Abstract

This article provides a review of the One Parameter Logistic Ability-based Guessing (1PL-AG) model (San Martin et al., 2006), which belongs to the family of ability-based guessing models within the Item Response Theory (IRT) framework. The model considers both the characteristics of the test items and the abilities of the individuals when estimating the probability of a correct guess and incorporates a general discrimination parameter to account for item difficulty. A comprehensive model that encompasses the 1PL-AG model as a specific instance, while employing a general Cumulative Distribution Function (CDF) as item characteristic curve (ICC) is introduced. Additionally, we explore another scenario, referred to as the One Parameter Normal Ogive Ability-based Guessing (1PNO-AG) model. 1PNO-AG employs the standard normal distribution as its link function and then a Bayesian approach is developed. Results considering simulations indicated that the R code developed with the use of JAGS successfully recovered the true parameter values. From an applied perspective, we compare the results obtained from applying various alternative models to a real dataset. We observed that the 1PNO-AG model exhibited superior performance in terms of the Deviance Information Criterion (DIC).