IDEAS home Printed from https://ideas.repec.org/a/eee/intell/v111y2025ics0160289625000261.html

Validating Rules: A non-verbal free fluid intelligence test

Author

Listed:
  • Van Cauwenberghe, Sofie
  • Schelfhout, Stijn
  • Roels, Elisabeth
  • Heeren, Jordi
  • De Wachter, Lieve
  • Duyck, Wouter
  • Dirix, Nicolas

Abstract

Intelligence is one of the strongest predictors of academic achievement. Fluid intelligence is one part of the construct, that can be measured by deductive and inductive reasoning. We set up a validation study of a free, non-verbal fluid intelligence test (Rules) in the context of study orientation. In this study, we investigate the reliability, distribution and structural validity of Rules, consisting of 28 items. Evidence from confirmatory multidimensional item response theory models suggests structural validity of the non-verbal reasoning test. For construct validity, a cross-validation between Rules and Raven's 2 Progressive Matrices in a sample of 235 last-year secondary school students resulted in a correlation of 0.62. Furthermore, we analyzed the predictive validity of the non-verbal reasoning test, which was administered to 32,585 last-year secondary school students. A standardized mathematics and language test were administered as a proxy for academic achievement scores. The results confirmed the predictive validity of the non-verbal reasoning test for cognitive achievement, with correlations of r = 0.61 for mathematics and r = 0.41 for language. Findings support the use of Rules in psychological practice, in particular for large-scale study exploration tools and low-stakes testing as a proxy for cognition or fluid reasoning.

Suggested Citation

  • Van Cauwenberghe, Sofie & Schelfhout, Stijn & Roels, Elisabeth & Heeren, Jordi & De Wachter, Lieve & Duyck, Wouter & Dirix, Nicolas, 2025. "Validating Rules: A non-verbal free fluid intelligence test," Intelligence, Elsevier, vol. 111(C).
    • Handle: RePEc:eee:intell:v:111:y:2025:i:c:s0160289625000261
    DOI: 10.1016/j.intell.2025.101923
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0160289625000261
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.intell.2025.101923?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

    for a different version of it.

