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Choi, Kilchan; Kim, Jinok – Journal of Educational and Behavioral Statistics, 2019
This article proposes a latent variable regression four-level hierarchical model (LVR-HM4) that uses a fully Bayesian approach. Using multisite multiple-cohort longitudinal data, for example, annual assessment scores over grades for students who are nested within cohorts within schools, the LVR-HM4 attempts to simultaneously model two types of…
Descriptors: Regression (Statistics), Hierarchical Linear Modeling, Longitudinal Studies, Cohort Analysis
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Losh, Molly; Martin, Gary E.; Lee, Michelle; Klusek, Jessica; Sideris, John; Barron, Sheila; Wassink, Thomas – Journal of Autism and Developmental Disorders, 2017
Genetic liability to autism spectrum disorder (ASD) can be expressed in unaffected relatives through subclinical, genetically meaningful traits, or endophenotypes. This study aimed to identify developmental endophenotypes in parents of individuals with ASD by examining parents' childhood academic development over the school-age period. A cohort of…
Descriptors: Genetics, Autism, Pervasive Developmental Disorders, Parents
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Warne, Russell T. – Gifted Child Quarterly, 2014
Above-level testing is the practice of administering aptitude or academic achievement tests that are designed for typical students in higher grades or older age-groups to gifted or high-achieving students. Although widely accepted in gifted education, above-level testing has not been subject to careful psychometric scrutiny. In this study, I…
Descriptors: Academically Gifted, Middle School Students, Longitudinal Studies, Hierarchical Linear Modeling