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Direct linkSinharay, Sandip – Applied Measurement in Education, 2017
Karabatsos compared the power of 36 person-fit statistics using receiver operating characteristics curves and found the "H[superscript T]" statistic to be the most powerful in identifying aberrant examinees. He found three statistics, "C", "MCI", and "U3", to be the next most powerful. These four statistics,…
Descriptors: Nonparametric Statistics, Goodness of Fit, Simulation, Comparative Analysis
Sinharay, Sandip – Journal of Educational and Behavioral Statistics, 2016
Meijer and van Krimpen-Stoop noted that the number of person-fit statistics (PFSs) that have been designed for computerized adaptive tests (CATs) is relatively modest. This article partially addresses that concern by suggesting three new PFSs for CATs. The statistics are based on tests for a change point and can be used to detect an abrupt change…
Descriptors: Computer Assisted Testing, Adaptive Testing, Item Response Theory, Goodness of Fit
Sinharay, Sandip; Wan, Ping; Choi, Seung W.; Kim, Dong-In – Journal of Educational Measurement, 2015
With an increase in the number of online tests, the number of interruptions during testing due to unexpected technical issues seems to be on the rise. For example, interruptions occurred during several recent state tests. When interruptions occur, it is important to determine the extent of their impact on the examinees' scores. Researchers such as…
Descriptors: Computer Assisted Testing, Testing Problems, Scores, Statistical Analysis
Download full textGuo, Hongwen; Sinharay, Sandip – Educational Testing Service, 2011
Nonparametric, or kernel, estimation of item response curve (IRC) is a concern theoretically and operationally. Accuracy of this estimation, often used in item analysis in testing programs, is biased when the observed scores are used as the regressor because the observed scores are contaminated by measurement error. In this study, we investigate…
Descriptors: Error of Measurement, Nonparametric Statistics, Item Response Theory, Computation
Sinharay, Sandip; Holland, Paul W. – Journal of Educational Measurement, 2010
The nonequivalent groups with anchor test (NEAT) design involves missing data that are missing by design. Three equating methods that can be used with a NEAT design are the frequency estimation equipercentile equating method, the chain equipercentile equating method, and the item-response-theory observed-score-equating method. We suggest an…
Descriptors: Equated Scores, Item Response Theory, Comparative Analysis, Evaluation
Guo, Hongwen; Sinharay, Sandip – Journal of Educational and Behavioral Statistics, 2011
Nonparametric or kernel regression estimation of item response curves (IRCs) is often used in item analysis in testing programs. These estimates are biased when the observed scores are used as the regressor because the observed scores are contaminated by measurement error. Accuracy of this estimation is a concern theoretically and operationally.…
Descriptors: Testing Programs, Measurement, Item Analysis, Error of Measurement
Puhan, Gautam; Sinharay, Sandip; Haberman, Shelby; Larkin, Kevin – ETS Research Report Series, 2008
Will reporting subscores provide any additional information than the total score? Is there a method that can be used to provide more trustworthy subscores than observed subscores? These 2 questions are addressed in this study. To answer the 2nd question, 2 subscore estimation methods (i.e., subscore estimated from the observed total score or…
Descriptors: Comparative Analysis, Scores, Tests, Certification
Sinharay, Sandip; Holland, Paul W. – Educational Testing Service, 2008
The nonequivalent groups with anchor test (NEAT) design involves missing data that are missing by design. Three popular equating methods that can be used with a NEAT design are the poststratification equating method, the chain equipercentile equating method, and the item-response-theory observed-score-equating method. These three methods each…
Descriptors: Equated Scores, Test Items, Item Response Theory, Data
