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Speed and the Flynn Effect

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  • Must, Olev
  • Must, Aasa

Abstract

We investigated the role of test-taking speed on the Flynn Effect (FE). Our study compared two cohorts of Estonian students (1933/36, n = 888; 2006, n = 912) using 9 subtests from the Estonian adaptation of the National Intelligence Tests (NIT). The speededness of the items and the subtests was found by determining the proportion of unreached items from among the total number of errors (Stafford, 1971). The test-taking speed of the younger cohort was higher in all 9 of the subtests. This suggests that the younger cohort is able to solve more items than the older one. The lack of measurement invariance at the item and subtest level was quantitatively estimated using a method proposed by Dimitrov (2017). The test-taking speed and the non-invariance of the items was strongly, yet inversely correlated (up to - 0.89). The subtests versions that consisted of only invariant items showed no, or a small positive, FE. The subtest versions consisting of only speeded items showed a large positive FE, with cohort differences of up to 50%. If the requirement of measurement invariance is ignored then this effect becomes apparent. The rise in test-taking speed between cohorts can be attributed to an increase in automated responses, which is an outgrowth of modern education (differences in the mandatory age of school attendance, and in the student's readiness to solve abstract items also affected the test-taking speed of the cohorts). We were able to conclude that the younger cohort is faster than the older one.

Suggested Citation

  • Must, Olev & Must, Aasa, 2018. "Speed and the Flynn Effect," Intelligence, Elsevier, vol. 68(C), pages 37-47.
    • Handle: RePEc:eee:intell:v:68:y:2018:i:c:p:37-47
    DOI: 10.1016/j.intell.2018.03.001
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    References listed on IDEAS

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    1. Feinian Chen & Patrick J. Curran & Kenneth A. Bollen & James Kirby & Pamela Paxton, 2008. "An Empirical Evaluation of the Use of Fixed Cutoff Points in RMSEA Test Statistic in Structural Equation Models," Sociological Methods & Research, , vol. 36(4), pages 462-494, May.
    2. Flynn, James R. & Shayer, Michael, 2018. "IQ decline and Piaget: Does the rot start at the top?," Intelligence, Elsevier, vol. 66(C), pages 112-121.
    3. R. Klein Entink & J.-P. Fox & W. Linden, 2009. "A Multivariate Multilevel Approach to the Modeling of Accuracy and Speed of Test Takers," Psychometrika, Springer;The Psychometric Society, vol. 74(1), pages 21-48, March.
    4. Ren, Xuezhu & Wang, Tengfei & Sun, Sumin & Deng, Mi & Schweizer, Karl, 2018. "Speeded testing in the assessment of intelligence gives rise to a speed factor," Intelligence, Elsevier, vol. 66(C), pages 64-71.
    5. Ledyard Tucker & Charles Lewis, 1973. "A reliability coefficient for maximum likelihood factor analysis," Psychometrika, Springer;The Psychometric Society, vol. 38(1), pages 1-10, March.
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    Cited by:

    1. Lazaridis, Alexandros & Vetter, Marco & Pietschnig, Jakob, 2022. "Domain-specificity of Flynn effects in the CHC-model: Stratum II test score changes in Germanophone samples (1996–2018)," Intelligence, Elsevier, vol. 95(C).
    2. Must, Olev & Must, Aasa, 2020. "No decline in the principle-based thought of 9-year-old schoolchildren between 1991 and 2001," Intelligence, Elsevier, vol. 80(C).
    3. Egeland, Jonathan, 2022. "The ups and downs of intelligence: The co-occurrence model and its associated research program," Intelligence, Elsevier, vol. 92(C).

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    Keywords

    Flynn Effect; NIT; Speed; Tork; Estonia;
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