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Constructing the exact significance level for a person fit statistic

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Abstract

This paper uses an extension of the network algorithm originally introduced by Mehta and Patel to construct exact tail probabilities for testing the general hypothesis that item responses are distributed according to the Rasch model. By assuming that item difficulties are known, the algorithm is applicable to the statistical tests either given the maximum likelihood ability estimate or conditioned on the total score. A simulation study indicates that the network algorithm is an efficient tool for computing the significance level of a person fit statistic based on test lengths of 30 items or less.

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Authors and Affiliations

  1. Institute of Statistical Science, Academia Sinica, 115, Taipei, Taiwan, R.O.C.

    Michelle Liou & Chih-Hsin Chang

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  1. Michelle Liou
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  2. Chih-Hsin Chang
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Liou, M., Chang, CH. Constructing the exact significance level for a person fit statistic. Psychometrika 57, 169–181 (1992). https://doi.org/10.1007/BF02294503

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  • DOI: https://doi.org/10.1007/BF02294503

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