Comparison of Person-Fit Statistics for Polytomous Items in Different Test Conditions

Abstract

The validity of individual test scores is an important issue that needs to be studied in psychological and educational assessment. An important factor affecting the validity of individual test scores is aberrant item response behavior. Aberrant item scores may increase/decrease the individuals' scores and as a result individuals' ability can be estimated above/below their true ability. Person-fit statistics (PFS) are useful tools to detect aberrant behavior. There are a great number of parametric and nonparametric PFS in the literature. The general purpose of the study is to examine the effectiveness of the parametric and nonparametric PFS in data sets which consist of polytomous items. This study is fundamental research aimed at determining the effectiveness of PFS using simulated data sets. According to the results, as expected, as the Type I error rates (significance alpha level) increased, detection rates (power) increased. In general, it is seen that as the number of misfitting item score vector and number of items increased, detection rates increased. Generally, nonparametric PFS (N-PFS) (especially G P) detected more aberrant individuals than parametric PFS (P-PFS) lz p. However, in some tests' conditions lz p detected more aberrant individuals than N-PFS for longer tests. The results indicate that N-PFS outperformed P-PFS in most of the test conditions.