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Zamarro, Gema; Engberg, John; Saavedra, Juan Esteban; Steele, Jennifer – Journal of Research on Educational Effectiveness, 2015
This article investigates the use of teacher value-added estimates to assess the distribution of effective teaching across students of varying socioeconomic disadvantage in the presence of classroom composition effects. We examine, via simulations, how accurately commonly used teacher value-added estimators recover the rank correlation between…
Descriptors: Teacher Effectiveness, Disadvantaged Youth, Socioeconomic Influences, Socioeconomic Status
Harring, Jeffrey R.; Weiss, Brandi A.; Hsu, Jui-Chen – Psychological Methods, 2012
Two Monte Carlo simulations were performed to compare methods for estimating and testing hypotheses of quadratic effects in latent variable regression models. The methods considered in the current study were (a) a 2-stage moderated regression approach using latent variable scores, (b) an unconstrained product indicator approach, (c) a latent…
Descriptors: Structural Equation Models, Geometric Concepts, Computation, Comparative Analysis
Huang, Hung-Yu; Wang, Wen-Chung – Educational and Psychological Measurement, 2014
In the social sciences, latent traits often have a hierarchical structure, and data can be sampled from multiple levels. Both hierarchical latent traits and multilevel data can occur simultaneously. In this study, we developed a general class of item response theory models to accommodate both hierarchical latent traits and multilevel data. The…
Descriptors: Item Response Theory, Hierarchical Linear Modeling, Computation, Test Reliability
Jia, Yue; Stokes, Lynne; Harris, Ian; Wang, Yan – Journal of Educational and Behavioral Statistics, 2011
In this article, we consider estimation of parameters of random effects models from samples collected via complex multistage designs. Incorporation of sampling weights is one way to reduce estimation bias due to unequal probabilities of selection. Several weighting methods have been proposed in the literature for estimating the parameters of…
Descriptors: Sampling, Computation, Statistical Bias, Statistical Analysis
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

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