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Rohit Batra; Silvia A. Bunge; Emilio Ferrer – Structural Equation Modeling: A Multidisciplinary Journal, 2022
Studying development processes, as they unfold over time, involves collecting repeated measures from individuals and modeling the changes over time. One methodological challenge in this type of longitudinal data is separating retest effects, due to the repeated assessments, from developmental processes such as maturation or age. In this article,…
Descriptors: Children, Adolescents, Longitudinal Studies, Test Reliability
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Blozis, Shelley A.; Ge, Xiaojia; Xu, Shu; Natsuaki, Misaki N.; Shaw, Daniel S.; Neiderhiser, Jenae M.; Scaramella, Laura V.; Leve, Leslie D.; Reiss, David – Structural Equation Modeling: A Multidisciplinary Journal, 2013
Missing data are common in studies that rely on multiple informant data to evaluate relationships among variables for distinguishable individuals clustered within groups. Estimation of structural equation models using raw data allows for incomplete data, and so all groups can be retained for analysis even if only 1 member of a group contributes…
Descriptors: Data, Structural Equation Models, Correlation, Data Analysis
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Castro-Schilo, Laura; Widaman, Keith F.; Grimm, Kevin J. – Structural Equation Modeling: A Multidisciplinary Journal, 2013
In 1959, Campbell and Fiske introduced the use of multitrait-multimethod (MTMM) matrices in psychology, and for the past 4 decades confirmatory factor analysis (CFA) has commonly been used to analyze MTMM data. However, researchers do not always fit CFA models when MTMM data are available; when CFA modeling is used, multiple models are available…
Descriptors: Multitrait Multimethod Techniques, Factor Analysis, Structural Equation Models, Monte Carlo Methods