Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
Hostname: page-component-78c5997874-94fs2 Total loading time: 0 Render date: 2024-11-10T11:35:23.423Z Has data issue: false hasContentIssue false

16 - Race and Intelligence

It’s Not a Black and White Issue

from Part III - Intelligence and Group Differences

Published online by Cambridge University Press:  13 December 2019

By
Christine E. Daley  and
Anthony J. Onwuegbuzie
Edited by
Robert J. Sternberg
Robert J. Sternberg
Affiliation:
Cornell University, New York
Get access

Summary

The purpose of this chapter is to explore the extent that the claim of racial differences in intelligence represents a Black and White (i.e., absolute) issue, in a post-truth era characterized by discourses that are no longer moored in T/truth. Specifically, we summarize the debate over racial differences in intelligence. In so doing, we deconstruct the concepts of race and intelligence. Next, using Onwuegbuzie, Daniel, and Collins’s (2009) meta-validation model, we assess the fidelity of IQ tests. Then, we provide arguments that challenge hereditarian assumptions about the largely genetic nature of intelligence, including delineating evidence of the relationship between IQ and socioeconomic status (and its many correlates). We call for continued rigorously peer-reviewed research on race and intelligence, particularly with regard to the etiology of differences in IQ scores, wherein the investigators are comprehensive, transparent, and cautious, given the potential for divisiveness and far-reaching sociopolitical implications in a post-truth era.

Keywords

intelligenceintelligence testingIQIQ testingraceracial differences in intelligencepost-truth
Type
Chapter
Information
The Cambridge Handbook of Intelligence , pp. 373 - 394
Publisher: Cambridge University Press
Print publication year: 2020

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Adler, N. E., & Ostrove, J. M. (1999). Socioeconomic status and health: What we know and what we don’t. In Adler, N. E., Marmot, M., McEwen, B. S., & Stewart, J. (Eds.), Annals of the New York Academy of Sciences, vol. 896, Socioeconomic status and health in industrial nations: Social, psychological, and biological pathways. (pp. 3–15). New York: New York Academy of Sciences.Google Scholar
Amato, P. R., & Keith, B. (1991). Parental divorce and adult well-being: A meta-analysis. Journal of Marriage and the Family, 53, 43–58. https://doi.org/10.2307/353132Google Scholar
Anderson, N. B., & Armstead, C. A. (1995). Toward understanding the association of socioeconomic status and health: A new challenge for the biopsychosocial approach. Psychosomatic Medicine, 57, 213–225. https://doi.org/10.1097/00006842-199505000-00003Google Scholar
Barnett, W. S. (1998). Long-term cognitive and academic effects of early childhood education on children in poverty. Preventive Medicine, 27, 204–207. https://doi.org/10.1006/pmed.1998.0275Google Scholar
Baydar, N., Brooks-Gunn, J., & Furstenberg, F. (1993). Early warning signs of functional illiteracy: Predictors in childhood and adolescence. Child Development, 64, 815–829. https://doi.org/10.2307/1131220Google Scholar
Binet, A., & Simon, T. (1916). The development of intelligence in children (trans. E. S. Kite). Baltimore, MD: Williams & Wilkins.Google Scholar
Blake, J. (1989). Number of siblings and educational attainment. Science, 245, 32–37. https://doi.org/10.1126/science.2740913Google Scholar
Boring, E. G. (1923). Intelligence as the tests test it. New Republic, 36, 35–37.Google Scholar
