Improving the Validity of Letters of Recommendation: An Investigation of Three Standardized Reference Forms
Bibliographic record
Abstract
Although letters of recommendation are (LORs) widely used, little research has examined how accurately they predict job performance. The few existing studies have yielded mixed results, and meta-analytic estimates of validity range from .14 to .27 (Hunter & Hunter, 1984 Hunter, J. E., & Hunter, R. F. (1984). Validity and utility of alternative predictors of job performance. Psychological Bulletin, 96, 72–98.[Crossref], [Web of Science ®] , [Google Scholar]; Reilly & Chao, 1982 Reilly, R. R., & Chao, G. T. (1982). Validity and fairness of some alternate employee selection procedures. Personnel Psychology, 35, 1–62.[Crossref], [Web of Science ®] , [Google Scholar]). This investigation was designed to improve predictive validity by developing a standardized reference form and evaluating 3 different rating formats: Multi-Item scales, Relative Percentile Method (RPM) scales, and Global Trait Rankings. A total of 520 individuals applied to the Canadian military, and 544 LORs were obtained. Complete predictor and criterion data were available for 57 participants. Regression analyses indicated that the validity of the RPM rating format (R2(adj) = .18; R(adj) = .42) was substantially higher than previous estimates of LOR validity. The 2 remaining methods produced nonsignificant results. Limitations of the study, suggestions for future research, and implications for the field are discussed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.223 | 0.583 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".