IDEAS home Printed from https://ideas.repec.org/a/eee/intell/v92y2022ics0160289622000319.html
   My bibliography  Save this article

Specific cognitive aptitudes and gifted samples

Author

Listed:
  • Wai, Jonathan
  • Lakin, Joni M.
  • Kell, Harrison J.

Abstract

This paper explores the way in which the literatures on gifted education and specific cognitive aptitudes can be better integrated and inform one another to advance scientific knowledge. We first briefly review evidence accumulated to date on specific cognitive aptitudes and gifted samples and then explore what might be usefully investigated in the future. We consider measurement issues, value for applied uses of tests, specific cognitive aptitudes beyond what has been focused on to date and conclude with a discussion surrounding cross-field integration using the totality of evidence and consideration of policy. Continued research and better integration of research evidence across domains and translation to policy and practice might correspondingly improve basic scientific understanding of cognitive aptitudes.

Suggested Citation

  • Wai, Jonathan & Lakin, Joni M. & Kell, Harrison J., 2022. "Specific cognitive aptitudes and gifted samples," Intelligence, Elsevier, vol. 92(C).
    • Handle: RePEc:eee:intell:v:92:y:2022:i:c:s0160289622000319
    DOI: 10.1016/j.intell.2022.101650
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0160289622000319
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.intell.2022.101650?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. Yiu-Fai Yung & David Thissen & Lori McLeod, 1999. "On the relationship between the higher-order factor model and the hierarchical factor model," Psychometrika, Springer;The Psychometric Society, vol. 64(2), pages 113-128, June.
    2. Wai, Jonathan & Worrell, Frank C., 2021. "The future of intelligence research and gifted education," Intelligence, Elsevier, vol. 87(C).
    3. Kan, Kees-Jan & van der Maas, Han L.J. & Levine, Stephen Z., 2019. "Extending psychometric network analysis: Empirical evidence against g in favor of mutualism?," Intelligence, Elsevier, vol. 73(C), pages 52-62.
    4. Coyle, Thomas R., 2019. "Tech tilt predicts jobs, college majors, and specific abilities: Support for investment theories," Intelligence, Elsevier, vol. 75(C), pages 33-40.
    5. Blum, Diego & Holling, Heinz, 2017. "Spearman's law of diminishing returns. A meta-analysis," Intelligence, Elsevier, vol. 65(C), pages 60-66.
    6. Lang, Jonas W. B. & Bliese, Paul D., 2012. "I–O Psychology and Progressive Research Programs on Intelligence," Industrial and Organizational Psychology, Cambridge University Press, vol. 5(2), pages 161-166, June.
    7. Breit, Moritz & Scherrer, Vsevolod & Preckel, Franzis, 2021. "Temporal stability of specific ability scores and intelligence profiles in high ability students," Intelligence, Elsevier, vol. 86(C).
    8. Coyle, Thomas R. & Greiff, Samuel, 2021. "The future of intelligence: The role of specific abilities," Intelligence, Elsevier, vol. 88(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Coyle, Thomas R. & Greiff, Samuel, 2023. "Carbon is to life as g is to _____: A review of the contributions to the special issue on specific abilities in intelligence," Intelligence, Elsevier, vol. 101(C).

