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      Diagnosing item score patterns on a test using item response theory-based person-fit statistics.

      Psychological methods
      Adult, Aptitude Tests, statistics & numerical data, Bias (Epidemiology), Data Interpretation, Statistical, Humans, Intelligence Tests, Mathematical Computing, Models, Statistical, Personnel Selection, Probability, Psychometrics, Reproducibility of Results, Software

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          Abstract

          Person-fit statistics have been proposed to investigate the fit of an item score pattern to an item response theory (IRT) model. The author investigated how these statistics can be used to detect different types of misfit. Intelligence test data were analyzed using person-fit statistics in the context of the G. Rasch (1960) model and R. J. Mokken's (1971, 1997) IRT models. The effect of the choice of an IRT model to detect misfitting item score patterns and the usefulness of person-fit statisticsfor diagnosis of misfit are discussed. Results showed that different types of person-fit statistics can be used to detect different kinds of person misfit. Parametric person-fit statistics had more power than nonparametric person-fit statistics.

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          A Theory and Procedure of Scale Analysis

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            Appropriateness measurement with polychotomous item response models and standardized indices

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              A new item response theory modeling approach with applications to unidimensionality assessment and ability estimation

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