Detection of Aberrant Item Score Patterns: A Review of Recent Developments

Methods for detecting item score patterns that are unlikely (aberrant) given that a parametric item response theory (IRT) model gives an adequate description of the data or given the responses of the other persons in the group are discussed. The emphasis here is on the latter group of statistics. These statistics can be applied when a nonparametric model is used to fit the data or when the data are described in the absence of an IRT model. After discussion of the literature on person-fit methods, the use of person-fit statistics in empirical data analysis is briefly discussed. In some situations, the analysis of item score patterns might reveal more information about examinees than the analysis of test scores. Finding an aberrant pattern does not explain the reason for the aberrance. A full person-fit analysis requires additional research into the motives, strategies, and backgrounds of the examinees who deviate from the statistical norm set by the model or group. (Contains 47 references.)