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50 Years of ERIC
50 Years of ERIC
The Education Resources Information Center (ERIC) is celebrating its 50th Birthday! First opened on May 15th, 1964 ERIC continues the long tradition of ongoing innovation and enhancement.

Learn more about the history of ERIC here. PDF icon

Showing 1 to 15 of 54 results
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Longford, Nicholas T. – Journal of Educational and Behavioral Statistics, 2014
A method for medical screening is adapted to differential item functioning (DIF). Its essential elements are explicit declarations of the level of DIF that is acceptable and of the loss function that quantifies the consequences of the two kinds of inappropriate classification of an item. Instead of a single level and a single function, sets of…
Descriptors: Test Items, Test Bias, Simulation, Hypothesis Testing
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Wang, Chun – Journal of Educational and Behavioral Statistics, 2014
Many latent traits in social sciences display a hierarchical structure, such as intelligence, cognitive ability, or personality. Usually a second-order factor is linearly related to a group of first-order factors (also called domain abilities in cognitive ability measures), and the first-order factors directly govern the actual item responses.…
Descriptors: Measurement, Accuracy, Item Response Theory, Adaptive Testing
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van der Linden, Wim J.; Xiong, Xinhui – Journal of Educational and Behavioral Statistics, 2013
Two simple constraints on the item parameters in a response--time model are proposed to control the speededness of an adaptive test. As the constraints are additive, they can easily be included in the constraint set for a shadow-test approach (STA) to adaptive testing. Alternatively, a simple heuristic is presented to control speededness in plain…
Descriptors: Adaptive Testing, Heuristics, Test Length, Reaction Time
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Wang, Chun; Fan, Zhewen; Chang, Hua-Hua; Douglas, Jeffrey A. – Journal of Educational and Behavioral Statistics, 2013
The item response times (RTs) collected from computerized testing represent an underutilized type of information about items and examinees. In addition to knowing the examinees' responses to each item, we can investigate the amount of time examinees spend on each item. Current models for RTs mainly focus on parametric models, which have the…
Descriptors: Reaction Time, Computer Assisted Testing, Test Items, Accuracy
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Tao, Jian; Shi, Ning-Zhong; Chang, Hua-Hua – Journal of Educational and Behavioral Statistics, 2012
For mixed-type tests composed of both dichotomous and polytomous items, polytomous items often yield more information than dichotomous ones. To reflect the difference between the two types of items, polytomous items are usually pre-assigned with larger weights. We propose an item-weighted likelihood method to better assess examinees' ability…
Descriptors: Test Items, Weighted Scores, Maximum Likelihood Statistics, Statistical Bias
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van der Linden, Wim J.; Jeon, Minjeong – Journal of Educational and Behavioral Statistics, 2012
The probability of test takers changing answers upon review of their initial choices is modeled. The primary purpose of the model is to check erasures on answer sheets recorded by an optical scanner for numbers and patterns that may be indicative of irregular behavior, such as teachers or school administrators changing answer sheets after their…
Descriptors: Probability, Models, Test Items, Educational Testing
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Andrich, David; Hagquist, Curt – Journal of Educational and Behavioral Statistics, 2012
The literature in modern test theory on procedures for identifying items with differential item functioning (DIF) among two groups of persons includes the Mantel-Haenszel (MH) procedure. Generally, it is not recognized explicitly that if there is real DIF in some items which favor one group, then as an artifact of this procedure, artificial DIF…
Descriptors: Test Bias, Test Items, Item Response Theory, Statistical Analysis
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Andrich, David; Marais, Ida; Humphry, Stephen – Journal of Educational and Behavioral Statistics, 2012
Andersen (1995, 2002) proves a theorem relating variances of parameter estimates from samples and subsamples and shows its use as an adjunct to standard statistical analyses. The authors show an application where the theorem is central to the hypothesis tested, namely, whether random guessing to multiple choice items affects their estimates in the…
Descriptors: Test Items, Item Response Theory, Multiple Choice Tests, Guessing (Tests)
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Zwick, Rebecca; Ye, Lei; Isham, Steven – Journal of Educational and Behavioral Statistics, 2012
This study demonstrates how the stability of Mantel-Haenszel (MH) DIF (differential item functioning) methods can be improved by integrating information across multiple test administrations using Bayesian updating (BU). The authors conducted a simulation that showed that this approach, which is based on earlier work by Zwick, Thayer, and Lewis,…
Descriptors: Test Bias, Computation, Statistical Analysis, Bayesian Statistics
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Fan, Zhewen; Wang, Chun; Chang, Hua-Hua; Douglas, Jeffrey – Journal of Educational and Behavioral Statistics, 2012
Traditional methods for item selection in computerized adaptive testing only focus on item information without taking into consideration the time required to answer an item. As a result, some examinees may receive a set of items that take a very long time to finish, and information is not accrued as efficiently as possible. The authors propose two…
Descriptors: Computer Assisted Testing, Adaptive Testing, Test Items, Item Analysis
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Smithson, Michael; Merkle, Edgar C.; Verkuilen, Jay – Journal of Educational and Behavioral Statistics, 2011
This paper describes the application of finite-mixture general linear models based on the beta distribution to modeling response styles, polarization, anchoring, and priming effects in probability judgments. These models, in turn, enhance our capacity for explicitly testing models and theories regarding the aforementioned phenomena. The mixture…
Descriptors: Priming, Research Methodology, Probability, Item Response Theory
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Schuster, Christof; Yuan, Ke-Hai – Journal of Educational and Behavioral Statistics, 2011
Because of response disturbances such as guessing, cheating, or carelessness, item response models often can only approximate the "true" individual response probabilities. As a consequence, maximum-likelihood estimates of ability will be biased. Typically, the nature and extent to which response disturbances are present is unknown, and, therefore,…
Descriptors: Computation, Item Response Theory, Probability, Maximum Likelihood Statistics
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Wang, Lijuan – Journal of Educational and Behavioral Statistics, 2010
This study introduces an item response theory-zero-inflated Poisson (IRT-ZIP) model to investigate psychometric properties of multiple items and predict individuals' latent trait scores for multivariate zero-inflated count data. In the model, two link functions are used to capture two processes of the zero-inflated count data. Item parameters are…
Descriptors: Item Response Theory, Models, Test Items, Psychometrics
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Wainer, Howard; Bradlow, Eric; Wang, Xiaohui – Journal of Educational and Behavioral Statistics, 2010
Confucius pointed out that the first step toward wisdom is calling things by the right name. The term "Differential Item Functioning" (DIF) did not arise fully formed from the miasma of psychometrics, it evolved from a variety of less accurate terms. Among its forebears was "item bias" but that term has a pejorative connotation that does not…
Descriptors: Test Bias, Difficulty Level, Test Items, Statistical Analysis
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Passos, Valeria Lima; Berger, Martijn P. F.; Tan, Frans E. S. – Journal of Educational and Behavioral Statistics, 2008
During the early stage of computerized adaptive testing (CAT), item selection criteria based on Fisher"s information often produce less stable latent trait estimates than the Kullback-Leibler global information criterion. Robustness against early stage instability has been reported for the D-optimality criterion in a polytomous CAT with the…
Descriptors: Computer Assisted Testing, Adaptive Testing, Evaluation Criteria, Item Analysis
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