Author(s): Wang Tao*
This article delves into the pivotal role of Item Response Theory (IRT) and, specifically, IRTree models in optimizing selection validity amidst the challenges posed by extreme response biases. Extreme response biases, characterized by consistent endorsement of extreme response categories, can introduce distortions and jeopardize the reliability of selection tools. The IRTree model, an advanced variant within the IRT framework, proves instrumental in identifying, analyzing, and mitigating these biases. Through a nuanced exploration of the advantages and implementation strategies associated with IRTree models, this article presents a comprehensive guide for organizations seeking to enhance the robustness of their selection processes in the face of extreme response biases. First, we present a simulation which demonstrates that when noise traits do exist, the selection decisions made based on the IRTree model estimated scores have higher accuracy rates and have less instances of adverse impact based on extreme response style group membership when compared to the GPCM. Both models performed similarly when there was no influence of noise traits on the responses. Second, we present an application using data collected from the Open-Source Psychometrics Project Fisher Temperament Inventory dataset. We found that the IRTree model had a better fit, but a high agreement rate between the model decisions resulted in virtually identical impact ratios between the models.
The Journal of International Social Research received 7760 citations as per Google Scholar report
