Cohen's <i>d</i> Corrected for Case IV Range Restriction: A More Accurate Procedure for Evaluating Subgroup Differences in Organizational Research
Bibliographic record
Abstract
Organizational and staffing researchers are often interested in evaluating whether subgroup differences exist (e.g., between Caucasian and African‐American individuals) on predictors of job performance. To investigate subgroup differences, researchers often will collect data from current employees to make inferences about subgroup differences among job applicants. However, the magnitude of subgroup differences (i.e., Cohen's d) within incumbent samples may be different (i.e., smaller) than the magnitude of subgroup differences in applicant samples because selection of applicants typically reduces the variance of scores on the predictors (i.e., because lower scoring applicants are not selected). If researchers seek to generalize a d value in an incumbent sample to the applicant population, they may use Bobko, Roth, and Bobko's (correcting the effect size of d for range restriction and unreliability, 2001) Case II or III correction. By extension, Hunter, Schmidt, and Le (implications of direct and indirect range restriction for meta‐analysis methods and findings, 2006) have proposed a Case IV correction, which is more realistic than Bobko et al.'s approach. Therefore, this paper develops a Case IV correction for d (i.e., dc4). The simulation results showed that the dc4 was generally accurate across 6,000 simulation conditions. Moreover, 2 published datasets were reanalyzed to show the influence of the Case IV correction on d. In addition, implications and future directions of the dc4 are discussed.
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.
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 | 0.375 | 0.625 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.022 |
| Bibliometrics | 0.012 | 0.015 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.010 | 0.007 |
| Research integrity | 0.008 | 0.014 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".