On the Treatment of Outliners in Cognitive and Psychomotor Test Data

Authors of many statistical texts and review articles have pointed to the possi- ble adverse effects that outliers can have on the calculation of sample statistics and have suggested several methods for detecting and treating outliers. We investigated two different methods-data censoring and transformation-for treating outliers in aptitude test data at the item level and total-score level and their effects on the internal consistency and predictive validity of six computer- ized tests being evaluated by the U.S. Air Force. Results from our sample of more than 2,000 pilot training candidates indicated that neither outlier treat- ment method at either level of analysis had significant effects on the tests' internal consistencies or predictive validities. Possible reasons for these findings include the frequency with which outliers occur and the robustness of linear modeling methods.