Exploration and analysis of a generalized one-parameter item response model with flexible link functions

This paper primarily analyzes the one-parameter generalized logistic (1PGlogit) model, which is a generalized model containing other one-parameter item response theory (IRT) models. The essence of the 1PGlogit model is the introduction of a generalized link function that includes the probit, logit, and complementary log-log functions. By transforming different parameters, the 1PGlogit model can flexibly adjust the speed at which the item characteristic curve (ICC) approaches the upper and lower asymptote, breaking the previous constraints in one-parameter IRT models where the ICC curves were either all symmetric or all asymmetric. This allows for a more flexible way to fit data and achieve better fitting performance. We present three simulation studies, specifically designed to validate the accuracy of parameter estimation for a variety of one-parameter IRT models using the Stan program, illustrate the advantages of the 1PGlogit model over other one-parameter IRT models from a model fitting perspective, and demonstrate the effective fit of the 1PGlogit model with the three-parameter logistic (3PL) and four-parameter logistic (4PL) models. Finally, we demonstrate the good fitting performance of the 1PGlogit model through an analysis of real data.

Automated summary

This paper primarily analyzes the 1PGlogit model, which is a generalized model containing other one-parameter IRT models. The 1PGlogit model introduces a generalized link function that includes the probit, logit, and complementary log-log functions, allowing for more flexible fitting of data and achieving better performance. The paper provides simulation studies to validate parameter estimation accuracy, compares the 1PGlogit model with other IRT models, and demonstrates the model's good fitting performance using real data.