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Lee, Daniel Y.; Harring, Jeffrey R. – Journal of Educational and Behavioral Statistics, 2023
A Monte Carlo simulation was performed to compare methods for handling missing data in growth mixture models. The methods considered in the current study were (a) a fully Bayesian approach using a Gibbs sampler, (b) full information maximum likelihood using the expectation-maximization algorithm, (c) multiple imputation, (d) a two-stage multiple…
Descriptors: Monte Carlo Methods, Research Problems, Statistical Inference, Bayesian Statistics
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Bradlow, Eric T. – Journal of Educational and Behavioral Statistics, 2003
In this article, the author comments on an article by Dunn, Kadane, and Garrow, "Comparing Harm Done by Mobility and Class Absence: Missing Students and Missing Data." He believes the research reported in that article should serve as a model for future applications of Bayesian methods in important educational research problems. The author lauds…
Descriptors: Research Problems, Educational Research, Bayesian Statistics, Researchers
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May, Henry – Journal of Educational and Behavioral Statistics, 2006
In this article, a new method is presented and implemented for deriving a scale of socioeconomic status (SES) from international survey data using a multilevel Bayesian item response theory (IRT) model. The proposed model incorporates both international anchor items and nation-specific items and is able to (a) produce student family SES scores…
Descriptors: Item Response Theory, Bayesian Statistics, Socioeconomic Status, Scaling