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Download full textZwick, Rebecca; Ye, Lei; Isham, Steven – ETS Research Report Series, 2013
Differential item functioning (DIF) analysis is a key component in the evaluation of the fairness and validity of educational tests. Although it is often assumed that refinement of the matching criterion always provides more accurate DIF results, the actual situation proves to be more complex. To explore the effectiveness of refinement, we…
Descriptors: Test Bias, Statistical Analysis, Simulation, Educational Testing
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
Peer reviewedZwick, Rebecca; Thayer, Dorothy; Lewis, Charles – Journal of Educational and Behavioral Statistics, 2000
Studied a method for flagging differential item functioning (DIF) based on loss functions. Builds on earlier research that led to the development of an empirical Bayes enhancement to the Mantel-Haenszel DIF analysis. Tested the method through simulation and found its performance better than some commonly used DIF classification systems. (SLD)
Descriptors: Bayesian Statistics, Identification, Item Bias, Simulation
Peer reviewedZwick, Rebecca – Educational and Psychological Measurement, 1997
Recent simulations have shown that, for a given sample size, the Mantel-Haenszel (MH) variances tend to be larger when items are administered to randomly selected examinees than when they are administered adaptively. Results suggest that adaptive testing may lead to more efficient application of MH differential item functioning analyses. (SLD)
Descriptors: Adaptive Testing, Item Bias, Sample Size, Simulation
Zwick, Rebecca – 1994
The Mantel Haenszel (MH; 1959) approach of Holland and Thayer (1988) is a well-established method for assessing differential item functioning (DIF). The formula for the variance of the MH DIF statistic is based on work by Phillips and Holland (1987) and Robins, Breslow, and Greenland (1986). Recent simulation studies showed that the MH variances…
Descriptors: Adaptive Testing, Evaluation Methods, Item Bias, Measurement Techniques
Peer reviewedZwick, Rebecca; Thayer, Dorothy T. – Journal of Educational and Behavioral Statistics, 1996
Two possible standard error formulas for the polytomous differential item functioning index proposed by N. J. Dorans and A. P. Schmitt (1991) were derived. These standard errors, and associated hypothesis-testing procedures, were evaluated through simulated data. The standard error that performed better is based on N. Mantel's (1963)…
Descriptors: Error of Measurement, Evaluation Methods, Hypothesis Testing, Item Bias
Zwick, Rebecca; Thayer, Dorothy T. – 2003
This study investigated the applicability to computerized adaptive testing (CAT) data of a differential item functioning (DIF) analysis that involves an empirical Bayes (EB) enhancement of the popular Mantel Haenszel (MH) DIF analysis method. The computerized Law School Admission Test (LSAT) assumed for this study was similar to that currently…
Descriptors: Adaptive Testing, Bayesian Statistics, College Entrance Examinations, Computer Assisted Testing
Rudas, Tamas; Zwick, Rebecca – 1995
A method is proposed to assess the importance of differential item functioning (DIF) by estimating the largest possible fraction of the population in which DIF does not occur, or equivalently, the smallest possible portion of the population in which DIF may occur. The approach is based on latent class (C. C. Clogg, 1981) or mixture concepts, and…
Descriptors: Estimation (Mathematics), Goodness of Fit, Item Bias, Maximum Likelihood Statistics
Zwick, Rebecca; Thayer, Dorothy T. – 1994
Several recent studies have investigated the application of statistical inference procedures to the analysis of differential item functioning (DIF) in test items that are scored on an ordinal scale. Mantel's extension of the Mantel-Haenszel test is a possible hypothesis-testing method for this purpose. The development of descriptive statistics for…
Descriptors: Error of Measurement, Evaluation Methods, Hypothesis Testing, Item Bias
Zwick, Rebecca – 1995
This paper describes a study, now in progress, of new methods for representing the sampling variability of Mantel-Haenszel differential item functioning (DIF) results, based on the system for categorizing the severity of DIF that is now in place at the Educational Testing Service. The methods, which involve a Bayesian elaboration of procedures…
Descriptors: Adaptive Testing, Bayesian Statistics, Classification, Computer Assisted Testing
Peer reviewedZwick, Rebecca – Journal of Educational Measurement, 1987
National Assessment of Educational Progress reading data were scaled using a unidimensional item response theory model. Bock's full-information factor analysis and Rosenbaum's test of unidimensionality were applied. Conclusions about unidimensionality for balanced incomplete block spiralled data were the same as for complete data. (Author/GDC)
Descriptors: Factor Analysis, Item Analysis, Latent Trait Theory, Mathematical Models
Zwick, Rebecca; And Others – 1993
Simulated data were used to investigate the performance of modified versions of the Mantel-Haenszel and standardization methods of differential item functioning (DIF) analysis in computer-adaptive tests (CATs). Each "examinee" received 25 items out of a 75-item pool. A three-parameter logistic item response model was assumed, and…
Descriptors: Adaptive Testing, Computer Assisted Testing, Correlation, Error of Measurement
Zwick, Rebecca; And Others – 1994
A simulation study of methods of assessing differential item functioning (DIF) in computer-adaptive tests (CATs) was conducted by Zwick, Thayer, and Wingersky (in press, 1993). Results showed that modified versions of the Mantel-Haenszel and standardization methods work well with CAT data. DIF methods were also investigated for nonadaptive…
Descriptors: Adaptive Testing, Computer Assisted Testing, Error of Measurement, Estimation (Mathematics)
