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Grund, Simon; Lüdtke, Oliver; Robitzsch, Alexander – Journal of Educational and Behavioral Statistics, 2023
Multiple imputation (MI) is a popular method for handling missing data. In education research, it can be challenging to use MI because the data often have a clustered structure that need to be accommodated during MI. Although much research has considered applications of MI in hierarchical data, little is known about its use in cross-classified…
Descriptors: Educational Research, Data Analysis, Error of Measurement, Computation
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Gitelman, Alix I. – Journal of Educational and Behavioral Statistics, 2005
In group-allocation studies for comparing behavioral, social, or educational interventions, subjects in the same group necessarily receive the same treatment, whereby a group and/or group-dynamic effect can confound the treatment effect. General counterfactual outcomes that depend on group characteristics, group membership, and treatment are…
Descriptors: Computation, Causal Models, Intervention, Group Membership
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Briggs, Derek C. – Journal of Educational and Behavioral Statistics, 2004
In the social sciences, evaluating the effectiveness of a program or intervention often leads researchers to draw causal inferences from observational research designs. Bias in estimated causal effects becomes an obvious problem in such settings. This article presents the Heckman Model as an approach sometimes applied to observational data for the…
Descriptors: Social Science Research, Statistical Inference, Causal Models, Test Bias
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Sinharay, Sandip – Journal of Educational and Behavioral Statistics, 2006
Bayesian networks are frequently used in educational assessments primarily for learning about students' knowledge and skills. There is a lack of works on assessing fit of Bayesian networks. This article employs the posterior predictive model checking method, a popular Bayesian model checking tool, to assess fit of simple Bayesian networks. A…
Descriptors: Models, Educational Assessment, Diagnostic Tests, Evaluation Methods