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Kim, Sooyeon; Walker, Michael E. – ETS Research Report Series, 2009
This study investigated the subgroup invariance of equating functions for a licensure test in the context of a nonequivalent groups with anchor test (NEAT) design. Examinees who had taken a new, to-be-equated form of the test were divided into three subgroups according to their previous testing experience: (a) repeaters who previously took the…
Descriptors: Equated Scores, Licensing Examinations (Professions), Test Construction, Repetition
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Kim, Sooyeon; von Davier, Alina A.; Haberman, Shelby – ETS Research Report Series, 2007
The synthetic function, which is a weighted average of the identity (the trivial linking function for forms that are known to be completely parallel) and a traditional equating method, has been proposed as an alternative for performing linking with very small samples (Kim, von Davier, & Haberman, 2006). The purpose of the present study was to…
Descriptors: Equated Scores, Sample Size, Statistical Analysis, Licensing Examinations (Professions)
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Kim, Sooyeon; Linvingston, Samuel A.; Lewis, Charles – ETS Research Report Series, 2008
This paper describes an empirical evaluation of a Bayesian procedure for equating scores on test forms taken by small numbers of examinees, using collateral information from the equating of other test forms. In this procedure, a separate Bayesian estimate is derived for the equated score at each raw-score level, making it unnecessary to specify a…
Descriptors: Equated Scores, Statistical Analysis, Sample Size, Bayesian Statistics
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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