TY - JOUR
T1 - Rethinking the Dunning-Kruger effect
T2 - Negligible influence on a limited segment of the population
AU - Gignac, Gilles E.
N1 - Publisher Copyright:
© 2024 The Author
PY - 2024/5
Y1 - 2024/5
N2 - Gignac and Zajenkowski (2020) recommended testing the Dunning-Kruger (DK) hypothesis with a combination of polynomial regression and LOESS regression, as the conventional approach to testing the hypothesis (i.e., quartile split) confounds regression toward the mean and the better-than-average effect. Building upon Gignac and Zajenkowski (2020), an insightful method to estimate the magnitude and prevalence of a DK effect is introduced based on comparing linear and LOESS regression predicted values. Based on simulated data specified to exhibit a plausible DK effect for cognitive abilities, the magnitude of the DK effect was empirically modeled. The effect peaked at a 20-point relative overestimation at an IQ of 55, impacting only 0.14% of the population, and decreased to a 7-point relative overestimation at an IQ of 70, affecting 2.3% of the population. Analysing two large field data samples (N ≈ 3500 each) from participants who completed intelligence subtests in grammar and logical reasoning, the DK effect was found to account for a maximum relative ability overestimation of 7 to 9 percentile points. Notably, this effect was confined to only ≈ 0.2% of the participants (IQ ≈ 55), all of whom scored at chance levels. Finally, low levels of conditional reliability (≈ 0.40) at distribution extremes were found to complicate interpreting results that superficially support the DK hypothesis. It is concluded that, when analyzed using appropriate methods, it is unlikely that the DK effect will ever be demonstrated as an unambiguously meaningful psychological phenomenon affecting an appreciable portion of the population.
AB - Gignac and Zajenkowski (2020) recommended testing the Dunning-Kruger (DK) hypothesis with a combination of polynomial regression and LOESS regression, as the conventional approach to testing the hypothesis (i.e., quartile split) confounds regression toward the mean and the better-than-average effect. Building upon Gignac and Zajenkowski (2020), an insightful method to estimate the magnitude and prevalence of a DK effect is introduced based on comparing linear and LOESS regression predicted values. Based on simulated data specified to exhibit a plausible DK effect for cognitive abilities, the magnitude of the DK effect was empirically modeled. The effect peaked at a 20-point relative overestimation at an IQ of 55, impacting only 0.14% of the population, and decreased to a 7-point relative overestimation at an IQ of 70, affecting 2.3% of the population. Analysing two large field data samples (N ≈ 3500 each) from participants who completed intelligence subtests in grammar and logical reasoning, the DK effect was found to account for a maximum relative ability overestimation of 7 to 9 percentile points. Notably, this effect was confined to only ≈ 0.2% of the participants (IQ ≈ 55), all of whom scored at chance levels. Finally, low levels of conditional reliability (≈ 0.40) at distribution extremes were found to complicate interpreting results that superficially support the DK hypothesis. It is concluded that, when analyzed using appropriate methods, it is unlikely that the DK effect will ever be demonstrated as an unambiguously meaningful psychological phenomenon affecting an appreciable portion of the population.
KW - Conditional reliability
KW - Dunning-Kruger effect
KW - EAP factor scores
KW - Effect size
UR - http://www.scopus.com/inward/record.url?scp=85189657533&partnerID=8YFLogxK
U2 - 10.1016/j.intell.2024.101830
DO - 10.1016/j.intell.2024.101830
M3 - Article
AN - SCOPUS:85189657533
SN - 0160-2896
VL - 104
JO - Intelligence
JF - Intelligence
M1 - 101830
ER -