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Showing all 12 results Save | Export
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Casabianca, Jodi M.; Lewis, Charles – Journal of Educational and Behavioral Statistics, 2018
The null hypothesis test used in differential item functioning (DIF) detection tests for a subgroup difference in item-level performance--if the null hypothesis of "no DIF" is rejected, the item is flagged for DIF. Conversely, an item is kept in the test form if there is insufficient evidence of DIF. We present frequentist and empirical…
Descriptors: Test Bias, Hypothesis Testing, Bayesian Statistics, Statistical Analysis
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Magis, David; Tuerlinckx, Francis; De Boeck, Paul – Journal of Educational and Behavioral Statistics, 2015
This article proposes a novel approach to detect differential item functioning (DIF) among dichotomously scored items. Unlike standard DIF methods that perform an item-by-item analysis, we propose the "LR lasso DIF method": logistic regression (LR) model is formulated for all item responses. The model contains item-specific intercepts,…
Descriptors: Test Bias, Test Items, Regression (Statistics), Scores
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Patarapichayatham, Chalie; Kamata, Akihito; Kanjanawasee, Sirichai – Educational and Psychological Measurement, 2012
Model specification issues on the cross-level two-way differential item functioning model were previously investigated by Patarapichayatham et al. (2009). Their study clarified that an incorrect model specification can easily lead to biased estimates of key parameters. The objective of this article is to provide further insights on the issue by…
Descriptors: Test Bias, Models, Bayesian Statistics, Statistical Analysis
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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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Sinharay, Sandip; Dorans, Neil J. – Journal of Educational and Behavioral Statistics, 2010
The Mantel-Haenszel (MH) procedure (Mantel and Haenszel) is a popular method for estimating and testing a common two-factor association parameter in a 2 x 2 x K table. Holland and Holland and Thayer described how to use the procedure to detect differential item functioning (DIF) for tests with dichotomously scored items. Wang, Bradlow, Wainer, and…
Descriptors: Test Bias, Statistical Analysis, Computation, Bayesian Statistics
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Qian, Xiaoyu; Nandakumar, Ratna; Glutting, Joseoph; Ford, Danielle; Fifield, Steve – ETS Research Report Series, 2017
In this study, we investigated gender and minority achievement gaps on 8th-grade science items employing a multilevel item response methodology. Both gaps were wider on physics and earth science items than on biology and chemistry items. Larger gender gaps were found on items with specific topics favoring male students than other items, for…
Descriptors: Item Analysis, Gender Differences, Achievement Gap, Grade 8
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Zwick, Rebecca – ETS Research Report Series, 2012
Differential item functioning (DIF) analysis is a key component in the evaluation of the fairness and validity of educational tests. The goal of this project was to review the status of ETS DIF analysis procedures, focusing on three aspects: (a) the nature and stringency of the statistical rules used to flag items, (b) the minimum sample size…
Descriptors: Test Bias, Sample Size, Bayesian Statistics, Evaluation Methods
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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
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Fidalgo, Angel M.; Hashimoto, Kanako; Bartram, Dave; Muniz, Jose – Journal of Experimental Education, 2007
In this study, the authors assess several strategies created on the basis of the Mantel-Haenszel (MH) procedure for conducting differential item functioning (DIF) analysis with small samples. One of the analytical strategies is a loss function (LF) that uses empirical Bayes Mantel-Haenszel estimators, whereas the other strategies use the classical…
Descriptors: Bayesian Statistics, Test Bias, Statistical Analysis, Sample Size
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Sinharay, Sandip; Dorans, Neil J.; Grant, Mary C.; Blew, Edwin O.; Knorr, Colleen M. – ETS Research Report Series, 2006
The application of the Mantel-Haenszel test statistic (and other popular DIF-detection methods) to determine DIF requires large samples, but test administrators often need to detect DIF with small samples. There is no universally agreed upon statistical approach for performing DIF analysis with small samples; hence there is substantial scope of…
Descriptors: Test Bias, Computation, Sample Size, Bayesian Statistics
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Sinharay, Sandip – ETS Research Report Series, 2004
Assessing fit of psychometric models has always been an issue of enormous interest, but there exists no unanimously agreed upon item fit diagnostic for the models. Bayesian networks, frequently used in educational assessments (see, for example, Mislevy, Almond, Yan, & Steinberg, 2001) primarily for learning about students' knowledge and…
Descriptors: Bayesian Statistics, Networks, Models, Goodness of Fit
Warm, Thomas A. – 1978
This primer is an introduction to item response theory (also called item characteristic curve theory, or latent trait theory) as it is used most commonly--for scoring multiple choice achievement or aptitude tests. Written for the testing practitioner with minimum training in statistics and psychometrics, it presents and illustrates the basic…
Descriptors: Ability Identification, Achievement Tests, Adaptive Testing, Aptitude Tests