Latent structureScale / measurement
Bayesian Item Analysis
Bayesian item analysis applies Bayesian inference to estimate item-level statistics — difficulty, discrimination, and distractor effectiveness — by combining observed response data with prior knowledge. It produces full posterior distributions over item parameters rather than single point estimates, providing richer uncertainty information especially with small samples.
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Sources
- Fox, J.-P. (2010). Bayesian Item Response Modeling: Theory and Applications. Springer. DOI: 10.1007/978-1-4419-0742-4 ↗
- Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A. & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press. ISBN: 978-1439840955