Faking by actual applicants on personality tests: A meta‐analysis of within‐subjects studies
Bibliographic record
Abstract
Abstract Background Researchers have used within‐subjects designs to assess personality faking in real‐world contexts. However, no research is available to (a) characterize the typical finding from these studies and (b) examine variability across study results. Aims The current study was aimed at filling these gaps by meta‐analyzing actual applicants’ responses to personality measurements in high‐stakes contexts versus low‐stakes contexts reported in within‐subjects studies. Materials & Methods This meta‐analysis examined 20 within‐subjects applicant–honest studies (where individuals completed an assessment once as applicants and again in a low‐stakes setting). Results We found that applicants had moderately higher (more socially desirable) means, slightly reduced variability, and stronger rank‐order consistency in high‐stakes settings. The assessment order moderated the findings; studies with a high‐to‐low order (where the high‐stakes setting was introduced first) showed a stronger faking effect—demonstrated by higher means and weaker rank‐order consistencies—than those in a low‐to‐high order. Discussion and Conclusion These findings are consistent with expectations that, relative to low‐stakes situations, individuals tend to exaggerate, in a positive direction, their personality descriptions as job applicants. In addition, assessment order matters when understanding the magnitudes of faking effects.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".