A sequential exploratory diagnostic model using a P ólya-gamma data augmentation strategy

Br J Math Stat Psychol. 2023 Nov;76(3):513-538. doi: 10.1111/bmsp.12307. Epub 2023 May 21.ABSTRACTCognitive diagnostic models provide a framework for classifying individuals into latent proficiency classes, also known as attribute profiles. Recent research has examined the implementation of a Pólya-gamma data augmentation strategy binary response model using logistic item response functions within a Bayesian Gibbs sampling procedure. In this paper, we propose a sequential exploratory diagnostic model for ordinal response data using a logit-link parameterization at the category level and extend the Pólya-gamma data augmentation strategy to ordinal response processes. A Gibbs sampling procedure is presented for efficient Markov chain Monte Carlo (MCMC) estimation methods. We provide results from a Monte Carlo study for model performance and present an application of the model.PMID:37786373 | DOI:10.1111/bmsp.12307

Source: The British Journal of Mathematical and Statistical Psychology - October 3, 2023 Category: Statistics Authors: Auburn Jimenez James Joseph Balamuta Steven Andrew Culpepper Source Type: research