Literature DB >> 30140099

Using the Stan Program for Bayesian Item Response Theory.

Yong Luo1, Hong Jiao2.   

Abstract

Stan is a new Bayesian statistical software program that implements the powerful and efficient Hamiltonian Monte Carlo (HMC) algorithm. To date there is not a source that systematically provides Stan code for various item response theory (IRT) models. This article provides Stan code for three representative IRT models, including the three-parameter logistic IRT model, the graded response model, and the nominal response model. We demonstrate how IRT model comparison can be conducted with Stan and how the provided Stan code for simple IRT models can be easily extended to their multidimensional and multilevel cases.

Keywords:  Bayesian; Markov chain Monte Carlo (MCMC); item response theory (IRT)

Year:  2017        PMID: 30140099      PMCID: PMC6096466          DOI: 10.1177/0013164417693666

Source DB:  PubMed          Journal:  Educ Psychol Meas        ISSN: 0013-1644            Impact factor:   2.821


  2 in total

1.  Stochastic relaxation, gibbs distributions, and the bayesian restoration of images.

Authors:  S Geman; D Geman
Journal:  IEEE Trans Pattern Anal Mach Intell       Date:  1984-06       Impact factor: 6.226

2.  Using SAS PROC MCMC for Item Response Theory Models.

Authors:  Allison J Ames; Kelli Samonte
Journal:  Educ Psychol Meas       Date:  2014-09-25       Impact factor: 2.821

  2 in total
  10 in total

1.  A Short Note on Obtaining Point Estimates of the IRT Ability Parameter With MCMC Estimation in Mplus: How Many Plausible Values Are Needed?

Authors:  Yong Luo; Dimiter M Dimitrov
Journal:  Educ Psychol Meas       Date:  2018-05-29       Impact factor: 2.821

2.  A Comparison of Monte Carlo Methods for Computing Marginal Likelihoods of Item Response Theory Models.

Authors:  Yang Liu; Guanyu Hu; Lei Cao; Xiaojing Wang; Ming-Hui Chen
Journal:  J Korean Stat Soc       Date:  2019-05-17       Impact factor: 0.805

3.  Bridging Models of Biometric and Psychometric Assessment: A Three-Way Joint Modeling Approach of Item Responses, Response Times, and Gaze Fixation Counts.

Authors:  Kaiwen Man; Jeffrey R Harring; Peida Zhan
Journal:  Appl Psychol Meas       Date:  2022-05-27

4.  Bayesian Item Response Theory Models With Flexible Generalized Logit Links.

Authors:  Jiwei Zhang; Ying-Ying Zhang; Jian Tao; Ming-Hui Chen
Journal:  Appl Psychol Meas       Date:  2022-05-20

5.  How Do Test Takers Interact With Simulation-Based Tasks? A Response-Time Perspective.

Authors:  Yi-Hsuan Lee; Jiangang Hao; Kaiwen Man; Lu Ou
Journal:  Front Psychol       Date:  2019-04-24

6.  Modeling Dependence Structures for Response Times in a Bayesian Framework.

Authors:  Konrad Klotzke; Jean-Paul Fox
Journal:  Psychometrika       Date:  2019-05-16       Impact factor: 2.500

7.  Analysing Standard Progressive Matrices (SPM-LS) with Bayesian Item Response Models.

Authors:  Paul-Christian Bürkner
Journal:  J Intell       Date:  2020-02-04

8.  Regularized Bayesian calibration and scoring of the WD-FAB IRT model improves predictive performance over marginal maximum likelihood.

Authors:  Joshua C Chang; Julia Porcino; Elizabeth K Rasch; Larry Tang
Journal:  PLoS One       Date:  2022-04-08       Impact factor: 3.240

9.  Preventive chemotherapy reverses covert, lymphatic-associated tissue change in young people with lymphatic filariasis in Myanmar.

Authors:  Janet Douglass; Lukah Dykes; Louise Kelly-Hope; Susan Gordon; Peter Leggat; Ni Ni Aye; San San Win; Tint Wai; Yi Yi Win; Thet Wai Nwe; Patricia Graves
Journal:  Trop Med Int Health       Date:  2019-03-07       Impact factor: 2.622

10.  Longitudinal association between an overall diet quality index and latent profiles of cardiovascular risk factors: results from a population based 13-year follow up cohort study.

Authors:  Fatemeh Nouri; Masoumeh Sadeghi; Noushin Mohammadifard; Hamidreza Roohafza; Awat Feizi; Nizal Sarrafzadegan
Journal:  Nutr Metab (Lond)       Date:  2021-03-10       Impact factor: 4.169

  10 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.