<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
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Assessing reliability of situational judgment tests ( SJTs ) in high‐stakes situations is problematic with reliability inappropriately measured by C ronbach'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 T ime 1 and α = .52 and stratified α = .51 at T ime 2. We used a student sample ( T ime 1: n = 185; T ime 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 S tudy 2, we assessed test–retest reliability with Human Resource professionals ( T ime 1: n = 94; T ime 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 T ime 1 and α = .61 and stratified α = .67 at T ime 2. We discuss the theoretical implications of the S tudy 1 results as well as the practical implications for use of SJTs in credentialing examinations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.107 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it