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EHBEA Statement on National IQ Datasets. Issued July 2020

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EHBEA Statement on National IQ Datasets. Issued July 2020 EHBEA Statement on National IQ Datasets. Issued July 2020 Evolutionary behavioural sciences aim to understand human behaviour in a whole-species context. For that reason, cross-cultural work is of particular importance in our field. However, cross-cultural analysis must be based on reliable data. Multiple papers published in evolutionary journals (examples below 7-10) have used ‘national IQ’ data, developed by Richard Lynn, and datasets derived from this work1-6. Although these datasets are treated in these publications as neutral and objective indicators of variation in intelligence across countries and regions, the datasets fall a long way short of the expected scientific standard of rigour, in terms of both data curation and measure validity, as discussed below. Any conclusions drawn from analyses which use these data are therefore unsound, and no reliable evolutionary work should be using these data. There has been extensive documentation of the very poor quality of this dataset11-17. Notably, the empirical samples from which ‘national IQ’ was estimated are wholly inadequate for a large number of countries. Samples are often far too small to reliably generalise to the national population, and are frequently highly unrepresentative of the national population; in many cases ‘national IQ’ is estimated entirely from samples of children, and in other cases, samples were not even resident in the country in question. Furthermore, a large number of countries are not represented at all and are ascribed scores based on neighbouring countries. The inclusion and exclusion criteria for the dataset are also opaque, and it has been suggested that the original dataset was compiled in a systematically biased manner. For some countries, multiple samples existed but not all are included in the dataset. In some of these cases, samples with the lowest scores appear to have been selected17. That is to say, the dataset was compiled in a manner which resulted in artificially low scores for certain countries, particularly those with a majority Black population. This ‘national IQ’ dataset was initially compiled in 2002 and has been repeatedly updated. However, although some of the critiques we cite refer to earlier versions of the data, and some samples have been revised in later iterations, recent examinations of the latest version clearly demonstrate that all above critiques of the dataset remain valid13,19. Beyond the wholly inadequate construction of the dataset, there is a fundamental problem in trying to use Western IQ tests across diverse cultural settings. Work with the Tsimane20 and with children in Mali12, for instance, has demonstrated that low scores on IQ tests in these groups are not replicated in other, more culturally relevant or familiar tasks. Even those IQ tests which claim to be culture-neutral, such as the Cattel “Culture Fair Intelligence Test”, rely on modes of thinking which are routinely embedded in Western education systems (e.g. analysing 2-dimensional stimuli), but not reflective of skills and learning experiences of a large proportion of the global population18 (see also critiques by Gervais21). As such, these data are both systematically biased against some nationalities and ethnicities, and rely on an approach to understanding intelligence which lacks universal construct validity. Any conclusions drawn from these data are both untenable, and likely to give rise to racist conclusions, even where that is not the intention of the authors. To publish using a dataset which has been constructed in a wholly flawed manner violates the principles of scientific rigour our Society was founded on. The European Human Behaviour and Evolution Association committee: Prof Bogusław Pawlowski (President) Prof Lynda Boothroyd (Vice President) Dr Paula Sheppard (Secretary) Dr Max van Duijn (Treasurer) Dr Sally Street (Early Career Representative) Dr Andrzej Galbarczyk (Web Officer) Ms May Zhang (Student Representative) Prof Rebecca Sear (Ex. Officio EDI Champion) References IQ datasets discussed: 1. Becker, D. (2017). THE NIQ-DATASET. 2. Becker, D. (2019). The NIQ-Dataset 1.3. 2. 3. Lynn, R., & Becker, D. (2019). The intelligence of nations. Ulster Institute for Social Research. 4. Lynn, R., & Meisenberg, G. (2010). National IQs calculated and validated for 108 nations. Intelligence, 38(4), 353-360. 5. Lynn, R., & Vanhanen, T. (2006). IQ and global inequality. Washington Summit Publishers. 6. Lynn, R., Vanhanen, T., & Stuart, M. (2002). IQ and the wealth of nations. Greenwood Publishing Group. Example papers using these data: 7. Dutton, E., & Madison, G. (2017). Why do Finnish men marry Thai women but Finnish women marry British men? Cross-national marriages in a modern, industrialized society exhibit sex-dimorphic sexual selection according to primordial selection pressures. Evolutionary Psychological Science, 3(1), 1-9. 8. Eppig, C., Fincher, C. L., & Thornhill, R. (2010). Parasite prevalence and the worldwide distribution of cognitive ability. Proceedings of the Royal Society B: Biological Sciences, 277(1701), 3801-3808. 9. Figueredo, A. J., de Baca, T. C., Fernandes, H. B. F., Black, C. J., Peñaherrera, M., Hertler, S., ... & Meisenberg, G. (2017). A sequential canonical cascade model of social biogeography: Plants, parasites, and people. Evolutionary Psychological Science, 3(1), 40-61. 10. Luoto, S. (2019). An updated theoretical framework for human sexual selection: From ecology, genetics, and life history to extended phenotypes. Adaptive Human Behavior and Physiology, 1–55. (As a note on peer review: In this paper Lynn’s data became included through the peer review process.) Critiques and discussions of these data: 11. Dickins, T. E., Sear, R., & Wells, A. J. (2007). Mind the gap (s)… in theory, method and data: Re‐examining Kanazawa (2006). British journal of health psychology, 12(2), 167-178. 12. Dramé, C., & Ferguson, C. J. (2019). Measurements of Intelligence in sub-Saharan Africa: Perspectives Gathered from Research in Mali. Current Psychology, 38(1), 110-115. 13. Ebbesen, C. L. (2020, June 17). Flawed estimates of cognitive ability in Clark et al. Psychological Science, 2020. https://doi.org/10.31234/osf.io/tzr8c 14. Ellison, G. T. H. (2007). Health, wealth and IQ in sub-Saharan Africa: Challenges facing the ‘Savanna Principle’ as an explanation for global inequalities in health . British Journal of Health Psychology 12, 191–227 15. Richardson, K. (2004). IQ and the Wealth of Nations. Heredity, 92(4), 359–360. 16. Volken, T. (2003). IQ and the wealth of nations. A critique of Richard Lynn and Tatu Vanhanen's recent book. European Sociological Review, 19(4), 411-412. 17. Wicherts, J. M., Dolan, C. V., & van der Maas, H. L. (2010a). The dangers of unsystematic selection methods and the representativeness of 46 samples of African test-takers. Intelligence, 38(1), 30-37. 18. Wicherts, J. M., Borsboom, D., & Dolan, C. V. (2010b). Why national IQs do not support evolutionary theories of intelligence. Personality and Individual Differences, 48(2), 91-96. 19. Sear, R. Twitter thread: https://twitter.com/RebeccaSear/status/1271547090221572096 20. Piantadosi, S.T. Twitter thread: https://twitter.com/spiantado/status/1275783961499717632?s=20 21. Gervais, W. Twitter thread: https://twitter.com/wgervais/status/1271234490187182080 .
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