1. A sequential exploratory diagnostic model using a Pólya‐gamma data augmentation strategy.
- Author
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Jimenez, Auburn, Balamuta, James Joseph, and Culpepper, Steven Andrew
- Subjects
- *
DATA augmentation , *MARKOV chain Monte Carlo , *GIBBS sampling , *SAMPLING (Process) , *LATENT class analysis (Statistics) - Abstract
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 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. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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