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Direct linkByrnes, James P.; Miller-Cotto, Dana; Wang, Aubrey H. – Journal of Cognition and Development, 2018
As the United States experiences greater income inequality, more and more students experience an early science achievement gap. This study tested several competing theoretical models of early science achievement with a longitudinal sample of 14,624 children who were followed from kindergarten entry to the end of 1st grade. To understand why and…
Descriptors: Cognitive Development, Grade 1, Elementary School Students, Kindergarten
Shanley, Lina – Educational Researcher, 2016
Accurately measuring and modeling academic achievement growth is critical to support educational policy and practice. Using a nationally representative longitudinal data set, this study compared various models of mathematics achievement growth on the basis of both practical utility and optimal statistical fit and explored relationships within and…
Descriptors: Mathematics Achievement, Achievement Gains, Longitudinal Studies, Academic Achievement
Silinskas, Gintautas; Kiuru, Noona; Aunola, Kaisa; Lerkkanen, Marja-Kristiina; Nurmi, Jari-Erik – Developmental Psychology, 2015
This study investigated the longitudinal associations between children's academic performance and their mothers' affect, practices, and perceptions of their children in homework situations. The children's (n = 2,261) performance in reading and math was tested in Grade 1 and Grade 4, and the mothers (n = 1,476) filled out questionnaires on their…
Descriptors: Homework, Mothers, Parent Child Relationship, Academic Achievement
Wu, Wei; West, Stephen G. – Multivariate Behavioral Research, 2010
This study investigated the sensitivity of fit indices to model misspecification in within-individual covariance structure, between-individual covariance structure, and marginal mean structure in growth curve models. Five commonly used fit indices were examined, including the likelihood ratio test statistic, root mean square error of…
Descriptors: Goodness of Fit, Computation, Statistical Analysis, Structural Equation Models
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
Leite, Walter L.; Sandbach, Robert; Jin, Rong; MacInnes, Jann W.; Jackman, M. Grace-Anne – Structural Equation Modeling: A Multidisciplinary Journal, 2012
Because random assignment is not possible in observational studies, estimates of treatment effects might be biased due to selection on observable and unobservable variables. To strengthen causal inference in longitudinal observational studies of multiple treatments, we present 4 latent growth models for propensity score matched groups, and…
Descriptors: Structural Equation Models, Probability, Computation, Observation