Bornstein, M. H., & Bradley, R. H. (2003). Socioeconomic status, parenting, and child development. Mahwah, NJ: Lawrence Erlbaum.Google Scholar
Bouchard, T. J., Lykken, D. T., McGue, M., Segal, N. L., & Tellegen, A. (1990). Sources of human psychological differences: The Minnesota Study of Twins Reared Apart. Science, 250, 223–228. https://doi.org/10.1126/science.2218526Google Scholar
Bridgeman, B., & Buttram, J. (1975). Race differences on nonverbal analogy test performance as a function of verbal strategy training. Journal of Educational Psychology, 67, 586–590. https://doi.org/10.1037/h0077030Google Scholar
Brooks-Gunn, J., Guo, G., & Furstenberg, F. (1993). Who drops out of and who continues beyond high school? Journal of Research on Adolescence, 3, 271–294. https://doi.org/10.1207/s15327795jra0303_4Google Scholar
Brooks-Gunn, J., McCarton, C., Casey, P., McCormick, M., Bauer, C., Bernbaum, J., & Tyson, J. (1994). Early intervention in low birthweight, premature infants. Journal of the American Medical Association, 272, 1257–1262. https://doi.org/10.1001/jama.1994.03520160041040Google Scholar
Brown, J. L., & Pollitt, E. (1996). Malnutrition, poverty, and intellectual development. Scientific American, 274(2), 38–43. https://doi.org/10.1038/scientificamerican0296-38Google Scholar
Cantwell, M. F., Mckenna, M. T., McCray, E., & Onorato, I. M. (1998). Tuberculosis and race/ethnicity in the United States: Impact of socioeconomic status. American Journal of Respiratory and Critical Care Medicine, 157, 1016–1020. https://doi.org/10.1164/ajrccm.157.4.9704036CrossRefGoogle Scholar
Carroll, J. B. (1993). Human cognitive abilities: A survey of factor-analytic studies. Cambridge, MA: Cambridge University Press.Google Scholar
Caruso, J. C., & Cliff, N. (1998). The factor structure of the WAIS-R: Replicability across age-groups. Multivariate Behavioral Research, 33, 273–293. https://doi.org/10.1207/s15327906mbr3302_4Google Scholar
Ceci, S., & Williams, W. (2009). Darwin 200: Should scientists study race and IQ? YES: The scientific truth must be pursued. Nature, 457, 788–789. https://doi.org/10.1038/457788aCrossRefGoogle ScholarPubMed
Coleman, J. S. (1988). Social capital in the creation of human capital. American Journal of Sociology, 94, S95–S120. https://doi.org/10.1086/228943Google Scholar
Collins, K. M. T., Onwuegbuzie, A. J., & Sutton, I. L. (2006). A model incorporating the rationale and purpose for conducting mixed methods research in special education and beyond. Learning Disabilities: A Contemporary Journal, 4, 67–100.Google Scholar
Cundiff, J., Matthews, M., & Karen, A. (2017). Is subjective social status a unique correlate of physical health? A meta-analysis. Health Psychology, 36(12), 1109–1125. https://doi.org/10.1037/hea0000534Google Scholar
Cunningham, L. S., & Kelsey, J. L. (1984). Epidemiology of musculoskeletal impairments and associated disability. Journal of Public Health, 74, 574–579. https://doi.org/10.2105/AJPH.74.6.574Google Scholar
Dorn, C. (2017). For the common good: A new history of higher education in America. Ithaca, NY: Cornell University Press.Google Scholar
Eccles, J. S., Lord, S., & Midgley, C. (1991). What are we doing to early adolescents? The impact of educational context on early adolescents. American Journal of Education, 99, 521–542. https://doi.org/10.1086/443996Google Scholar
Elangovan, G. P., Muthu, J., Periyasamy, I. K., Balu, P., & Kumar, R. S. (2017). Self-reported prenatal oral health-care practices of preterm low birth weight-delivered women belonging to different socioeconomic status: A postnatal survey. Journal of Indian Society of Periodontology, 21, 489–493. https://doi.org/10.4103/jisp.jisp_79_16Google Scholar
