Fast Bayesian Estimation for the Four-Parameter Logistic Model (4PLM)
Author
Abstract
Suggested Citation
DOI: 10.1177/21582440211052556
Download full text from publisher
References listed on IDEAS
- A. Béguin & C. Glas, 2001. "MCMC estimation and some model-fit analysis of multidimensional IRT models," Psychometrika, Springer;The Psychometric Society, vol. 66(4), pages 541-561, December.
- Steven Andrew Culpepper, 2016. "Revisiting the 4-Parameter Item Response Model: Bayesian Estimation and Application," Psychometrika, Springer;The Psychometric Society, vol. 81(4), pages 1142-1163, December.
- R. Bock & Murray Aitkin, 1981. "Marginal maximum likelihood estimation of item parameters: Application of an EM algorithm," Psychometrika, Springer;The Psychometric Society, vol. 46(4), pages 443-459, December.
- J. Ramsay, 1991. "Kernel smoothing approaches to nonparametric item characteristic curve estimation," Psychometrika, Springer;The Psychometric Society, vol. 56(4), pages 611-630, December.
- Chalmers, R. Philip, 2012. "mirt: A Multidimensional Item Response Theory Package for the R Environment," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 48(i06).
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Christian A. Gregory, 2020. "Are We Underestimating Food Insecurity? Partial Identification with a Bayesian 4-Parameter IRT Model," Journal of Classification, Springer;The Classification Society, vol. 37(3), pages 632-655, October.
- Sainan Xu & Jing Lu & Jiwei Zhang & Chun Wang & Gongjun Xu, 2024. "Optimizing Large-Scale Educational Assessment with a “Divide-and-Conquer” Strategy: Fast and Efficient Distributed Bayesian Inference in IRT Models," Psychometrika, Springer;The Psychometric Society, vol. 89(4), pages 1119-1147, December.
- Christopher J. Urban & Daniel J. Bauer, 2021. "A Deep Learning Algorithm for High-Dimensional Exploratory Item Factor Analysis," Psychometrika, Springer;The Psychometric Society, vol. 86(1), pages 1-29, March.
- Yunxiao Chen & Xiaoou Li & Siliang Zhang, 2019. "Joint Maximum Likelihood Estimation for High-Dimensional Exploratory Item Factor Analysis," Psychometrika, Springer;The Psychometric Society, vol. 84(1), pages 124-146, March.
- Melissa Gladstone & Gillian Lancaster & Gareth McCray & Vanessa Cavallera & Claudia R. L. Alves & Limbika Maliwichi & Muneera A. Rasheed & Tarun Dua & Magdalena Janus & Patricia Kariger, 2021. "Validation of the Infant and Young Child Development (IYCD) Indicators in Three Countries: Brazil, Malawi and Pakistan," IJERPH, MDPI, vol. 18(11), pages 1-19, June.
- Björn Andersson & Tao Xin, 2021. "Estimation of Latent Regression Item Response Theory Models Using a Second-Order Laplace Approximation," Journal of Educational and Behavioral Statistics, , vol. 46(2), pages 244-265, April.
- Michael Edwards, 2010. "A Markov Chain Monte Carlo Approach to Confirmatory Item Factor Analysis," Psychometrika, Springer;The Psychometric Society, vol. 75(3), pages 474-497, September.
- Yoav Bergner & Peter Halpin & Jill-Jênn Vie, 2022. "Multidimensional Item Response Theory in the Style of Collaborative Filtering," Psychometrika, Springer;The Psychometric Society, vol. 87(1), pages 266-288, March.
- Javier Revuelta, 2004. "Analysis of distractor difficulty in multiple-choice items," Psychometrika, Springer;The Psychometric Society, vol. 69(2), pages 217-234, June.
- Gregory Camilli & Jean-Paul Fox, 2015. "An Aggregate IRT Procedure for Exploratory Factor Analysis," Journal of Educational and Behavioral Statistics, , vol. 40(4), pages 377-401, August.
- Joakim Wallmark & James O. Ramsay & Juan Li & Marie Wiberg, 2024. "Analyzing Polytomous Test Data: A Comparison Between an Information-Based IRT Model and the Generalized Partial Credit Model," Journal of Educational and Behavioral Statistics, , vol. 49(5), pages 753-779, October.
- Alexander Robitzsch, 2021. "A Comprehensive Simulation Study of Estimation Methods for the Rasch Model," Stats, MDPI, vol. 4(4), pages 1-23, October.
- Battauz, Michela & Vidoni, Paolo, 2022. "A likelihood-based boosting algorithm for factor analysis models with binary data," Computational Statistics & Data Analysis, Elsevier, vol. 168(C).
- Yang Liu & Jan Hannig, 2017. "Generalized Fiducial Inference for Logistic Graded Response Models," Psychometrika, Springer;The Psychometric Society, vol. 82(4), pages 1097-1125, December.
- Yan Huo & Jimmy de la Torre & Eun-Young Mun & Su-Young Kim & Anne Ray & Yang Jiao & Helene White, 2015. "A Hierarchical Multi-Unidimensional IRT Approach for Analyzing Sparse, Multi-Group Data for Integrative Data Analysis," Psychometrika, Springer;The Psychometric Society, vol. 80(3), pages 834-855, September.
- Steven Andrew Culpepper, 2019. "An Exploratory Diagnostic Model for Ordinal Responses with Binary Attributes: Identifiability and Estimation," Psychometrika, Springer;The Psychometric Society, vol. 84(4), pages 921-940, December.
- Isabel Gallego‐Alvarez & Eduardo Ortas & José Luis Vicente‐Villardón & Igor Álvarez Etxeberria, 2017. "Institutional Constraints, Stakeholder Pressure and Corporate Environmental Reporting Policies," Business Strategy and the Environment, Wiley Blackwell, vol. 26(6), pages 807-825, September.
- Yang Liu & Ji Seung Yang, 2018. "Bootstrap-Calibrated Interval Estimates for Latent Variable Scores in Item Response Theory," Psychometrika, Springer;The Psychometric Society, vol. 83(2), pages 333-354, June.
- Michela Gnaldi & Simone Del Sarto, 2024. "Validating Corruption Risk Measures: A Key Step to Monitoring SDG Progress," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 175(3), pages 1045-1071, December.
- Björn Andersson & Marie Wiberg, 2017. "Item Response Theory Observed-Score Kernel Equating," Psychometrika, Springer;The Psychometric Society, vol. 82(1), pages 48-66, March.
More about this item
Keywords
4PLM; mixture modeling; Gibbs sampler; BME; BE3M;All these keywords.
JEL classification:
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:sagope:v:11:y:2021:i:4:p:21582440211052556. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.