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Kaplan, David; Su, Dan – Journal of Educational and Behavioral Statistics, 2016
This article presents findings on the consequences of matrix sampling of context questionnaires for the generation of plausible values in large-scale assessments. Three studies are conducted. Study 1 uses data from PISA 2012 to examine several different forms of missing data imputation within the chained equations framework: predictive mean…
Descriptors: Sampling, Questionnaires, Measurement, International Assessment
Azen, Razia; Traxel, Nicole – Journal of Educational and Behavioral Statistics, 2009
This article proposes an extension of dominance analysis that allows researchers to determine the relative importance of predictors in logistic regression models. Criteria for choosing logistic regression R[superscript 2] analogues were determined and measures were selected that can be used to perform dominance analysis in logistic regression. A…
Descriptors: Regression (Statistics), Predictor Variables, Measurement, Simulation
Moerbeek, Mirjam – Journal of Educational and Behavioral Statistics, 2008
Three issues need to be decided in the design stage of a longitudinal intervention study: the number of persons, the number of repeated measurements per person, and the duration of the study. The degree to which polynomial effects vary across persons and the drop-out pattern also influence the statistical power to detect intervention effects. This…
Descriptors: Intervention, Sample Size, Research Methodology, Longitudinal Studies
Schochet, Peter Z. – Journal of Educational and Behavioral Statistics, 2008
This article examines theoretical and empirical issues related to the statistical power of impact estimates for experimental evaluations of education programs. The author considers designs where random assignment is conducted at the school, classroom, or student level, and employs a unified analytic framework using statistical methods from the…
Descriptors: Elementary School Students, Research Design, Standardized Tests, Program Evaluation

Seltzer, Michael H.; And Others – Journal of Educational and Behavioral Statistics, 1996
The Gibbs sampling algorithms presented by M. H. Seltzer (1993) are fully generalized to a broad range of settings in which vectors of random regression parameters in the hierarchical model are assumed multivariate normally or multivariate "t" distributed across groups. The use of a fully Bayesian approach is discussed. (SLD)
Descriptors: Algorithms, Bayesian Statistics, Estimation (Mathematics), Multivariate Analysis