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Lu, Ru; Guo, Hongwen; Dorans, Neil J. – ETS Research Report Series, 2021
Two families of analysis methods can be used for differential item functioning (DIF) analysis. One family is DIF analysis based on observed scores, such as the Mantel-Haenszel (MH) and the standardized proportion-correct metric for DIF procedures; the other is analysis based on latent ability, in which the statistic is a measure of departure from…
Descriptors: Robustness (Statistics), Weighted Scores, Test Items, Item Analysis
Kim, Jihye – ProQuest LLC, 2010
In DIF studies, a Type I error refers to the mistake of identifying non-DIF items as DIF items, and a Type I error rate refers to the proportion of Type I errors in a simulation study. The possibility of making a Type I error in DIF studies is always present and high possibility of making such an error can weaken the validity of the assessment.…
Descriptors: Test Bias, Test Length, Simulation, Testing
Wei, Youhua – ProQuest LLC, 2008
Scale linking is the process of developing the connection between scales of two or more sets of parameter estimates obtained from separate test calibrations. It is the prerequisite for many applications of IRT, such as test equating and differential item functioning analysis. Unidimensional scale linking methods have been studied and applied…
Descriptors: Test Length, Test Items, Sample Size, Simulation
Chang, Yuan-chin Ivan – Psychometrika, 2005
In this paper, we apply sequential one-sided confidence interval estimation procedures with beta-protection to adaptive mastery testing. The procedures of fixed-width and fixed proportional accuracy confidence interval estimation can be viewed as extensions of one-sided confidence interval procedures. It can be shown that the adaptive mastery…
Descriptors: Mastery Tests, Probability, Intervals, Testing
Hambleton, Ronald K.; Cook, Linda L. – 1978
The purpose of the present research was to study, systematically, the "goodness-of-fit" of the one-, two-, and three-parameter logistic models. We studied, using computer-simulated test data, the effects of four variables: variation in item discrimination parameters, the average value of the pseudo-chance level parameters, test length,…
Descriptors: Career Development, Difficulty Level, Goodness of Fit, Item Analysis

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