Entwisle, D. R., & Astone, N. M. (1994). Some practical guidelines for measuring youth’s race/ethnicity and socioeconomic status. Child Development, 65, 1521–1540. https://doi.org/10.2307/1131278Google Scholar
Fagan, J. F. (1992). Intelligence: A theoretical viewpoint. Current Directions in Psychological Science, 1, 82–86. https://doi.org/10.1111/1467-8721.ep10768727Google Scholar
Fagan, J. F. (2000). A theory of intelligence as processing: Implications for society. Psychology, Public Policy, and Law, 6, 168–179. https://doi.org/10.1037/1076-8971.6.1.168Google Scholar
Fagan, J. F., & Holland, C. (2002). Equal opportunity and racial differences in IQ. Intelligence, 30, 361–387. https://doi.org/10.1016/S0160-2896(02)00080-6Google Scholar
Fagan, J. F., & Holland, C. (2007). Racial equality in intelligence: Predictions from a theory of intelligence as processing. Intelligence, 35, 319–334. https://doi.org/10.1016/j.intell.2006.08.009Google Scholar
Fagan, J. F., & Holland, C. (2009). Culture-fair prediction of academic achievement. Intelligence, 37, 62–67. https://doi.org/10.1016/j.intell.2008.07.004Google Scholar
Fernald, A., Marchman, V. A., & Weisleder, A. (2013). SES differences in language processing skill and vocabulary are evident at 18 months. Development Science, 16, 234–248. https://doi.org/10.1111/desc.12019Google Scholar
Fish, J. M. (2002). A scientific approach to understanding race and intelligence. In Fish, J. M. (Ed.), Race and intelligence: Separating science from myth (pp. 1–28). Mahwah, NJ: Erlbaum.Google Scholar
Flynn, J. R. (1987). Massive IQ gains in 4 nations: What IQ tests really measure. Psychological Bulletin, 101, 171–191. https://doi.org/10.1037/0033-2909.101.2.171Google Scholar
Foucault, M. (2003). Society must be defended: Lectures at the Collège de France 1975–1976. Eds. Bertani, M. & Fontana, A., trans. D. Macey. New York: Picador.Google Scholar
Frank, G. (1983). The Wechsler enterprise: An assessment of the development, structure, and use of the Wechsler test of intelligence. New York: Pergamon.Google Scholar
Galton, F. (1892). Hereditary genius. London: Macmillan.Google Scholar
Gardner, H. (1983). Frames of mind: The theory of multiple intelligences. New York: Basic Books.Google Scholar
Gardner, H. (1995). Cracking open the IQ box. In Fraser, S. (Ed.), The bell curve wars: Race, intelligence, and the future of America (pp. 23–35). New York: Basic Books.Google Scholar
Gardner, H. (2006). Multiple intelligences: New horizons. New York: Basic Books.Google Scholar
Geary, D. C., & Whitworth, R. H. (1988). Dimensional structure of the WAIS-R: A simultaneous multi-sample analysis. Educational and Psychological Measurement, 48, 945–956. https://doi.org/10.1177/0013164488484009CrossRefGoogle Scholar
Geiger, R. L. (2015). The history of American higher education: Learning and culture from the founding to World War II. Princeton: Princeton University Press.Google Scholar
Gleason, N. W. (Ed.) (2018). Higher education in the era of the fourth industrial revolution. London: Palgrave Macmillan.Google Scholar
Glenday, C. (Ed.) (2013). Guinness world records. London: Jim Pattison Group.Google Scholar
Gottfried, A. W., Gottfried, A. E., Bathurst, K., Guerin, D. W., & Parramore, M. M. (2003). Socioeconomic status in children’s development and family environment: Infancy through adolescence. In Bornstein, M. H. & Bradley, R. H. (Eds.), Socioeconomic status, parenting and child development (pp. 189–207). Mahwah, NJ: Erlbaum.Google Scholar
Gould, S. J. (1996). The mismeasure of man. New York: Norton.Google Scholar
