TY - JOUR
T1 - Residual structures in latent growth curve modeling
AU - Grimm, Kevin J.
AU - Widaman, Keith F.
N1 - Funding Information:
This research was supported by a National Science Foundation REECE Program Grant (DRL-0815787) and the National Center for Research on Early Childhood Education, Institute of Education Sciences, U.S. Department of Education (R305A06021). The opinions and views expressed in this article are those of the authors and do not necessarily represent the views and opinions of the U.S. Department of Education.
PY - 2010
Y1 - 2010
N2 - Several alternatives are available for specifying the residual structure in latent growth curve modeling. Two specifications involve uncorrelated residuals and represent the most commonly used residual structures. The first, building on repeated measures analysis of variance and common specifications in multilevel models, forces residual variances to be invariant across measurement occasions. The second, generally arising from structural equation modeling perspectives, allows residual variances to be freely estimated across occasions. Usually an afterthought, alternate specifications of the residual structure can have sizable effects on variance and covariance estimates for the intercept and slope factors (Ferron, Dailey, & Yi, 2002; Kwok, West, & Green, 2007; Sivo, Fan, & Witta, 2005), the fit of the model, and model convergence. We propose additional residual structures in latent growth modeling arising from ideas regarding growth curve reliability. These structures allow residual variances to change across time, but in constrained ways. We provide three illustrations and highlight the importance of focusing on residual structures in latent growth curve modeling.
AB - Several alternatives are available for specifying the residual structure in latent growth curve modeling. Two specifications involve uncorrelated residuals and represent the most commonly used residual structures. The first, building on repeated measures analysis of variance and common specifications in multilevel models, forces residual variances to be invariant across measurement occasions. The second, generally arising from structural equation modeling perspectives, allows residual variances to be freely estimated across occasions. Usually an afterthought, alternate specifications of the residual structure can have sizable effects on variance and covariance estimates for the intercept and slope factors (Ferron, Dailey, & Yi, 2002; Kwok, West, & Green, 2007; Sivo, Fan, & Witta, 2005), the fit of the model, and model convergence. We propose additional residual structures in latent growth modeling arising from ideas regarding growth curve reliability. These structures allow residual variances to change across time, but in constrained ways. We provide three illustrations and highlight the importance of focusing on residual structures in latent growth curve modeling.
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U2 - 10.1080/10705511.2010.489006
DO - 10.1080/10705511.2010.489006
M3 - Article
AN - SCOPUS:77954446028
VL - 17
SP - 424
EP - 442
JO - Structural Equation Modeling
JF - Structural Equation Modeling
SN - 1070-5511
IS - 3
ER -