Evaluating interview criterion‐related validity for distinct constructs: A meta‐analysis
Bibliographic record
Abstract
Abstract The employment interview is used to assess myriad constructs to inform personnel selection decisions. This article describes the first meta‐analytic review of the criterion‐related validity of interview‐based assessments of specific constructs (i.e., related to task and contextual performance). As such, this study explores the suitability of the interview for predicting specific dimensions of performance, and furthermore, if and how interviews should be designed to inform the assessment of distinct constructs. A comprehensive search process identified k = 37 studies comprising N = 30,646 participants ( N = 4449 with the removal of one study). Results suggest that constructs related to task ( ρ = .30) and contextual ( ρ = .28) performance are assessed with similar levels of criterion‐related validity. Although interview evaluations of task and contextual performance constructs did not show discriminant validity within the interview itself, interview evaluations were more predictive of the targeted criterion construct than of alternative constructs. We further found evidence that evaluations of contextual performance constructs might particularly benefit from the adoption of more structured interview scoring procedures. However, we expect that new research on interview design factors may find additional moderating effects and we point to critical gaps in our current body of literature on employment interviews. These results illustrate how a construct‐specific approach to interview validity can spur new developments in the modeling, assessment, and selection of specific work performance constructs.
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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 | 0.274 | 0.410 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.036 |
| Bibliometrics | 0.015 | 0.012 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".