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

Auburn Jimenez, James Joseph Balamuta, Steven Andrew Culpepper

Research output: Contribution to journalArticlepeer-review

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 languageEnglish (US)
Pages (from-to)513-538
Number of pages26
JournalBritish Journal of Mathematical and Statistical Psychology
Volume76
Issue number3
DOIs
StatePublished - Nov 2023

Keywords

  • Bayesian estimation
  • Pólya-gamma data augmentation
  • sequential response model

ASJC Scopus subject areas

  • Arts and Humanities (miscellaneous)
  • Statistics and Probability
  • General Psychology

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