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Wang, Chun; Lu, Jing – Journal of Educational and Behavioral Statistics, 2021
In cognitive diagnostic assessment, multiple fine-grained attributes are measured simultaneously. Attribute hierarchies are considered important structural features of cognitive diagnostic models (CDMs) that provide useful information about the nature of attributes. Templin and Bradshaw first introduced a hierarchical diagnostic classification…
Descriptors: Cognitive Measurement, Models, Vertical Organization, Classification
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Park, Soojin; Esterling, Kevin M. – Journal of Educational and Behavioral Statistics, 2021
The causal mediation literature has developed techniques to assess the sensitivity of an inference to pretreatment confounding, but these techniques are limited to the case of a single mediator. In this article, we extend sensitivity analysis to possible violations of pretreatment confounding in the case of multiple mediators. In particular, we…
Descriptors: Statistical Analysis, Research Design, Influences, Anxiety
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Wu, Edward; Gagnon-Bartsch, Johann A. – Journal of Educational and Behavioral Statistics, 2021
In paired experiments, participants are grouped into pairs with similar characteristics, and one observation from each pair is randomly assigned to treatment. The resulting treatment and control groups should be well-balanced; however, there may still be small chance imbalances. Building on work for completely randomized experiments, we propose a…
Descriptors: Experiments, Groups, Research Design, Statistical Analysis
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Shear, Benjamin R.; Reardon, Sean F. – Journal of Educational and Behavioral Statistics, 2021
This article describes an extension to the use of heteroskedastic ordered probit (HETOP) models to estimate latent distributional parameters from grouped, ordered-categorical data by pooling across multiple waves of data. We illustrate the method with aggregate proficiency data reporting the number of students in schools or districts scoring in…
Descriptors: Statistical Analysis, Computation, Regression (Statistics), Sample Size
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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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Reardon, Sean F.; Kalogrides, Demetra; Ho, Andrew D. – Journal of Educational and Behavioral Statistics, 2021
Linking score scales across different tests is considered speculative and fraught, even at the aggregate level. We introduce and illustrate validation methods for aggregate linkages, using the challenge of linking U.S. school district average test scores across states as a motivating example. We show that aggregate linkages can be validated both…
Descriptors: Equated Scores, Validity, Methods, School Districts
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Fox, Jean-Paul; Wenzel, Jeremias; Klotzke, Konrad – Journal of Educational and Behavioral Statistics, 2021
Standard item response theory (IRT) models have been extended with testlet effects to account for the nesting of items; these are well known as (Bayesian) testlet models or random effect models for testlets. The testlet modeling framework has several disadvantages. A sufficient number of testlet items are needed to estimate testlet effects, and a…
Descriptors: Bayesian Statistics, Tests, Item Response Theory, Hierarchical Linear Modeling
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Andersson, Björn; Xin, Tao – Journal of Educational and Behavioral Statistics, 2021
The estimation of high-dimensional latent regression item response theory (IRT) models is difficult because of the need to approximate integrals in the likelihood function. Proposed solutions in the literature include using stochastic approximations, adaptive quadrature, and Laplace approximations. We propose using a second-order Laplace…
Descriptors: Item Response Theory, Computation, Regression (Statistics), Statistical Bias
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Kuijpers, Renske E.; Visser, Ingmar; Molenaar, Dylan – Journal of Educational and Behavioral Statistics, 2021
Mixture models have been developed to enable detection of within-subject differences in responses and response times to psychometric test items. To enable mixture modeling of both responses and response times, a distributional assumption is needed for the within-state response time distribution. Since violations of the assumed response time…
Descriptors: Test Items, Responses, Reaction Time, Models
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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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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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Suk, Youmi; Kim, Jee-Seon; Kang, Hyunseung – Journal of Educational and Behavioral Statistics, 2021
There has been increasing interest in exploring heterogeneous treatment effects using machine learning (ML) methods such as causal forests, Bayesian additive regression trees, and targeted maximum likelihood estimation. However, there is little work on applying these methods to estimate treatment effects in latent classes defined by…
Descriptors: Artificial Intelligence, Statistical Analysis, Statistical Inference, Classification
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Pashley, Nicole E.; Miratrix, Luke W. – Journal of Educational and Behavioral Statistics, 2021
Evaluating blocked randomized experiments from a potential outcomes perspective has two primary branches of work. The first focuses on larger blocks, with multiple treatment and control units in each block. The second focuses on matched pairs, with a single treatment and control unit in each block. These literatures not only provide different…
Descriptors: Causal Models, Statistical Inference, Research Methodology, Computation
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Park, Soojin; Palardy, Gregory J. – Journal of Educational and Behavioral Statistics, 2020
Estimating the effects of randomized experiments and, by extension, their mediating mechanisms, is often complicated by treatment noncompliance. Two estimation methods for causal mediation in the presence of noncompliance have recently been proposed, the instrumental variable method (IV-mediate) and maximum likelihood method (ML-mediate). However,…
Descriptors: Computation, Compliance (Psychology), Maximum Likelihood Statistics, Statistical Analysis
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Hasegawa, Raiden B.; Deshpande, Sameer K.; Small, Dylan S.; Rosenbaum, Paul R. – Journal of Educational and Behavioral Statistics, 2020
Causal effects are commonly defined as comparisons of the potential outcomes under treatment and control, but this definition is threatened by the possibility that either the treatment or the control condition is not well defined, existing instead in more than one version. This is often a real possibility in nonexperimental or observational…
Descriptors: Causal Models, Inferences, Randomized Controlled Trials, Experimental Groups
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