mixedsubjectsirt
Item Response Theory Calibration with a Mixed Subjects Design
Integrates large language model generated item responses into psychometric calibration studies through a mixed-subjects design for unidimensional two-parameter and one-parameter logistic item response theory models. Human pilot responses are augmented with model-generated responses using a prediction-powered inference estimator (Angelopoulos, Bates, Fannjiang, Jordan and Zrnic (2023) <doi:10.1126/science.adi6000>; Angelopoulos, Duchi and Zrnic (2023) <doi:10.48550/arXiv.2311.01453>) adapted to marginal maximum-likelihood estimation, following the mixed-subjects design of Broska, Howes and van Loon (2025) <doi:10.1177/00491241251326865>. The estimator is anchored to the human responses and is asymptotically unbiased for the human item parameters at any tuning weight; the weight on the synthetic responses is chosen to minimize propagated ability-score risk, down-weighting uninformative or biased generated responses. Louis-corrected sandwich standard errors, ability scoring, cross-fitted tuning, and scale linking are also provided.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
1.0.0 |
rolling linux/jammy R-4.5 | mixedsubjectsirt_1.0.0.tar.gz |
662.7 KiB |
1.0.0 |
rolling linux/noble R-4.5 | mixedsubjectsirt_1.0.0.tar.gz |
662.3 KiB |
1.0.0 |
rolling source/ R- | mixedsubjectsirt_1.0.0.tar.gz |
490.5 KiB |
1.0.0 |
latest linux/jammy R-4.5 | mixedsubjectsirt_1.0.0.tar.gz |
662.7 KiB |
1.0.0 |
latest linux/noble R-4.5 | mixedsubjectsirt_1.0.0.tar.gz |
662.3 KiB |
1.0.0 |
latest source/ R- | mixedsubjectsirt_1.0.0.tar.gz |
490.5 KiB |
1.0.0 |
2026-04-23 source/ R- | mixedsubjectsirt_1.0.0.tar.gz |
0 B |