A regularization approach for the detection of differential item functioning in generalized partial credit models

Most common analysis tools for the detection of differential item functioning (DIF) in item response theory are restricted to the use of single covariates. If several variables have to be considered, the respective method is repeated independently for each variable. We propose a regularization approach based on the lasso principle for the detection of uniform DIF. It is applicable to a broad range of polytomous item response models with the generalized partial credit model as the most general case. A joint model is specified where the possible DIF effects for all items and all covariates are explicitly parameterized. The model is estimated using a penalized likelihood approach that automatically detects DIF effects and provides trait estimates that correct for the detected DIF effects from different covariates simultaneously. The approach is evaluated by means of several simulation studies. An application is presented using data from the children's depression inventory.