The influence of test characteristics on the detection of aberrant response patterns

Statistical methods to assess the congruence between an item response pattern and a specified item response theory model have recently proliferated. This "person fit" research has focused on the question: To what extent can person-fit indices identify well-defined forms of aberrant item response? This study extended previous person-fit research in two ways. First, an unexplored model for generating aberrant response patterns was explicated. The data-generation model is based on the theory that aberrant item responses result in less psychometric information for the individual than predicated by the parameters of a specified response model. Second, the proposed response aberrancy generation model was implemented to investigate how the aberrancy detection power of a person-fit statistic is influenced by test properties (e.g., the spread of item difficulties). Results indicated that detecting aberrant response patterns was especially problematic for tests with less than 20 items, and for tests with limited ranges of item difficulty. An applied consequence of these results is that certain types of test designs (e.g., peaked tests) and administration procedures (e.g., adaptive tests) potentially act to limit the detection of aberrant item responses. Index terms: aberrancy detection, IRT, person fit, response aberrancy, Z₁ index