Improving The Prediction Of Differential Item Functioning

Improving the Prediction of Differential Item Functioning

Psychometricians and test developers use DIF analysis to determine if there is possible bias in a given test item. This study examines the conditions under which two predominant methods for determining differential item function compare with each other in item bias detection using an effect size statistic as the basis for comparison. The main focus of the present research was to test whether or not incorporating an effect size for LR DIF will more accurately detect DIF and to compare the utility of an effect size index across MH DIF and LR DIF methods. A simulation study was used to compare the accuracy of MH DIF and LR DIF methods using a p value or supplemented with an effect size. Effect sizes were found to increase the accuracy of DIF and the possibility of the detection of DIF across varying ability distributions, population distributions, and sample size combinations. Varying ability distributions and sample size combinations affected the detection of DIF, while population distributions did not seem to affect the detection of DIF.

Applying Decision Research to Improve Clinical Outcomes, Psychological Assessment, and Clinical Prediction

Mental health professionals often must make judgments or decisions involving vital matters. Is an individual likely to act violently? Has a child been sexually abused? Is a police officer fit to carry a gun? An explosion of research in clinical and cognitive psychology provides practical means for enhancing the accuracy of clinical decision making and prediction and thereby improving outcomes and the quality of care. Unfortunately, this research has not been broadly disseminated in the mental health field. The book is designed to familiarize readers with essential findings from decision science and its practical, immediate applications in the mental health field.

Differential Item Functioning in the SAT I