Application of lasso penalization to differential item functioning detection

Identification of differential item functioning (DIF) in dichotomously scored items is often performed item by item. This approach increases the risk of false discovery errors (Type I error rate) as all items other than the tested one are assumed to be free of DIF. Some ad-hoc procedures, such as item purification and alpha level adjustment for multiple comparisons, have been studied in this context. The purpose of this talk is to focus on a different approach based on penalized likelihood estimation of a look-alike IRT model. Specifically, a Rasch model is being introduced with item-group interaction terms (i.e. DIF effects). Rather than obtaining pointwise estimates of the interaction parameters, which may be impossible because of high collinearity effects, the DIF effects are estimated with a lasso penalty term. Several criteria for optimally selecting the lasso tuning parameter are discussed, including cross-validation, AIC, BIC, and variants of these criteria. Preliminary results of a simulation study are presented and discussed.