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Wang, Xiaohui; Bradlow, Eric T.; Wainer, Howard; Muller, Eric S. – Journal of Educational and Behavioral Statistics, 2008
In the course of screening a form of a medical licensing exam for items that function differentially (DIF) between men and women, the authors used the traditional Mantel-Haenszel (MH) statistic for initial screening and a Bayesian method for deeper analysis. For very easy items, the MH statistic unexpectedly often found DIF where there was none.…
Descriptors: Bayesian Statistics, Licensing Examinations (Professions), Medicine, Test Items

Wainer, Howard – Journal of Educational Measurement, 1986
An example demonstrates and explains that summary statistics commonly used to measure test quality can be seriously misleading and that summary statistics for the whole test are not sufficient for judging the quality of the test. (Author/LMO)
Descriptors: Correlation, Item Analysis, Statistical Bias, Statistical Studies

Wainer, Howard – Journal of Educational Measurement, 1989
This paper reviews the role of the item in test construction, and suggests some new methods of item analysis. A look at dynamic, graphical item analysis is provided that uses the advantages of modern, high-speed, highly interactive computing. Several illustrations are provided. (Author/TJH)
Descriptors: Computer Assisted Testing, Computer Graphics, Graphs, Item Analysis

Thissen, David; Wainer, Howard – Psychometrika, 1982
The mathematics required to calculate the asymptotic standard errors of the parameters of three commonly used logistic item response models is described and used to generate values for common situations. Difficulties in using maximum likelihood estimation with the three parameter model are discussed. (Author/JKS)
Descriptors: Error of Measurement, Item Analysis, Latent Trait Theory, Maximum Likelihood Statistics

Wainer, Howard – Journal of Educational Statistics, 1991
A graphical-decision rule for studying an item's behavior and admitting items onto a test form is proposed. The tool--the isthmus of acceptance--ameliorates some parameter stability problems common to binary item response theory models by using a functional representation of item performance, and it is tuned to match current standards. (TJH)
Descriptors: Difficulty Level, Graphs, Item Analysis, Item Response Theory
Wainer, Howard; Wright, Benjamin D. – 1980
The pure Rasch model was compared with four modifications of the model in a number of different simulations in order to ascertain the comparative efficiencies of the parameter estimations of these modifications. Because there is always noise in test score data, some individuals may have response patterns that do not fit the model and their…
Descriptors: Error of Measurement, Guessing (Tests), Item Analysis, Latent Trait Theory

Wainer, Howard; And Others – Journal of Educational Statistics, 1984
A mathematics item on the Scholastic Aptitude Test (SAT) was found to be faulty and received wide publicity. A detailed investigation into its mathematical and psychometric properties is presented. It was found that the problem could be considered ambiguous but that almost no one noticed the ambiguity. (Author/JKS)
Descriptors: Classification, College Entrance Examinations, Geometry, High Schools
Wainer, Howard – 1985
Techniques derived from item response theory are useful for estimating the reliability of test classification above and below the cutting score. Test developers can construct a test whose information is peaked in the region of the cutting score; users can select a test which provides the most information in this region. The Cut-Score…
Descriptors: Cutting Scores, Item Analysis, Latent Trait Theory, Mastery Tests
Wainer, Howard – 1985
It is important to estimate the number of examinees who reached a test item, because item difficulty is defined by the number who answered correctly divided by the number who reached the item. A new method is presented and compared to the previously used definition of three categories of response to an item: (1) answered; (2) omitted--a…
Descriptors: College Entrance Examinations, Difficulty Level, Estimation (Mathematics), High Schools