Different approaches to modeling response styles in Divide-by-Total IRT models (Part I): A model integration

A large variety of item response theory (IRT) modeling approaches aim at measuring and correcting for response styles in rating data. Here, we integrate response style models of the divide-by-total model family into one superordinate framework that parameterizes response styles as person-specific shifts in threshold parameters. This superordinate framework allows us to structure and compare existing approaches to modeling response styles and therewith makes model-implied restrictions explicit. With a simulation study, we show how the new framework allows us to assess consequences of violations of model assumptions and to compare response style estimates across different model parameterizations. The integrative framework of divide-by-total modeling approaches facilitates the correction for and examination of response styles. In addition to providing a superordinate framework for psychometric research, it gives guidance to applied researchers for model selection and specification in psychological assessment.