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Wang, Lin; Qian, Jiahe; Lee, Yi-Hsuan – ETS Research Report Series, 2013
The purpose of this study was to evaluate the combined effects of reduced equating sample size and shortened anchor test length on item response theory (IRT)-based linking and equating results. Data from two independent operational forms of a large-scale testing program were used to establish the baseline results for evaluating the results from…
Descriptors: Test Construction, Item Response Theory, Testing Programs, Simulation
Guo, Hongwen; Liu, Jinghua; Dorans, Neil; Feigenbaum, Miriam – ETS Research Report Series, 2011
Maintaining score stability is crucial for an ongoing testing program that administers several tests per year over many years. One way to stall the drift of the score scale is to use an equating design with multiple links. In this study, we use the operational and experimental SAT® data collected from 44 administrations to investigate the effect…
Descriptors: Equated Scores, College Entrance Examinations, Reliability, Testing Programs
Moses, Tim; Liu, Jinghua; Tan, Adele; Deng, Weiling; Dorans, Neil J. – ETS Research Report Series, 2013
In this study, differential item functioning (DIF) methods utilizing 14 different matching variables were applied to assess DIF in the constructed-response (CR) items from 6 forms of 3 mixed-format tests. Results suggested that the methods might produce distinct patterns of DIF results for different tests and testing programs, in that the DIF…
Descriptors: Test Construction, Multiple Choice Tests, Test Items, Item Analysis
Kim, Sooyeon; von Davier, Alina A.; Haberman, Shelby – ETS Research Report Series, 2006
This study addresses the sample error and linking bias that occur with small and unrepresentative samples in a non-equivalent groups anchor test (NEAT) design. We propose a linking method called the "synthetic function," which is a weighted average of the identity function (the trivial equating function for forms that are known to be…
Descriptors: Equated Scores, Sample Size, Test Items, Statistical Bias