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Download full textJoao M. Souto-Maior; Kenneth A. Shores; Rachel E. Fish – Annenberg Institute for School Reform at Brown University, 2025
Whether selection processes contribute to group-level disparities or merely reflect pre-existing inequalities is an important societal question. In the context of observational data, researchers, concerned about omitted-variable bias, assess selection-contributing inequality via a kitchen-sink approach, comparing selection outcomes of…
Descriptors: Control Groups, Predictor Variables, Correlation, Selection Criteria
Peer reviewed
Direct linkPreacher, Kristopher J.; Zhang, Zhen; Zyphur, Michael J. – Structural Equation Modeling: A Multidisciplinary Journal, 2011
Multilevel modeling (MLM) is a popular way of assessing mediation effects with clustered data. Two important limitations of this approach have been identified in prior research and a theoretical rationale has been provided for why multilevel structural equation modeling (MSEM) should be preferred. However, to date, no empirical evidence of MSEM's…
Descriptors: Data, Structural Equation Models, Statistical Analysis, Computation
Kelava, Augustin; Nagengast, Benjamin – Multivariate Behavioral Research, 2012
Structural equation models with interaction and quadratic effects have become a standard tool for testing nonlinear hypotheses in the social sciences. Most of the current approaches assume normally distributed latent predictor variables. In this article, we present a Bayesian model for the estimation of latent nonlinear effects when the latent…
Descriptors: Bayesian Statistics, Computation, Structural Equation Models, Predictor Variables
Bauer, Daniel J.; Baldasaro, Ruth E.; Gottfredson, Nisha C. – Structural Equation Modeling: A Multidisciplinary Journal, 2012
Structural equation models are commonly used to estimate relationships between latent variables. Almost universally, the fitted models specify that these relationships are linear in form. This assumption is rarely checked empirically, largely for lack of appropriate diagnostic techniques. This article presents and evaluates two procedures that can…
Descriptors: Structural Equation Models, Mixed Methods Research, Statistical Analysis, Sampling
Reichardt, Charles S. – Multivariate Behavioral Research, 2011
Maxwell, Cole, and Mitchell (2011) demonstrated that simple structural equation models, when used with cross-sectional data, generally produce biased estimates of meditated effects. I extend those results by showing how simple structural equation models can produce biased estimates of meditated effects when used even with longitudinal data. Even…
Descriptors: Structural Equation Models, Statistical Data, Longitudinal Studies, Error of Measurement
Mayer, Matthew J. – Journal of School Violence, 2004
Methodological challenges associated with structural equation modeling (SEM) and structured means modeling (SMM) in research on school violence and related topics in the social and behavioral sciences are examined. Problems associated with multiyear implementations of large-scale surveys are discussed. Complex sample designs, part of any…
Descriptors: Violence, Structural Equation Models, Research Methodology, Measures (Individuals)
