Fast Bayesian Estimation for the Four-Parameter Logistic Model (4PLM)

Chanjin Zheng, Shaoyang Guo and Justin L Kern

SAGE Open, 2021, vol. 11, issue 4, 21582440211052556

Abstract: There is a rekindled interest in the four-parameter logistic item response model (4PLM) after three decades of neglect among the psychometrics community. Recent breakthroughs in item calibration include the Gibbs sampler specially made for 4PLM and the Bayes modal estimation (BME) method as implemented in the R package mirt. Unfortunately, the MCMC is often time-consuming, while the BME method suffers from instability due to the prior settings. This paper proposes an alternative BME method, the Bayesian Expectation-Maximization-Maximization-Maximization (BE3M) method, which is developed from by combining an augmented variable formulation of the 4PLM and a mixture model conceptualization of the 3PLM. The simulation shows that the BE3M can produce estimates as accurately as the Gibbs sampling method and as fast as the EM algorithm. A real data example is also provided.

Keywords: 4PLM; mixture modeling; Gibbs sampler; BME; BE3M (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://journals.sagepub.com/doi/10.1177/21582440211052556 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:sae:sagope:v:11:y:2021:i:4:p:21582440211052556

DOI: 10.1177/21582440211052556

Access Statistics for this article

More articles in SAGE Open
Bibliographic data for series maintained by SAGE Publications ().