Differential Item Functioning in item response models:new estimators and diagnostic instruments

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Item response models build the methodological background for the measurement of performance and intelligence. The basic concept is that the solution of an item is determined by two latent traits, the ability of the person and the difficulty of the item. Item response models are intrinsically high-dimensional. The aim of the project is to develop regularization methods that are able to capture the deviation of specific items from the structure assumed by the model..The potential of regularized estimators is in particular used for the diagnosis of differential item functioning. Differential item functioning (DIF) describes the phenomenon that the difficulty of an item may vary over individuals depending on their social or cultural background yielding biased estimates of person abilities. It is planned to develop penalized likelihood and boosting methods that meet the complexity of the modeling task. Since the number of parameters is growing strongly when the potentially DIF-inducing variables are included all conventional estimators fail. The methods should be able to identify the items that are responsible for DIF and also find the predictors that are responsible for DIF. In addition, the methods are to be extended to fit models for items with ordered response. The methods to be developed are definitely beyond the methods that are available. The adaptation and modification of methods that have been first proposed in the machine learning community and in contemporary statistics to meet the challenges of DIF in item response models has just started. New methods are expected that crucially extend the tool box of available diagnostic instruments.