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Gao, Xuliang; Ma, Wenchao; Wang, Daxun; Cai, Yan; Tu, Dongbo – Journal of Educational and Behavioral Statistics, 2021
This article proposes a class of cognitive diagnosis models (CDMs) for polytomously scored items with different link functions. Many existing polytomous CDMs can be considered as special cases of the proposed class of polytomous CDMs. Simulation studies were carried out to investigate the feasibility of the proposed CDMs and the performance of…
Descriptors: Cognitive Measurement, Models, Test Items, Scoring
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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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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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Ma, Wenchao; de la Torre, Jimmy – Journal of Educational and Behavioral Statistics, 2019
Solving a constructed-response item usually requires successfully performing a sequence of tasks. Each task could involve different attributes, and those required attributes may be "condensed" in various ways to produce the responses. The sequential generalized deterministic input noisy "and" gate model is a general cognitive…
Descriptors: Test Items, Cognitive Measurement, Models, Hypothesis Testing
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Jeon, Minjeong; De Boeck, Paul; van der Linden, Wim – Journal of Educational and Behavioral Statistics, 2017
We present a novel application of a generalized item response tree model to investigate test takers' answer change behavior. The model allows us to simultaneously model the observed patterns of the initial and final responses after an answer change as a function of a set of latent traits and item parameters. The proposed application is illustrated…
Descriptors: Item Response Theory, Behavior, Mathematics Tests, Change
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Liao, Xiangyi; Bolt, Daniel M. – Journal of Educational and Behavioral Statistics, 2021
Four-parameter models have received increasing psychometric attention in recent years, as a reduced upper asymptote for item characteristic curves can be appealing for measurement applications such as adaptive testing and person-fit assessment. However, applications can be challenging due to the large number of parameters in the model. In this…
Descriptors: Test Items, Models, Mathematics Tests, Item Response Theory
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Bolsinova, Maria; Tijmstra, Jesper – Journal of Educational and Behavioral Statistics, 2016
Conditional independence (CI) between response time and response accuracy is a fundamental assumption of many joint models for time and accuracy used in educational measurement. In this study, posterior predictive checks (PPCs) are proposed for testing this assumption. These PPCs are based on three discrepancy measures reflecting different…
Descriptors: Reaction Time, Accuracy, Statistical Analysis, Robustness (Statistics)
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Rijmen, Frank; Jeon, Minjeong; von Davier, Matthias; Rabe-Hesketh, Sophia – Journal of Educational and Behavioral Statistics, 2014
Second-order item response theory models have been used for assessments consisting of several domains, such as content areas. We extend the second-order model to a third-order model for assessments that include subdomains nested in domains. Using a graphical model framework, it is shown how the model does not suffer from the curse of…
Descriptors: Item Response Theory, Models, Educational Assessment, Computation
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Furno, Marilena – Journal of Educational and Behavioral Statistics, 2011
The article considers a test of specification for quantile regressions. The test relies on the increase of the objective function and the worsening of the fit when unnecessary constraints are imposed. It compares the objective functions of restricted and unrestricted models and, in its different formulations, it verifies (a) forecast ability, (b)…
Descriptors: Goodness of Fit, Statistical Inference, Regression (Statistics), Least Squares Statistics
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Cho, Sun-Joo; Cohen, Allan S. – Journal of Educational and Behavioral Statistics, 2010
Mixture item response theory models have been suggested as a potentially useful methodology for identifying latent groups formed along secondary, possibly nuisance dimensions. In this article, we describe a multilevel mixture item response theory (IRT) model (MMixIRTM) that allows for the possibility that this nuisance dimensionality may function…
Descriptors: Simulation, Mathematics Tests, Item Response Theory, Student Behavior
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von Davier, Matthias; Sinharay, Sandip – Journal of Educational and Behavioral Statistics, 2010
This article presents an application of a stochastic approximation expectation maximization (EM) algorithm using a Metropolis-Hastings (MH) sampler to estimate the parameters of an item response latent regression model. Latent regression item response models are extensions of item response theory (IRT) to a latent variable model with covariates…
Descriptors: Item Response Theory, Statistical Analysis, Regression (Statistics), Models
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Soares, Tufi M.; Goncalves, Flavio B.; Gamerman, Dani – Journal of Educational and Behavioral Statistics, 2009
In this article, an integrated Bayesian model for differential item functioning (DIF) analysis is proposed. The model is integrated in the sense of modeling the responses along with the DIF analysis. This approach allows DIF detection and explanation in a simultaneous setup. Previous empirical studies and/or subjective beliefs about the item…
Descriptors: Test Bias, Bayesian Statistics, Models, Item Response Theory