<scp>A</scp>ssessing the <scp>R</scp>eliability of <scp>S</scp>ituational <scp>J</scp>udgment <scp>T</scp>ests <scp>U</scp>sed in <scp>H</scp>igh‐<scp>S</scp>takes <scp>S</scp>ituations
Bibliographic record
Abstract
Assessing reliability of situational judgment tests (SJTs) in high‐stakes situations is problematic with reliability inappropriately measured by Cronbach's alpha when test items are heterogeneous. We computed the corrected, weighted mean alpha from 56 alpha coefficients, which produced a value of α = .46 and reviewed appropriate types of reliability to use with SJTs. In the current longitudinal study, SJT test–retest reliability was r = .82, compared with internal consistency, α = .46, and stratified alpha, α = .45 at Time 1 and α = .52 and stratified α = .51 at Time 2. We used a student sample (Time 1: n = 185; Time 2: n = 132) with items from a credentialing exam with ‘should do’ instructions. The SJT correlated significantly with cognitive ability, r = .30, and agreeableness, r = .24. In Study 2, we assessed test–retest reliability with Human Resource professionals (Time 1: n = 94; Time 2: n = 32) who had been recently credentialed and who participated in a pilot test of new SJT items with ‘most likely/least likely do’ response options. The SJT test–retest reliability was r = .66 compared with internal consistency, α = .43 and stratified α = .47 at Time 1 and α = .61 and stratified α = .67 at Time 2. We discuss the theoretical implications of the Study 1 results as well as the practical implications for use of SJTs in credentialing examinations.
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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 | 0.010 | 0.076 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".