    References listed on IDEAS

    as
    1. DeRue, D. Scott & Ashford, Susan J. & Myers, Christopher G., 2012. "Learning Agility: In Search of Conceptual Clarity and Theoretical Grounding," Industrial and Organizational Psychology, Cambridge University Press, vol. 5(3), pages 258-279, September.
    2. E. Cureton, 1966. "Corrected item-test correlations," Psychometrika, Springer;The Psychometric Society, vol. 31(1), pages 93-96, March.
    3. Marks, Gary N. & O'Connell, Michael, 2021. "No evidence for cumulating socioeconomic advantage. Ability explains increasing SES effects with age on children's domain test scores," Intelligence, Elsevier, vol. 88(C).
    4. 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).
    5. Carolin Strobl & Julia Kopf & Achim Zeileis, 2015. "Rasch Trees: A New Method for Detecting Differential Item Functioning in the Rasch Model," Psychometrika, Springer;The Psychometric Society, vol. 80(2), pages 289-316, June.
    6. W. Bashaw & Harry Anderson, 1967. "A correction for replicated error in correlation coefficients," Psychometrika, Springer;The Psychometric Society, vol. 32(4), pages 435-441, December.
    7. Rindermann, Heiner & Becker, David & Coyle, Thomas R., 2020. "Survey of expert opinion on intelligence: Intelligence research, experts' background, controversial issues, and the media," Intelligence, Elsevier, vol. 78(C).
    8. Richard Zinbarg & William Revelle & Iftah Yovel & Wen Li, 2005. "Cronbach’s α, Revelle’s β, and Mcdonald’s ω H : their relations with each other and two alternative conceptualizations of reliability," Psychometrika, Springer;The Psychometric Society, vol. 70(1), pages 123-133, March.
    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. 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).
    2. Piotr Koc, 2021. "Measuring Non-electoral Political Participation: Bi-factor Model as a Tool to Extract Dimensions," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 156(1), pages 271-287, July.
    3. Ke-Hai Yuan & Hongyun Liu & Yuting Han, 2021. "Differential Item Functioning Analysis Without A Priori Information on Anchor Items: QQ Plots and Graphical Test," Psychometrika, Springer;The Psychometric Society, vol. 86(2), pages 345-377, June.
    4. Ting Wang & Carolin Strobl & Achim Zeileis & Edgar C. Merkle, 2018. "Score-Based Tests of Differential Item Functioning via Pairwise Maximum Likelihood Estimation," Psychometrika, Springer;The Psychometric Society, vol. 83(1), pages 132-155, March.
    5. Gignac, Gilles E. & Ilić, David, 2025. "Psychometrically derived 60-question benchmarks: Substantial efficiencies and the possibility of human-AI comparisons," Intelligence, Elsevier, vol. 110(C).
    6. Jeanne A. Teresi & Chun Wang & Marjorie Kleinman & Richard N. Jones & David J. Weiss, 2021. "Differential Item Functioning Analyses of the Patient-Reported Outcomes Measurement Information System (PROMIS®) Measures: Methods, Challenges, Advances, and Future Directions," Psychometrika, Springer;The Psychometric Society, vol. 86(3), pages 674-711, September.
    7. Ting Wang & Benjamin Graves & Yves Rosseel & Edgar C. Merkle, 2022. "Computation and application of generalized linear mixed model derivatives using lme4," Psychometrika, Springer;The Psychometric Society, vol. 87(3), pages 1173-1193, September.
    8. Ting Wang & Carolin Strobl & Achim Zeileis & Edgar C. Merkle, 2016. "Score-Based Tests of Differential Item Functioning in the Two-Parameter Model," Working Papers 2016-05, Faculty of Economics and Statistics, Universität Innsbruck.
    9. Weimeng Wang & Yang Liu & Hongyun Liu, 2022. "Testing Differential Item Functioning Without Predefined Anchor Items Using Robust Regression," Journal of Educational and Behavioral Statistics, , vol. 47(6), pages 666-692, December.
    10. Izolda Pristojkovic Suko & Magdalena Holter & Erwin Stolz & Elfriede Renate Greimel & Wolfgang Freidl, 2022. "Acculturation, Adaptation, and Health among Croatian Migrants in Austria and Ireland: A Cross-Sectional Study," IJERPH, MDPI, vol. 19(24), pages 1-15, December.
    11. Benjamin W. Domingue & Klint Kanopka & Ben Stenhaug & Michael J. Sulik & Tanesia Beverly & Matthieu Brinkhuis & Ruhan Circi & Jessica Faul & Dandan Liao & Bruce McCandliss & Jelena Obradović & Chris , 2022. "Speed–Accuracy Trade-Off? Not So Fast: Marginal Changes in Speed Have Inconsistent Relationships With Accuracy in Real-World Settings," Journal of Educational and Behavioral Statistics, , vol. 47(5), pages 576-602, October.
    12. Anne-Catherine Guio & David Gordon & Eric Marlier & Hector Najera & Marco Pomati, 2018. "Towards an EU measure of child deprivation," Child Indicators Research, Springer;The International Society of Child Indicators (ISCI), vol. 11(3), pages 835-860, June.
    13. Nana Kim & Daniel M. Bolt & James Wollack, 2022. "Noncompensatory MIRT For Passage-Based Tests," Psychometrika, Springer;The Psychometric Society, vol. 87(3), pages 992-1009, September.
    14. Yoonhee Park & Doo Hun Lim & Jae Young Lee, 2022. "Internal Marketability, External Marketability, and Career Resilience: The Mediating Role of Learning Agility," Sustainability, MDPI, vol. 14(24), pages 1-14, December.
    15. Gaetano Martino & Giulia Giacchè & Enrica Rossetti, 2016. "Organizing the Co-Production of Health and Environmental Values in Food Production: The Constitutional Processes in the Relationships between Italian Solidarity Purchasing Groups and Farmers," Sustainability, MDPI, vol. 8(4), pages 1-22, March.
    16. Gayoung Kim & Woo Jin Lee, 2021. "The Venture Firm’s Ambidexterity: Do Transformational Leaders Boost Organizational Learning for Venture Growth?," Sustainability, MDPI, vol. 13(15), pages 1-15, July.
    17. Mi Jung Lee & Daejin Kim & Sergio Romero & Ickpyo Hong & Nikolay Bliznyuk & Craig Velozo, 2022. "Examining Older Adults’ Home Functioning Using the American Housing Survey," IJERPH, MDPI, vol. 19(8), pages 1-13, April.
    18. Luo, Nanyu & Ji, Feng & Han, Yuting & He, Jinbo & Zhang, Xiaoya, 2024. "Fitting item response theory models using deep learning computational frameworks," OSF Preprints tjxab, Center for Open Science.
    19. Peng Wang & Yuanxin Zheng & Mingzhu Zhang & Kexin Yin & Fei Geng & Fangxiao Zheng & Junchi Ma & Xiaojie Wu, 2024. "Methods for measuring career readiness of high school students: based on multidimensional item response theory and text mining," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 11(1), pages 1-15, December.
    20. Mohd Asrof Gulam Nabi & Wan Khairuzzaman Wan Ismail, 2016. "Job Crafting In Public Sector," International Journal of Business Research and Management (IJBRM), Computer Science Journals (CSC Journals), vol. 7(4), pages 63-81, October.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:111:y:2025:i:c:s0160289625000261. 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.