Bayes estimates, Gibbs sampler, item response theory (IRT), Markov chain Monte Carlo, multilevel model, two-parameter normal ogive model,

This article is based on the first author's doctoral dissertation, written when he was a doctoral student at Tilburg University. The authors thank the anonymous JMR reviewers for their extremely useful and constructive comments. In addition, they thank AiMark for providing the data and gratefully acknowledge financial support from the Flemish Science Foundation (Grant No. G.0116.04).

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Despite growing international interest in the use of data to improve education, few studies examining the effects on student achievement are yet available. In the present study, the effects of a two-year data-based decisionmaking intervention on student achievement growth were investigated. Fifty-three primary schools participated in the project, and student achievement data were collected over the two years before and two years during the intervention. Linear mixed models were used to analyze the differential effect of data use on student achievement. A positive mean intervention effect was estimated, with an average effect of approximately one extra month of schooling. Furthermore, the results suggest that the intervention especially

Response times on test items are easily collected in modern computerized testing. When collecting both (binary) responses and (continuous) response times on test items, it is possible to measure the accuracy and speed of test takers. To study the relationships between these two constructs, the model is extended with a multivariate multilevel regression structure which allows the incorporation of covariates to explain the variance in speed and accuracy between individuals and groups of test takers. A Bayesian approach with Markov chain Monte Carlo (MCMC) computation enables straightforward estimation of all model parameters. Model-specific implementations of a Bayes factor (BF) and deviance information criterium (DIC) for model selection are proposed which are easily calculated as byproducts of the MCMC computation. Both results from simulation studies and real-data examples are given to illustrate several novel analyses possible with this modeling framework.