NotesFAQContact Us
Search Tips
Peer reviewed Peer reviewed
Direct linkDirect link
ERIC Number: EJ1174587
Record Type: Journal
Publication Date: 2018
Pages: 6
Abstractor: As Provided
ISSN: ISSN-1536-6367
Using Stan for Item Response Theory Models
Ames, Allison J.; Au, Chi Hang
Measurement: Interdisciplinary Research and Perspectives, v16 n2 p129-134 2018
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.
Routledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site:
Publication Type: Journal Articles; Reports - Evaluative
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Grant or Contract Numbers: N/A