Key references

1 · The data and the core estimates

• Stanek, K. C., & Ones, D. S. (2023). Personality traits and cognitive abilities relations database [Data set]. University of Minnesota. The public database Study 1 re-analyzes: thousands of personality–ability correlations with sample-type codes. Openly licensed — you can check any number on this site against it.
• Sackett, P. R., Zhang, C., Berry, C. M., & Lievens, F. (2022). Revisiting meta-analytic estimates of validity in personnel selection. Journal of Applied Psychology, 107(11). The current standard for operational validities — the .31 (ability) and .19 (Conscientiousness) the calculator uses — and the paper that reset the field’s expectations about range-restriction corrections.
• Roth, P. L., Switzer, F. S. III, Van Iddekinge, C. H., & Oh, I.-S. (2011). Toward better meta-analytic matrices. Personnel Psychology, 64(4). The methodological warning this whole project acts on: the matrix values you feed a selection simulation change its conclusions, so the values must match the setting.
• Dahlke, J. A., & Sackett, P. R. (2017). The relationship between cognitive-ability saturation and subgroup mean differences across predictors. Journal of Applied Psychology, 102(10). Source of the subgroup-difference inputs behind the calculator’s adverse-impact ratios — the statistic the simulations show staying quiet while hires change.

2 · How common is faking?

• Griffith, R. L., Chmielowski, T., & Yoshita, Y. (2007). Do applicants fake? An examination of the frequency of applicant faking behavior. Personnel Review, 36(3). The classic within-person demonstration: the same people tested as applicants and again honestly. The origin of the 30–50% prevalence range.
• Donovan, J. J., Dwight, S. A., & Schneider, D. (2014). The impact of applicant faking on selection measures, hiring decisions, and employee performance. Journal of Business and Psychology, 29(3). The replication — and the study that connects within-person faking to hiring decisions and later job performance in real data.
• Griffith, R. L., & Converse, P. D. (2011). The rules of evidence and the prevalence of applicant faking. In New perspectives on faking in personality assessment. Oxford University Press. The synthesis that concludes roughly 30% ± 10% of applicants fake — the basis for this site’s headline and the calculator’s default.
• Birkeland, S. A., Manson, T. M., Kisamore, J. L., Brannick, M. T., & Smith, M. A. (2006). A meta-analytic investigation of job applicant faking on personality measures. International Journal of Selection and Assessment, 14(4). How much scores shift: the meta-analytic applicant–incumbent differences (Conscientiousness d = .45) every simulation on this site is required to reproduce.

3 · Who fakes?

• Holtrop, D., Oostrom, J. K., Dunlop, P. D., & Runneboom, C. (2021). Predictors of faking behavior on personality inventories in selection. International Journal of Selection and Assessment, 29. The only study of genuine job applicants (128 firefighter candidates) measuring who fakes: cognitive ability correlated −.19 with score elevation — the compensatory pattern at the center of this site.
• Dunlop, P. D., Holtrop, D., Ashby, L. M., Bharadwaj, A., & Donovan, J. J. (2022). Valence, instrumentality, expectancy, and ability as determinants of faking. Journal of Business and Psychology, 37(6). The incentivized-simulation counterpart: with real money on a simulated application, ability predicted faking positively (+.14) — the laboratory anchor set the manuscript’s simulations test alongside the field set, and the two disagree in exactly the way setting-dependence predicts.
• Weißenbacher, B., Jud, M., & Krammer, G. (2025). Testing personality in student selection: Dispositional but not situational characteristics predict faking. Frontiers in Psychology, 16. The counterpoint from a different selection setting (teacher-education admission, N = 448): ability weakly positively predicted inflation there — a reminder the direction can depend on the setting, which is why the manuscript scopes its claims to job applicants.
• Ones, D. S., Viswesvaran, C., & Reiss, A. D. (1996). Role of social desirability in personality testing for personnel selection: The red herring. Journal of Applied Psychology, 81(6). The meta-analytic correlations between each Big Five trait and social desirability scales — the basis for the rival "everyone exaggerates proportionally" model that the simulations test and reject.

4 · Pricing the consequences

• Schmidt, F. L., & Hunter, J. E. (1983). Individual differences in productivity: An empirical test of estimates derived from studies of selection procedure utility. Journal of Applied Psychology, 68(3). The utility convention (performance SD in dollars ≈ 40% of salary) behind every dollar figure on this site.
• Howard, M. C., & Van Zandt, E. C. (2020). The discriminant validity of honesty-humility: A meta-analysis of the HEXACO, Big Five, and Dark Triad. Journal of Research in Personality, 87. One of the two integrity anchors behind the 0.2–0.8 SD integrity gap for faked-in hires (the other: overt integrity-test correlations).

5 · The audit regime

• NYC Local Law 144 of 2021, N.Y.C. Admin. Code § 20-870 et seq. Automated employment decision tools (NYC DCWP). The emerging audit regime for automated hiring tools. Its required statistic — selection-rate impact ratios — is the one these simulations show is blind to faking’s composition costs.