IDEAS home Printed from https://ideas.repec.org/a/eee/intell/v104y2024ics0160289624000242.html
   My bibliography  Save this article

Rethinking the Dunning-Kruger effect: Negligible influence on a limited segment of the population

Author

Listed:
  • Gignac, Gilles E.

Abstract

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.

Suggested Citation

  • Gignac, Gilles E., 2024. "Rethinking the Dunning-Kruger effect: Negligible influence on a limited segment of the population," Intelligence, Elsevier, vol. 104(C).
    • Handle: RePEc:eee:intell:v:104:y:2024:i:c:s0160289624000242
    DOI: 10.1016/j.intell.2024.101830
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0160289624000242
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.intell.2024.101830?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Z. Adamec & K. Drápela, 2015. "Generalized additive models as an alternative approach to the modelling of the tree height-diameter relationship," Journal of Forest Science, Czech Academy of Agricultural Sciences, vol. 61(6), pages 235-243.
    2. Feld, Jan & Sauermann, Jan & de Grip, Andries, 2017. "Estimating the relationship between skill and overconfidence," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 68(C), pages 18-24.
    3. Goecke, B. & Weiss, S. & Steger, D. & Schroeders, U. & Wilhelm, O., 2020. "Testing competing claims about overclaiming," Intelligence, Elsevier, vol. 81(C).
    4. Gignac, Gilles E. & Zajenkowski, Marcin, 2019. "People tend to overestimate their romantic partner's intelligence even more than their own," Intelligence, Elsevier, vol. 73(C), pages 41-51.
    5. Rachel A. Jansen & Anna N. Rafferty & Thomas L. Griffiths, 2021. "A rational model of the Dunning–Kruger effect supports insensitivity to evidence in low performers," Nature Human Behaviour, Nature, vol. 5(6), pages 756-763, June.
    6. Gignac, Gilles E. & Zajenkowski, Marcin, 2020. "The Dunning-Kruger effect is (mostly) a statistical artefact: Valid approaches to testing the hypothesis with individual differences data," Intelligence, Elsevier, vol. 80(C).
    7. Feld, Jan & Sauermann, Jan & de Grip, Andries, 2017. "Estimating the relationship between skill and overconfidence," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 68(C), pages 18-24.
    8. Dunkel, Curtis S. & Nedelec, Joseph & van der Linden, Dimitri, 2023. "Reevaluating the Dunning-Kruger effect: A response to and replication of Gignac and Zajenkowski (2020)," Intelligence, Elsevier, vol. 96(C).
    9. Chalmers, R. Philip, 2012. "mirt: A Multidimensional Item Response Theory Package for the R Environment," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 48(i06).
    10. Myszkowski, Nils & Storme, Martin, 2018. "A snapshot of g? Binary and polytomous item-response theory investigations of the last series of the Standard Progressive Matrices (SPM-LS)," Intelligence, Elsevier, vol. 68(C), pages 109-116.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Dunkel, Curtis S. & Nedelec, Joseph & van der Linden, Dimitri, 2023. "Reevaluating the Dunning-Kruger effect: A response to and replication of Gignac and Zajenkowski (2020)," Intelligence, Elsevier, vol. 96(C).
    2. Miklánek, Tomáš & Zajíček, Miroslav, 2020. "Personal traits and trading in an experimental asset market," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 86(C).
    3. Yomna Atef Ahmed & ElHassan Anas ElSabry, 2024. "Evaluating the Performance of Foresight Studies: Evidence from the Egyptian Energy Sector," Foresight and STI Governance (Foresight-Russia till No. 3/2015), National Research University Higher School of Economics, vol. 18(1), pages 69-79.
    4. Ramzi Boussaidi, 2022. "Implications of the overconfidence bias in presence of private information: Evidence from MENA stock markets," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(3), pages 3660-3678, July.
