Using Stan for Item Response Theory Models. Issue 2 (3rd April 2018)

• Record Type: Journal Article
• Authors: Ames, Allison J.; Au, Chi Hang
• Is Part Of: Measurement. Volume 16:Issue 2(2018)
• Page Start: 129
• Page End: 134
• Publication Date: 2018-04-03
• Subjects: Software review -- Item Response Theory -- Hamiltonian Monte Carlo -- Stan; Social sciences -- Methodology -- Periodicals; Social sciences -- Statistical methods -- Periodicals; Social sciences -- Mathematical models -- Periodicals; Measurement -- Periodicals; 300.72
• DOI: 10.1080/15366367.2018.1437304
• Languages: English
• ISSNs: 1536-6367

Abstract

Abstract: Stan is a flexible probabilistic programming language providing full Bayesian inference through Hamiltonian Monte Carlo algorithms. The benefits of Hamiltonian Monte Carlo include improved efficiency and faster inference, when compared to other MCMC software implementations. Users can interface with Stan through a variety of computing environments, including R, Python, MATLAB, Stata, and Mathematica. Programs written in Stan are portable across these interfaces, encouraging collaboration and transparency. These benefits, and others, offer several advantages for measurement practitioners; this review uses a simple example of Stan for a two-parameter logistic IRT model to illustrate the utility of Stan and its relevant features.