Outlier detection in high-stakes certification testing

Recent developments of person-fit analysis in computerized adaptive testing (CAT) are discussed. Methods from statistical process control are presented that have been proposed to classify an item score pattern as fitting or misfitting the underlying item response theory (IRT) model in a CAT. Most person-fit research in CAT is restricted to simulated data. In this study, empirical data from a certification test were used. The item score patterns of 1,392 examinees were analyzed. Alternatives are discussed to generate norms so that bounds can be determined to classify an item score pattern as fitting or misfitting. Using bounds determined from a sample of a high-stakes certification test, the empirical analysis shows that the different types of misfit can be distinguished. Further applications using statistical process control methods to detect misfitting item score patterns are discussed.