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Efficient Corrections for Standardized Person-Fit Statistics

Published online by Cambridge University Press:  27 December 2024

Kylie GorneyOpen the ORCID record for Kylie Gorney[Opens in a new window],
Sandip Sinharay and
Carol Eckerly
Kylie Gorney*
Affiliation:
Michigan State University
Sandip Sinharay
Affiliation:
Educational Testing Service
Carol Eckerly
Affiliation:
Educational Testing Service
*
Correspondence should be made to Kylie Gorney, Department of Counseling, Educational Psychology, and Special Education, Michigan State University, 460 Erickson Hall, 620 Farm Lane, East Lansing, MI 48824, USA. Email: kgorney@msu.edu
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Abstract

Many popular person-fit statistics belong to the class of standardized person-fit statistics, T, and are assumed to have a standard normal null distribution. However, in practice, this assumption is incorrect since T is computed using (a) an estimated ability parameter and (b) a finite number of items. Snijders (Psychometrika 66(3):331–342, 2001) developed mean and variance corrections for T to account for the use of an estimated ability parameter. Bedrick (Psychometrika 62(2):191–199, 1997) and Molenaar and Hoijtink (Psychometrika 55(1):75–106, 1990) developed skewness corrections for T to account for the use of a finite number of items. In this paper, we combine these two lines of research and propose three new corrections for T that simultaneously account for the use of an estimated ability parameter and the use of a finite number of items. The new corrections are efficient in that they only require the analysis of the original data set and do not require the simulation or analysis of any additional data sets. We conducted a detailed simulation study and found that the new corrections are able to control the Type I error rate while also maintaining reasonable levels of power. A real data example is also included.

Keywords

Person fititem response theoryaberrant behavior

Information

Type
Theory & Methods
Information
Psychometrika , Volume 89 , Issue 2 , June 2024, pp. 569 - 591
Copyright
Copyright © 2024 The Author(s), under exclusive licence to The Psychometric Society

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