Greenfield, P. M. (1998). The cultural evolution of IQ. In Neisser, U. (Ed.), The rising curve (pp. 81–124). Washington: American Psychological Association.Google Scholar
Haskins, R. (1989). Beyond metaphor: The efficacy of early childhood education. American Psychologist, 44, 274–282. https://doi.org/10.1037/0003-066X.44.2.274Google Scholar
Herrnstein, R. J., & Murray, C. (1994). The bell curve. New York: Simon & Schuster.Google Scholar
Hocutt, M., & Levin, M. (1999). The Bell Curve case for heredity. Philosophy of the Social Sciences, 29, 389–415. https://doi.org/10.1177/004839319902900303Google Scholar
Hoff, E. (2013). Interpreting the early language trajectories of children from low-SES and language minority homes: Implications for closing achievement gaps. Developmental Psychology, 49, 4–14. https://doi.org/10.1037/a0027238Google Scholar
Hoffman, S. (2006). “Racially tailored” medicine unraveled. American University Law Review, 55, 395–452.Google Scholar
Hunt, E., & Carlson, J. (2007a). Considerations relating to the study of group differences in intelligence. Perspectives on Psychological Science, 2, 194–213. https://doi.org/10.1111/j.1745-6916.2007.00037.xGoogle Scholar
Hunt, E., & Carlson, J. (2007b). The standards for conducting research on topics of immediate social relevance. Intelligence, 35, 393–399. https://doi.org/10.1016/j.intell.2006.10.002CrossRefGoogle Scholar
Jackson, A. P., Brooks-Gunn, J., Huang, C., & Glassman, M. (2000). Single mothers in low-wage jobs: Financial strain, parenting and preschoolers’ outcomes. Child Development, 71, 1409–1423. https://doi.org/10.1111/1467-8624.00236Google Scholar
Jazayeri, A. R., & Poorshahbaz, A. (2003). Reliability and validity of Wechsler Intelligence Scale for Children – Third Edition (WISC-III) in Iran. Journal of Medical Education, 2, 75–80.Google Scholar
Jensen, A. R. (1969). How much can we boost IQ and scholastic achievement? Harvard Educational Review, 39(1), 1–123. https://doi.org/10.17763/haer.39.1.l3u15956627424k7Google Scholar
Jensen, A. R. (1998). The g factor: The science of mental ability. Westport, CT: Praeger.Google Scholar
Johnson, R. B., & Onwuegbuzie, A. J. (2004). Mixed methods research: A research paradigm whose time has come. Educational Researcher, 33(7), 14–26. https://doi.org/10.1177/1558689806298224Google Scholar
Jokela, M., Elovainio, M., Singh-Manoux, A., & Kivimäki, M. (2009). IQ, socioeconomic status, and early death: The US National Longitudinal Survey of Youth. Psychosomatic Medicine, 71, 322–328. https://doi.org/10.1097/PSY.0b013e31819b69f6CrossRefGoogle ScholarPubMed
Kamin, L. J. (1997). Twin studies, heritability, and intelligence. Science, 278, 1385.Google Scholar
Kamphaus, R. W., Benson, J., Hutchison, S., & Platt, I. O. (1994). Identification of factor models for the WISC-III. Educational and Psychological Measurement, 54, 174–186. https://doi.org/10.1177/0013164494054001023Google Scholar
Kaplan, G. A., & Keil, J. E. (1993). Socioeconomic factors and cardiovascular disease: A review of the literature. Circulation, 88, 1973–1998. https://doi.org/10.1161/01.CIR.88.4.1973Google Scholar
Kapp, D. S., Chan, J., & Mann, A. (2018). Socioeconomic disparities in inflammatory response on cancer mortality using national health and nutrition examination survey. Journal of Clinical Ontology, e13574. https://doi.org/10.1200/JCO.2018.36.15_suppl.e13574CrossRefGoogle Scholar
Kaufman, A. S., & Kaufman, N. L. (2004). Kaufman Assessment Battery for Children – Second Edition. San Antonio, TX: Pearson/PsychCorp.Google Scholar
