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

Cognitive 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 Polya-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 Polya-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.

Automated summary

Cognitive diagnostic models classify individuals into latent proficiency classes. Recent research focuses on implementing binary response models with a Polya-gamma data augmentation strategy using Bayesian Gibbs sampling. This paper proposes a sequential exploratory diagnostic model for ordinal response data and extends the Polya-gamma data augmentation strategy. Results from a Monte Carlo study and an application of the model are presented.