-Post-Post Designs

Year: 2009

Type: article

Abstract: This study analyzes latent change scores using latent curve models (LCMs) for evaluation research with pre–post–post designs. The article extends a recent article by Willoughby, Vandergrift, Blair, and Granger (2007) Willoughby, M., Vandergrift, N., Blair, C. and Granger, D. A. 2007. A structural equation modeling approach for the analysis of cortisol data collected using pre–post–post designs. Structural Equation Modeling: A Multidisciplinary Journal, 14: 125–145. [Crossref], [Web of Science ®] , [Google Scholar] on the use of LCMs for studies with pre–post–post designs, and demonstrates that intervention effects can be better tested using different parameterizations of LCMs. This study illustrates how to test the overall mean of a latent variable at the time of research interest, not just at baseline, as well as means of latent change variables between assessments, and introduces how individual differences in the referent outcome (i.e., Level 2 random effects) and measurement-specific residuals (i.e., Level 1 residuals) can be modeled and interpreted. Two intervention data examples are presented. This LCM approach to change is more advantageous than other methods for its handling of measurement errors and individual differences in response to treatment, avoiding unrealistic assumptions, and being more powerful and flexible.

Source: Structural Equation Modeling A Multidisciplinary Journal

DOI: 10.1080/10705510902751358

Authors: Eun Young Mun, Alexander von Eye, Helene R. White

Institutions: Rutgers, The State University of New Jersey, Michigan State University

Language: English

Topic: Psychometric Methodologies and Testing

Subfield: Management Science and Operations Research

Field: Decision Sciences

Domain: Social Sciences

Open Access status: green

Funder: National Institute on Drug Abuse

Award: DRUG ABUSE PREVENTION DURING DEVELOPMENTAL TRANSITIONS