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Efficient Corrections for Standardized Person-Fit Statistics

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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.

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Funding

This work was completed while the first author was an Educational Testing Service (ETS) Harold Gulliksen Psychometric Research Fellow.

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Correspondence to Kylie Gorney.

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The authors declare that they have no conflict of interest.

Data Availability

The data that support the findings of this study are available from Dr. James Wollack upon reasonable request.

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Appendices

Appendix A

R Code to Compute the Person-Fit Statistics

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Appendix B

Ability Estimates

See Figs. 6 and 7.

Fig. 6
figure 6

Descriptive statistics of the null distributions of \(l_z\) and \(l_z^*\).

Fig. 7
figure 7

Type I error rates of \(l_z\) and \(l_z^*\).

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Gorney, K., Sinharay, S. & Eckerly, C. Efficient Corrections for Standardized Person-Fit Statistics. Psychometrika (2024). https://doi.org/10.1007/s11336-024-09960-x

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  • DOI: https://doi.org/10.1007/s11336-024-09960-x

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