Digital Module 13: Simulation Studies in IRT
Recorded On: 04/03/2020
In this digital ITEMS module, Dr. Brian Leventhal and Dr. Allison Ames provide an overview of Monte Carlo simulation studies (MCSS) in item response theory (IRT). MCSS are utilized for a variety of reasons, one of the most compelling being that they can be used when analytic solutions are impractical or nonexistent because they allow researchers to specify and manipulate an array of parameter values and experimental conditions (e.g., sample size, test length, and test characteristics). Dr. Leventhal and Dr. Ames review the conceptual foundation of MCSS in IRT and walk through the processes of simulating total scores as well as item responses using the two-parameter logistic, graded response, and bi-factor models. They provide guidance for how to implement MCSS using other item response models and best practices for efficient syntax and executing an MCSS. The digital module contains sample SAS code, diagnostic quiz questions, activities, curated resources, and a glossary.
Key words: bias, bi-factor model, estimation, graded response model, item response theory, mean squared error, Monte Carlo, simulation, standard error, two-parameter logistic model
Brian is an assistant professor in the Assessment and Measurement PhD program in the Department of Graduate Psychology at James Madison University as well as an assistant assessment specialist in the Center for Assessment and Research Studies at James Madison University. There, he teaches courses in quantitative methods, including a course on Simulation Studies in Psychometrics. Brian received his Ph.D. from the University of Pittsburgh. His research interests include multidimensional item response models that account for response styles, response process models, and classification errors in testing. Brian is passionate about teaching and providing professional development for graduate students and early-career practitioners. He has thoroughly enjoyed collaborating with Allison Ames and the Instructional Design Team to develop this module.
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Allison J. Ames
Allison is an assistant professor in the Educational Statistics and Research Methods program in the Department of Rehabilitation, Human Resources and Communication Disorders, Research Methodology, and Counseling at the University of Arkansas. There, she teaches courses in educational statistics, including a course on Bayesian inference. Allison received her Ph.D. from the University of North Carolina at Greensboro. Her research interests include Bayesian item response theory, with an emphasis on prior specification; model-data fit; and models for response processes. Her research has been published in prominent peer-reviewed journals. She enjoyed collaborating on this project with a graduate student, senior faculty member, and the Instructional Design Team.
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