Kolar, G. M. (2001). A literature review and critical analysis of the concurrent validity of the Differential Ability Scales and the Cognitive Assessment System. Unpublished master’s thesis, University of Wisconsin-Stout, Menomonie, Wisconsin.Google Scholar
Layzer, D. (1995). Science or superstition? In Jacoby, R. & Glauberman, N. (Eds.), The bell curve debate: History, documents, opinions (pp. 653–681). New York: Times Books/Random House.Google Scholar
Lewontin, R. C. (1982). Human diversity. New York: Freeman.Google Scholar
Li, D., & Koedel, C. (2017). Representation and salary gaps by race-ethnicity and gender at selective public universities. Educational Researcher, 46, 343–354. https://doi.org/10.3102/0013189X17726535Google Scholar
Lia-Hoagberg, B., Rode, P., Skovholt, C., Oberg, C., Berg, C., Mullett, S., & Choi, T. (1990). Barriers and motivators to prenatal care among low-income women. Social Science and Medicine, 30, 487–495. https://doi.org/10.1016/0277-9536(90)90351-RGoogle Scholar
Liaw, F. R., & Brooks-Gunn, J. (1994). Cumulative familial risks and low birthweight children’s cognitive and behavioral development. Journal of Clinical Child Psychology, 23, 360–372. https://doi.org/10.1207/s15374424jccp2304_2Google Scholar
Lind, M. (1995). Brave new right. In Fraser, S. (Ed.), The bell curve wars: Race, intelligence, and the future of America (pp. 172–178). New York: Basic Books.Google Scholar
Littlefield, A., Lieberman, L., & Reynolds, L. T. (1982). Redefining race: The potential demise of a concept in anthropology. Current Anthropology, 23, 641–647. https://doi.org/10.1086/202915Google Scholar
Markant, J., Ackerman, L. K., Nussenbaum, K., & Amso, D. (2016). Selective attention neutralizes the adverse effects of low socioeconomic status on memory in 9-month-old infants. Developmental Cognitive Neuroscience, 18, 26–33. https://doi.org/10.1016/j.dcn.2015.10.009Google Scholar
Massey, J. T. (1980). A comparison of interviewer observed race and respondent reported race in the National Health Interview Survey. In Proceedings of the American Statistical Association, Social Statistics Section (pp. 425–428). Washington: American Statistical Association.Google Scholar
Matthews, K. A., Kelsey, S. F., Meilahn, E. N., Kuller, L. H., & Wing, R. R. (1989). Educational attainment and behavioral and biologic risk factors for coronary heart disease in middle-aged women. American Journal of Epidemiology, 129, 1132–1144. https://doi.org/10.1093/oxfordjournals.aje.a115235Google Scholar
McCardle, J. J. (1998). Contemporary statistical models for examining test bias. In McCardle, J. J. & Woodcock, R. W. (Eds.), Human cognitive abilities: Theory and practice (pp. 157–196). Mahwah, NJ: Erlbaum.Google Scholar
McLoyd, V. C. (1998). Socioeconomic disadvantage and child development. American Psychologist, 53, 185–204. https://doi.org/10.1037/0003-066X.53.2.185Google Scholar
Messick, S. (1989). Validity. In Linn, R. L. (Ed.), Educational measurement (3rd ed., pp. 13–103). Old Tappan, NJ: Macmillan.Google Scholar
Messick, S. (1995). Validity of psychological assessment: Validation of inferences from persons’ responses and performances as scientific inquiry into score meaning. American Psychologist, 50, 741–749. https://doi.org/10.1037/0003-066X.50.9.741Google Scholar
Naglieri, J. A., & Das, J. P. (1997). Das-Naglieri Cognitive Assessment System. Rolling Meadows, IL: Riverside.Google Scholar
National Research Council (1999). Equity and adequacy in education finance: Issues and perspectives. Washington: National Research Council Committee on Education Finance.Google Scholar
