An SEM approach for the evaluation of intervention effects using pre-post-post designs

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

Research output: Contribution to journalArticlepeer-review

30 Scopus citations


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) 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.

Original languageEnglish (US)
Pages (from-to)315-337
Number of pages23
JournalStructural Equation Modeling
Issue number2
StatePublished - Apr 2009

All Science Journal Classification (ASJC) codes

  • General Decision Sciences
  • General Economics, Econometrics and Finance
  • Sociology and Political Science
  • Modeling and Simulation


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