Use of Situational Judgment Tests in Personnel Selection: Are the different methods for scoring the response options equivalent?
Bibliographic record
Abstract
The different methods used to score the response options in situational judgment tests ( SJT s) carried out as part of the personnel selection process were compared by creating different keys for a single SJT , and the potential benefits of an innovative method combining existing methods were examined. The results, based on a sample of 1,194 candidates, point to some interesting differences between scoring methods. First, the innovative method created the lowest mean, near 60%. Second, the single‐best‐answer method produced the largest variance. The curve of the rank‐ordering method was the closest to a normal distribution. Finally, evidence suggests that the best‐and‐worst‐answer method and the innovative method provide the best results regarding construct validity. In sum, although no clear conclusion could be drawn about which methods should be preferred to score SJT s, results indicate that the new method could prove to be very interesting.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".