    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. Coyle, Thomas R., 2023. "Sex differences in tech tilt and academic tilt in adolescence: Processing speed mediates age-tilt relations," Intelligence, Elsevier, vol. 100(C).
    2. Coyle, Thomas R., 2022. "Sex differences in spatial and mechanical tilt: Support for investment theories," Intelligence, Elsevier, vol. 95(C).
    3. Coyle, Thomas R., 2022. "Processing speed mediates the development of tech tilt and academic tilt in adolescence," Intelligence, Elsevier, vol. 94(C).
    4. Haier, Richard J., 2021. "Are we thinking big enough about the road ahead? Overview of the special issue on the future of intelligence research," Intelligence, Elsevier, vol. 89(C).
    5. Coyle, Thomas R. & Greiff, Samuel, 2023. "Carbon is to life as g is to _____: A review of the contributions to the special issue on specific abilities in intelligence," Intelligence, Elsevier, vol. 101(C).
    6. Becker, David & Coyle, Thomas R. & Minnigh, Tyler L. & Rindermann, Heiner, 2022. "International differences in math and science tilts: The stability, geography, and predictive power of tilt for economic criteria," Intelligence, Elsevier, vol. 92(C).
    7. Feraco, Tommaso & Cona, Giorgia, 2022. "Differentiation of general and specific abilities in intelligence. A bifactor study of age and gender differentiation in 8- to 19-year-olds," Intelligence, Elsevier, vol. 94(C).
    8. Shelley H. Liu & Yitong Chen & Jordan R. Kuiper & Emily Ho & Jessie P. Buckley & Leah Feuerstahler, 2024. "Applying Latent Variable Models to Estimate Cumulative Exposure Burden to Chemical Mixtures and Identify Latent Exposure Subgroups: A Critical Review and Future Directions," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 16(2), pages 482-502, July.
    9. Demetriou, Andreas & Mougi, Antigoni & Spanoudis, George & Makris, Nicolaos, 2022. "Changing developmental priorities between executive functions, working memory, and reasoning in the formation of g from 6 to 12 years," Intelligence, Elsevier, vol. 90(C).
    10. Sumin Kim & Benson Teck Heng Lim & Bee Lan Oo, 2022. "Energy Consumption and Carbon Emissions of Mandatory Green Certified Offices in Australia: Evidence and Lessons Learnt across 2011–2020," Sustainability, MDPI, vol. 14(21), pages 1-18, October.
    11. Knyspel, Jacob & Plomin, Robert, 2024. "Comparing factor and network models of cognitive abilities using twin data," Intelligence, Elsevier, vol. 104(C).
    12. Sacha Epskamp, 2020. "Psychometric network models from time-series and panel data," Psychometrika, Springer;The Psychometric Society, vol. 85(1), pages 206-231, March.
    13. Carlo Cavicchia & Maurizio Vichi & Giorgia Zaccaria, 2020. "The ultrametric correlation matrix for modelling hierarchical latent concepts," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 14(4), pages 837-853, December.
    14. Li Cai, 2010. "A Two-Tier Full-Information Item Factor Analysis Model with Applications," Psychometrika, Springer;The Psychometric Society, vol. 75(4), pages 581-612, December.
    15. Woodley of Menie, Michael A. & Peñaherrera-Aguirre, Mateo & Sarraf, Matthew A., 2022. "Signs of a Flynn effect in rodents? Secular differentiation of the manifold of general cognitive ability in laboratory mice (Mus musculus) and Norwegian rats (Rattus norvegicus) over a century—Results," Intelligence, Elsevier, vol. 95(C).
    16. Kano, Yutaka & Takai, Keiji, 2011. "Analysis of NMAR missing data without specifying missing-data mechanisms in a linear latent variate model," Journal of Multivariate Analysis, Elsevier, vol. 102(9), pages 1241-1255, October.
    17. Fu, Zhihui & Zhang, Xue & Tao, Jian, 2020. "Gibbs sampling using the data augmentation scheme for higher-order item response models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 541(C).
    18. Coyle, Thomas R., 2021. "White-Black differences in tech tilt: Support for Spearman's law and investment theories," Intelligence, Elsevier, vol. 84(C).
    19. Rozgonjuk, Dmitri & Schmitz, Florian & Kannen, Christopher & Montag, Christian, 2021. "Cognitive ability and personality: Testing broad to nuanced associations with a smartphone app," Intelligence, Elsevier, vol. 88(C).
    20. Satomi Doi & Masaya Ito & Yoshitake Takebayashi & Kumiko Muramatsu & Masaru Horikoshi, 2018. "Factorial validity and invariance of the Patient Health Questionnaire (PHQ)-9 among clinical and non-clinical populations," PLOS ONE, Public Library of Science, vol. 13(7), pages 1-9, July.

    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:92:y:2022:i:c:s0160289622000319. 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.