Premium
Assessment of Person Fit Using Resampling‐Based Approaches
Author(s) -
Sinharay Sandip
Publication year - 2016
Publication title -
journal of educational measurement
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.917
H-Index - 47
eISSN - 1745-3984
pISSN - 0022-0655
DOI - 10.1111/jedm.12101
Subject(s) - resampling , statistic , a priori and a posteriori , computer science , monte carlo method , mathematics , statistics , jackknife resampling , null hypothesis , permutation (music) , artificial intelligence , philosophy , physics , epistemology , estimator , acoustics
De la Torre and Deng suggested a resampling‐based approach for person‐fit assessment (PFA). The approach involves the use of the l z * statistic, a corrected expected a posteriori estimate of the examinee ability, and the Monte Carlo (MC) resampling method. The Type I error rate of the approach was closer to the nominal level than that of the traditional approach of using l z * along with the assumption of a standard normal null distribution. This article suggests a generalized resampling‐based approach for PFA that allows one to employ l z * or another person‐fit statistic (PFS) based on item response theory, the corrected expected a posteriori estimate or another ability estimate, and the MC method or another resampling method. The suggested approach includes the approach of de la Torre and Deng as a special case. Several approaches belonging to the generalized approach perform very similarly to the approach of de la Torre and Deng's in two simulation studies and in applications to three real data sets, irrespective of the PFS used. The generalized approach promises to be useful to those interested in resampling‐based PFA.