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Mingya Huang; David Kaplan – Journal of Educational and Behavioral Statistics, 2025
The issue of model uncertainty has been gaining interest in education and the social sciences community over the years, and the dominant methods for handling model uncertainty are based on Bayesian inference, particularly, Bayesian model averaging. However, Bayesian model averaging assumes that the true data-generating model is within the…
Descriptors: Bayesian Statistics, Hierarchical Linear Modeling, Statistical Inference, Predictor Variables
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Trendtel, Matthias; Robitzsch, Alexander – Journal of Educational and Behavioral Statistics, 2021
A multidimensional Bayesian item response model is proposed for modeling item position effects. The first dimension corresponds to the ability that is to be measured; the second dimension represents a factor that allows for individual differences in item position effects called persistence. This model allows for nonlinear item position effects on…
Descriptors: Bayesian Statistics, Item Response Theory, Test Items, Test Format
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Casabianca, Jodi M.; Lewis, Charles – Journal of Educational and Behavioral Statistics, 2018
The null hypothesis test used in differential item functioning (DIF) detection tests for a subgroup difference in item-level performance--if the null hypothesis of "no DIF" is rejected, the item is flagged for DIF. Conversely, an item is kept in the test form if there is insufficient evidence of DIF. We present frequentist and empirical…
Descriptors: Test Bias, Hypothesis Testing, Bayesian Statistics, Statistical Analysis
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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
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Depaoli, Sarah; Clifton, James P.; Cobb, Patrice R. – Journal of Educational and Behavioral Statistics, 2016
A review of the software Just Another Gibbs Sampler (JAGS) is provided. We cover aspects related to history and development and the elements a user needs to know to get started with the program, including (a) definition of the data, (b) definition of the model, (c) compilation of the model, and (d) initialization of the model. An example using a…
Descriptors: Monte Carlo Methods, Markov Processes, Computer Software, Models