    5. Gerardo Sabater-Grande & Nikolaos Georgantzís & Noemí Herranz-Zarzoso, 2023. "Goals and guesses as reference points: a field experiment on student performance," Theory and Decision, Springer, vol. 94(2), pages 249-274, February.
    6. Yao, Zheying & Rabbani, Abed G., 2021. "Association between investment risk tolerance and portfolio risk: The role of confidence level," Journal of Behavioral and Experimental Finance, Elsevier, vol. 30(C).
    7. Kovacs, Roxanne J. & Lagarde, Mylene & Cairns, John, 2020. "Overconfident health workers provide lower quality healthcare," Journal of Economic Psychology, Elsevier, vol. 76(C).
    8. Bonaccorsi, Andrea & Apreda, Riccardo & Fantoni, Gualtiero, 2020. "Expert biases in technology foresight. Why they are a problem and how to mitigate them," Technological Forecasting and Social Change, Elsevier, vol. 151(C).
    9. Wookjae Heo & Abed G. Rabbani & Jae Min Lee, 2021. "Mediation between financial risk tolerance and equity ownership: assessing the role of financial knowledge underconfidence," Journal of Financial Services Marketing, Palgrave Macmillan, vol. 26(3), pages 169-180, September.
    10. Jan R. Magnus & Anatoly A. Peresetsky, 2021. "A statistical explanation of the Dunning-Kruger effect," Working Papers w0286, New Economic School (NES).
    11. Melissa Gladstone & Gillian Lancaster & Gareth McCray & Vanessa Cavallera & Claudia R. L. Alves & Limbika Maliwichi & Muneera A. Rasheed & Tarun Dua & Magdalena Janus & Patricia Kariger, 2021. "Validation of the Infant and Young Child Development (IYCD) Indicators in Three Countries: Brazil, Malawi and Pakistan," IJERPH, MDPI, vol. 18(11), pages 1-19, June.
    12. Björn Andersson & Tao Xin, 2021. "Estimation of Latent Regression Item Response Theory Models Using a Second-Order Laplace Approximation," Journal of Educational and Behavioral Statistics, , vol. 46(2), pages 244-265, April.
    13. Victoria T. Tanaka & George Engelhard & Matthew P. Rabbitt, 2020. "Using a Bifactor Model to Measure Food Insecurity in Households with Children," Journal of Family and Economic Issues, Springer, vol. 41(3), pages 492-504, September.
    14. Çetin Toraman & Güneş Korkmaz, 2023. "What is the “Meaning of School†to High School Students? A Scale Development and Implementation Study Based on IRT and CTT," SAGE Open, , vol. 13(3), pages 21582440231, September.
    15. Cervantes, Víctor H., 2017. "DFIT: An R Package for Raju's Differential Functioning of Items and Tests Framework," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 76(i05).
    16. Elina Tsigeman & Sebastian Silas & Klaus Frieler & Maxim Likhanov & Rebecca Gelding & Yulia Kovas & Daniel Müllensiefen, 2022. "The Jack and Jill Adaptive Working Memory Task: Construction, Calibration and Validation," PLOS ONE, Public Library of Science, vol. 17(1), pages 1-29, January.
    17. Joshua B. Gilbert & James S. Kim & Luke W. Miratrix, 2023. "Modeling Item-Level Heterogeneous Treatment Effects With the Explanatory Item Response Model: Leveraging Large-Scale Online Assessments to Pinpoint the Impact of Educational Interventions," Journal of Educational and Behavioral Statistics, , vol. 48(6), pages 889-913, December.
    18. Bing Li & Cody Ding & Huiying Shi & Fenghui Fan & Liya Guo, 2023. "Assessment of Psychological Zone of Optimal Performance among Professional Athletes: EGA and Item Response Theory Analysis," Sustainability, MDPI, vol. 15(10), pages 1-15, May.
    19. Ick Hoon Jin & Minjeong Jeon, 2019. "A Doubly Latent Space Joint Model for Local Item and Person Dependence in the Analysis of Item Response Data," Psychometrika, Springer;The Psychometric Society, vol. 84(1), pages 236-260, March.
    20. Feld, Jan & Sauermann, Jan & de Grip, Andries, 2017. "Estimating the relationship between skill and overconfidence," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 68(C), pages 18-24.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:intell:v:104:y:2024:i:c:s0160289624000242. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/intelligence .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.