Different Approaches to Modeling Response Styles in Divide-By-Total Item Response Theory Models (Part 1): 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. Translational Abstract Responses to rating scales do not only contain the primary trait aimed to be measured, but also response tendencies, such as the tendency to prefer the extreme categories over others. Many item response theory (IRT) models aim to correct for such response tendencies by including them into the model. As there are many different variants of such response style IRT models, we propose a joint framework in which we structure and compare the models. With the framework, we highlight how response styles are specified and what assumptions are implicitly made on response styles. In a simulation study, we assess how the correlation between primary trait and response styles impact parameter estimation. In summary, the joint framework for response style IRT models guides applications of such models and supports informed model choice.