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Showing 1 to 15 of 541 results Save | Export
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Wang, Shiyu; Yang, Yan; Culpepper, Steven Andrew; Douglas, Jeffrey A. – Journal of Educational and Behavioral Statistics, 2018
A family of learning models that integrates a cognitive diagnostic model and a higher-order, hidden Markov model in one framework is proposed. This new framework includes covariates to model skill transition in the learning environment. A Bayesian formulation is adopted to estimate parameters from a learning model. The developed methods are…
Descriptors: Skill Development, Cognitive Measurement, Cognitive Processes, Markov Processes
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Philipp, Michel; Strobl, Carolin; de la Torre, Jimmy; Zeileis, Achim – Journal of Educational and Behavioral Statistics, 2018
Cognitive diagnosis models (CDMs) are an increasingly popular method to assess mastery or nonmastery of a set of fine-grained abilities in educational or psychological assessments. Several inference techniques are available to quantify the uncertainty of model parameter estimates, to compare different versions of CDMs, or to check model…
Descriptors: Computation, Error of Measurement, Models, Cognitive Measurement
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Sales, Adam C.; Hansen, Ben B.; Rowan, Brian – Journal of Educational and Behavioral Statistics, 2018
In causal matching designs, some control subjects are often left unmatched, and some covariates are often left unmodeled. This article introduces "rebar," a method using high-dimensional modeling to incorporate these commonly discarded data without sacrificing the integrity of the matching design. After constructing a match, a researcher…
Descriptors: Computation, Prediction, Models, Data
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Hong, Guanglei; Qin, Xu; Yang, Fan – Journal of Educational and Behavioral Statistics, 2018
Through a sensitivity analysis, the analyst attempts to determine whether a conclusion of causal inference could be easily reversed by a plausible violation of an identification assumption. Analytic conclusions that are harder to alter by such a violation are expected to add a higher value to scientific knowledge about causality. This article…
Descriptors: Statistical Inference, Probability, Statistical Bias, Statistical Analysis
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Yang, Ji Seung; Zheng, Xiaying – Journal of Educational and Behavioral Statistics, 2018
The purpose of this article is to introduce and review the capability and performance of the Stata item response theory (IRT) package that is available from Stata v.14, 2015. Using a simulated data set and a publicly available item response data set extracted from Programme of International Student Assessment, we review the IRT package from…
Descriptors: Item Response Theory, Item Analysis, Computer Software, Statistical Analysis
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Reardon, Sean F.; Shear, Benjamin R.; Castellano, Katherine E.; Ho, Andrew D. – Journal of Educational and Behavioral Statistics, 2017
Test score distributions of schools or demographic groups are often summarized by frequencies of students scoring in a small number of ordered proficiency categories. We show that heteroskedastic ordered probit (HETOP) models can be used to estimate means and standard deviations of multiple groups' test score distributions from such data. Because…
Descriptors: Scores, Statistical Analysis, Models, Computation
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Polanin, Joshua R.; Hennessy, Emily A.; Tanner-Smith, Emily E. – Journal of Educational and Behavioral Statistics, 2017
Meta-analysis is a statistical technique that allows an analyst to synthesize effect sizes from multiple primary studies. To estimate meta-analysis models, the open-source statistical environment R is quickly becoming a popular choice. The meta-analytic community has contributed to this growth by developing numerous packages specific to…
Descriptors: Meta Analysis, Open Source Technology, Computer Software, Effect Size
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Thoemmes, Felix; Liao, Wang; Jin, Ze – Journal of Educational and Behavioral Statistics, 2017
This article describes the analysis of regression-discontinuity designs (RDDs) using the R packages rdd, rdrobust, and rddtools. We discuss similarities and differences between these packages and provide directions on how to use them effectively. We use real data from the Carolina Abecedarian Project to show how an analysis of an RDD can be…
Descriptors: Regression (Statistics), Research Design, Robustness (Statistics), Computer Software
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Ramsay, James O.; Wiberg, Marie – Journal of Educational and Behavioral Statistics, 2017
This article promotes the use of modern test theory in testing situations where sum scores for binary responses are now used. It directly compares the efficiencies and biases of classical and modern test analyses and finds an improvement in the root mean squared error of ability estimates of about 5% for two designed multiple-choice tests and…
Descriptors: Scoring, Test Theory, Computation, Maximum Likelihood Statistics
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Qin, Xu; Hong, Guanglei – Journal of Educational and Behavioral Statistics, 2017
When a multisite randomized trial reveals between-site variation in program impact, methods are needed for further investigating heterogeneous mediation mechanisms across the sites. We conceptualize and identify a joint distribution of site-specific direct and indirect effects under the potential outcomes framework. A method-of-moments procedure…
Descriptors: Randomized Controlled Trials, Hierarchical Linear Modeling, Statistical Analysis, Probability
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Grabovsky, Irina; Wainer, Howard – Journal of Educational and Behavioral Statistics, 2017
In this essay, we describe the construction and use of the Cut-Score Operating Function in aiding standard setting decisions. The Cut-Score Operating Function shows the relation between the cut-score chosen and the consequent error rate. It allows error rates to be defined by multiple loss functions and will show the behavior of each loss…
Descriptors: Cutting Scores, Standard Setting (Scoring), Decision Making, Error Patterns
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Grabovsky, Irina; Wainer, Howard – Journal of Educational and Behavioral Statistics, 2017
In this article, we extend the methodology of the Cut-Score Operating Function that we introduced previously and apply it to a testing scenario with multiple independent components and different testing policies. We derive analytically the overall classification error rate for a test battery under the policy when several retakes are allowed for…
Descriptors: Cutting Scores, Weighted Scores, Classification, 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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Mistler, Stephen A.; Enders, Craig K. – Journal of Educational and Behavioral Statistics, 2017
Multiple imputation methods can generally be divided into two broad frameworks: joint model (JM) imputation and fully conditional specification (FCS) imputation. JM draws missing values simultaneously for all incomplete variables using a multivariate distribution, whereas FCS imputes variables one at a time from a series of univariate conditional…
Descriptors: Statistical Analysis, Comparative Analysis, Hierarchical Linear Modeling, Computer Simulation
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Kleinke, Kristian – Journal of Educational and Behavioral Statistics, 2017
Predictive mean matching (PMM) is a standard technique for the imputation of incomplete continuous data. PMM imputes an actual observed value, whose predicted value is among a set of k = 1 values (the so-called donor pool), which are closest to the one predicted for the missing case. PMM is usually better able to preserve the original distribution…
Descriptors: Statistical Analysis, Statistical Distributions, Robustness (Statistics), Sample Size
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