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.
Original language | English (US) |
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Pages (from-to) | 513-538 |
Number of pages | 26 |
Journal | British Journal of Mathematical and Statistical Psychology |
Volume | 76 |
Issue number | 3 |
Early online date | May 21 2023 |
DOIs | |
State | Published - Nov 2023 |
Keywords
- Bayesian estimation
- Pólya-gamma data augmentation
- sequential response model
ASJC Scopus subject areas
- Statistics and Probability
- Arts and Humanities (miscellaneous)
- General Psychology