TY - JOUR
T1 - An SEM approach for the evaluation of intervention effects using pre-post-post designs
AU - Mun, Eun Young
AU - von Eye, Alexander
AU - White, Helene R.
N1 - Funding Information:
This study was funded by the National Institute on Drug Abuse (DA 17552) as part of the Rutgers Transdiscplinary Prevention Research Center. We thank Tenko Raykov for helpful comments, and Tom Morgan, Lisa Laitman, Barbara Kachur, Brian Kaye, Malina Spirito, Sara Fink, Corey Grassl, Lisa Pugh, Kelly Pugh, and Adam Thacker for their help with data collection.
PY - 2009/4
Y1 - 2009/4
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=70449637594&partnerID=8YFLogxK
U2 - 10.1080/10705510902751358
DO - 10.1080/10705510902751358
M3 - Article
AN - SCOPUS:70449637594
SN - 1070-5511
VL - 16
SP - 315
EP - 337
JO - Structural Equation Modeling
JF - Structural Equation Modeling
IS - 2
ER -