On the Distribution of Job Performance: The Role of Measurement Characteristics in Observed Departures from Normality
Bibliographic record
Abstract
In a recent article, O'Boyle and Aguinis ( ) argued that job performance is not distributed normally but instead is nonnormal and highly skewed. However, we believe the extreme departures from normality observed by these authors may have been due to characteristics of performance measures used. To address this issue, we identify 7 measurement criteria that we argue must be present for inferences to be made about the distribution of job performance. Specifically, performance measures must: (a) reflect behavior, (b) include an aggregation of multiple behaviors, (c) include the full range of performers, (d) include the full range of performance, (e) be time bounded, (f) focus on comparable jobs, and (g) not be distorted by motivational forces. Next, we present data from a wide range of sources—including the workplace, laboratory, athletics, and computer simulations—that illustrate settings in which failing to meet one or more of these criteria led to a highly skewed distribution providing a better fit to the data than a normal distribution. However, measurement approaches that better align with the 7 criteria listed above resulted in a normal distribution providing a better fit. We conclude that large departures from normality are in many cases an artifact of measurement.
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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 | 0.154 | 0.474 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| 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; 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".