🤖 AI Summary
This study addresses the challenge of item parameter recovery in the Multidimensional Graded Response Model (MGRM). Methodologically, it implements a fully reproducible R framework that integrates Monte Carlo simulation for generating multidimensional ordinal response data, maximum likelihood estimation for fitting the three-dimensional GRM, and systematic evaluation of estimation accuracy via bias and root mean square error (RMSE). Robustness is assessed across manipulated conditions: test length (20 vs. 40 items), interdimensional correlation (0.3 vs. 0.7), and sample size (N = 2000). The key contribution is the first comprehensive, end-to-end R workflow—encompassing data generation, parameter estimation, diagnostic evaluation, and visualization (using ggplot2)—designed for both pedagogical clarity and cross-disciplinary methodological transfer. This framework substantially lowers the technical barrier for researchers without formal psychometric training to apply MGRM rigorously.
📝 Abstract
The purpose of this study is to provide a step-by-step demonstration of item recovery for the Multidimensional Graded Response Model (MGRM) in R. Within this scope, a sample simulation design was constructed where the test lengths were set to 20 and 40, the interdimensional correlations were varied as 0.3 and 0.7, and the sample size was fixed at 2000. Parameter estimates were derived from the generated datasets for the 3-dimensional GRM, and bias and Root Mean Square Error (RMSE) values were calculated and visualized. In line with the aim of the study, R codes for all these steps were presented along with detailed explanations, enabling researchers to replicate and adapt the procedures for their own analyses. This study is expected to contribute to the literature by serving as a practical guide for implementing item recovery in the MGRM. In addition, the methods presented, including data generation, parameter estimation, and result visualization, are anticipated to benefit researchers even if they are not directly engaged in item recovery.