1. Overview | 2. Who should use aiDIF | 3. The example data | 4. Calibration and identification | 5. Independent and paired scoring designs | 6. Fitting | 7. Conventional DIF in each condition | 8. DASB, intervals, and multiplicity | 9. Describing the pattern of change | 10. Anchor diagnostics | 11. Visualisation | 12. Full report | 13. Using aiDIF after calibration with mirt | 14. Simulating item-parameter inputs | 15. Limitations | References
Package: aiDIF 0.2.0
aiDIF: Differential Item Functioning for AI-Scored Assessments
Detects and quantifies differential item functioning (DIF) in AI-scored educational and psychological assessments. Provides a fully self-contained robust DIF engine (M-estimation via iteratively re-weighted least squares with the bi-square loss) alongside the Differential AI Scoring Bias (DASB) test, which detects item-level scoring shifts that differ across subgroups when comparing human and AI scoring conditions. Supports independent and paired scoring designs, robust linking of the cross-condition contrast, multiplicity control, conversion of fitted 'mirt' models to package inputs, simulation utilities, anchor weight diagnostics, and an AI-effect classification framework. Methods follow Halpin (2024) <doi:10.1007/s11336-024-09957-6>.
Authors:
aiDIF_0.2.0.tar.gz
aiDIF_0.2.0.zip(r-4.7-any)aiDIF_0.2.0.zip(r-4.6-any)aiDIF_0.2.0.zip(r-4.5-any)
aiDIF_0.2.0.tgz(r-4.6-any)aiDIF_0.2.0.tgz(r-4.5-any)
aiDIF_0.2.0.tar.gz(r-4.7-any)aiDIF_0.2.0.tar.gz(r-4.6-any)
aiDIF_0.2.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
aiDIF/json (API)
| # Install 'aiDIF' in R: |
| install.packages('aiDIF', repos = c('https://subirhait.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/causalfragility-lab/aidif/issues
Last updated from:6668f5f135. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel | OK | 170 | ||
| source / vignettes | OK | 253 | ||
| linux-release | OK | 158 | ||
| macos-release | OK | 93 | ||
| macos-oldrel | OK | 113 | ||
| windows-devel | OK | 107 | ||
| windows-release | OK | 112 | ||
| windows-oldrel | OK | 113 | ||
| wasm-release | OK | 119 |
Exports:ai_effect_summaryanchor_weightsas_aidifestimate_robust_scalefit_aidifmake_aidif_egread_ai_scoredscoring_bias_testsimulate_aidif_data
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Summarise the effect of AI scoring on DIF flagging | ai_effect_summary |
| Anchor item weights from the robust AI-DIF procedure | anchor_weights |
| Convert a fitted IRT model into aiDIF input | as_aidif as_aidif.MultipleGroupClass |
| Robust DIF scale estimation via IRLS | estimate_robust_scale |
| Fit the AI-DIF model | fit_aidif |
| Built-in example dataset for aiDIF | make_aidif_eg |
| S3 plot method for class '"aidif"' | plot.aidif |
| S3 print method for class '"aidif"' | print.aidif |
| Print method for '"summary.aidif"' objects | print.summary.aidif |
| Validate and bundle paired human/AI parameter estimates | read_ai_scored |
| Differential AI Scoring Bias (DASB) test | scoring_bias_test |
| Simulate item parameter estimates for the AI-DIF model. | simulate_aidif_data |
| S3 summary method for class '"aidif"' | summary.aidif |