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George Leckie; Richard Parker; Harvey Goldstein; Kate Tilling – Journal of Educational and Behavioral Statistics, 2024
School value-added models are widely applied to study, monitor, and hold schools to account for school differences in student learning. The traditional model is a mixed-effects linear regression of student current achievement on student prior achievement, background characteristics, and a school random intercept effect. The latter is referred to…
Descriptors: Academic Achievement, Value Added Models, Accountability, Institutional Characteristics
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Lyu, Weicong; Kim, Jee-Seon; Suk, Youmi – Journal of Educational and Behavioral Statistics, 2023
This article presents a latent class model for multilevel data to identify latent subgroups and estimate heterogeneous treatment effects. Unlike sequential approaches that partition data first and then estimate average treatment effects (ATEs) within classes, we employ a Bayesian procedure to jointly estimate mixing probability, selection, and…
Descriptors: Hierarchical Linear Modeling, Bayesian Statistics, Causal Models, Statistical Inference
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Giada Spaccapanico Proietti; Mariagiulia Matteucci; Stefania Mignani; Bernard P. Veldkamp – Journal of Educational and Behavioral Statistics, 2024
Classical automated test assembly (ATA) methods assume fixed and known coefficients for the constraints and the objective function. This hypothesis is not true for the estimates of item response theory parameters, which are crucial elements in test assembly classical models. To account for uncertainty in ATA, we propose a chance-constrained…
Descriptors: Automation, Computer Assisted Testing, Ambiguity (Context), Item Response Theory
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Yamaguchi, Kazuhiro – Journal of Educational and Behavioral Statistics, 2023
Understanding whether or not different types of students master various attributes can aid future learning remediation. In this study, two-level diagnostic classification models (DCMs) were developed to represent the probabilistic relationship between external latent classes and attribute mastery patterns. Furthermore, variational Bayesian (VB)…
Descriptors: Bayesian Statistics, Classification, Statistical Inference, Sampling
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Monroe, Scott – Journal of Educational and Behavioral Statistics, 2021
This research proposes a new statistic for testing latent variable distribution fit for unidimensional item response theory (IRT) models. If the typical assumption of normality is violated, then item parameter estimates will be biased, and dependent quantities such as IRT score estimates will be adversely affected. The proposed statistic compares…
Descriptors: Item Response Theory, Simulation, Scores, Comparative Analysis
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Grund, Simon; Lüdtke, Oliver; Robitzsch, Alexander – Journal of Educational and Behavioral Statistics, 2021
Large-scale assessments (LSAs) use Mislevy's "plausible value" (PV) approach to relate student proficiency to noncognitive variables administered in a background questionnaire. This method requires background variables to be completely observed, a requirement that is seldom fulfilled. In this article, we evaluate and compare the…
Descriptors: Data Analysis, Error of Measurement, Research Problems, Statistical Inference
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Martin, Michael O.; Mullis, Ina V. S. – Journal of Educational and Behavioral Statistics, 2019
International large-scale assessments of student achievement such as International Association for the Evaluation of Educational Achievement's Trends in International Mathematics and Science Study (TIMSS) and Progress in International Reading Literacy Study and Organization for Economic Cooperation and Development's Program for International…
Descriptors: Achievement Tests, International Assessment, Mathematics Tests, Science Achievement