Robustness of person-fit decisions in computerized adaptive testing

R.R. Meijer

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Person-fit statistics test whether or not the likelihood of a respondent’s complete vector of item scores on a test is low given the hypothesized item response theory (IRT) model. This binary information may be insufficient for diagnosing the cause of a misfitting item-score vector. This paper applies different types of person-fit analysis in a computer adaptive testing context and investigates the robustness of several methods to multidimensional test data. Both global person-fit statistics to make the binary decision about fit or misfit of a person’s item-score vector and local checks are applied. Results showed that there are differences between the methods with respect to the robustness in a multidimensional context and that some methods are more useful than othermethods.
Original languageUndefined
Place of PublicationNewton, PA, USA
PublisherLaw School Admission Council
Publication statusPublished - Nov 2005

Publication series

NameLSAC research report series
PublisherLaw School Admission Council


  • IR-104282

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