Situational bandwidth and the criterion-related validity of assessment center ratings: Is cross-exercise convergence always desirable?
Bibliographic record
Abstract
This research examines the relationship between the construct and criterion-related validity of assessment centers (ACs) based on how convergence of dimension ratings across AC exercises affects their ability to predict managerial performance. According to traditional multitrait-multimethod perspective, a high degree of convergence represents more reliable measurement and has the potential for better validity. In contrast, the concept of situational bandwidth suggests that behavior assessed under a dissimilar set of circumstances should result in a more comprehensive assessment of a candidate's tendencies even though ratings are less likely to show high convergence. To test these opposing viewpoints, data from 3 operational ACs were obtained along with experts' evaluations of exercise characteristics and supervisors' ratings of candidates' managerial performance. Across the 3 samples, AC ratings taken from exercises with dissimilar demands had higher estimates of criterion-related validity than ratings taken from similar exercises, even though the same dimension-different exercise correlations were substantially higher between similar exercises. Composites of ratings high in convergence did not emerge as better predictors of managerial performance, and validity particularly suffered when derived from ratings that converged as a result of exercises with similar demands. Implications for AC design 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 | 0.099 | 0.347 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".