The Effect of Missing Item Data on the Relative Predictive Accuracy of Intimate Partner Violence Risk Assessment Tools
Bibliographic record
Abstract
In an innovative simulation study, Perley-Robertson et al. found that two correctional risk assessment tools were robust to missing data, with summation, proration, and multiple imputation producing nearly identical relative predictive validity results. However, the uniform deletion of items across cases may have preserved their risk rankings and, consequently, relative predictive accuracy. We extend this research by applying identical missing data conditions (1%–50% of items deleted in 10% increments) to one third, two thirds, and three thirds of a high-risk intimate partner violence (IPV) sample assessed on the Ontario Domestic Assault Risk Assessment (ODARA) and Spousal Assault Risk Assessment–Version 2 (SARA-V2; N = 267). Neither missing data nor the handling method affected relative predictive accuracy, though summation underestimated absolute risk. These findings support proration or multiple imputation when IPV risk scale items are missing within a research sample, and underscore that proration is preferable to summed totals in practice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".