Neisser, U. (1998). Rising test scores. In Neisser, U. (Ed.), The rising curve (pp. 3–22). Washington: American Psychological Association.Google Scholar
Nisbett, R. (1995). Race, IQ, and scientism. In Fraser, S. (Ed.), The bell curve wars: Race, intelligence, and the future of America (pp. 36–57). New York: Basic Books.Google Scholar
Noble, K., Norman, M., & Farah, M. (2005). Neurocognitive correlates of socioeconomic status in kindergarten children. Developmental Science, 8(1), 74–87. https://doi.org/10.1111/j.1467-7687.2005.00394.xGoogle Scholar
O’Campo, P., Xue, X., Wang, M. C., & Caughy, M. (1997). Neighborhood risk factors for low birthweight in Baltimore: A multilevel analysis. American Journal of Public Health, 87, 1113–1118. https://doi.org/10.2105/AJPH.87.7.1113Google Scholar
O’Grady, K. (1989). Factor structure of the WISC-R. Multivariate Behavioral Research, 24, 177–193. https://doi.org/10.1207/s15327906mbr2402_3Google Scholar
O’Grady, K. (1990). A confirmatory maximum factor analysis of the WPPSI. Personality and Individual Differences, 11, 135–190. https://doi.org/10.1016/0191-8869(90)90005-CGoogle Scholar
Onwuegbuzie, A. J. (2003). Expanding the framework of internal and external validity in quantitative research. Research in the Schools, 10(1), 71–90.Google Scholar
Onwuegbuzie, A. J., Bustamante, R. M., & Nelson, J. A. (2010). Mixed research as a tool for developing quantitative instruments. Journal of Mixed Methods Research, 4, 56–78. https://doi.org/10.1177/1558689809355805Google Scholar
Onwuegbuzie, A. J., & Daley, C. E. (1996). Myths surrounding racial differences in intelligence: A statistical, sociological, social psychological, and historical critique of The Bell Curve. Paper presented to students and faculty at the University of Cape Town, South Africa, May.Google Scholar
Onwuegbuzie, A. J., & Daley, C. E. (2001). Racial differences in IQ revisited: A synthesis of nearly a century of research. Journal of Black Psychology, 27, 209–220. https://doi.org/10.1177/0095798401027002004Google Scholar
Onwuegbuzie, A. J., Daniel, L. G., & Collins, K. M. T. (2009). A meta-validation model for assessing the score-validity of student teacher evaluations. Quality and Quantity: International Journal of Methodology, 43, 197–209. https://doi.org/10.1007/s11135-007-9112-4Google Scholar
Pamuk, E., Makuc, D., Heck, K., Reuben, C., & Lochner, K. (1998). Socioeconomic status and health chartbook. Health, United States, 1998. Hyattsville, MD: National Center for Health Statistics.Google Scholar
Pearson, H. (1995). Developing the rage to win. In Fraser, S. (Ed.), The bell curve wars: Race, intelligence, and the future of America (pp. 164–171). New York: Basic Books.Google Scholar
Pesta, B. J., & Poznanski, P. J. (2014). Only in America: Cold winters theory, race, IQ and well-being. Intelligence, 46, 271–274. https://doi.org/10.1016/j.intell.2014.07.009Google Scholar
Plomin, R., & Kosslyn, S. M. (2001). Genes, brain and cognition. Nature Neuroscience, 4, 1153–1154. https://doi.org/10.1038/nn1201-1153Google Scholar
Raven, J., Raven, J. C., & Court, J. H. (1995). Manual for Raven’s Progressive Matrices and Vocabulary Scales (Section J, General Overview).Oxford: Oxford Psychologists Press.Google Scholar
Raver, C. C., Blair, C., & Willoughby, M. (2013). Poverty as a predictor of 4-year-olds’ executive function: New perspectives on models of differential susceptibility. Developmental Psychology, 49, 292–304. https://doi.org/10.1037/a0028343Google Scholar
Ridley, M. (2003). Nature via nurture: Genes, experience, and what makes us human. New York: HarperCollins.Google Scholar
Rindermann, H., Becker, D., & Coyle, T. R. (2016). Survey of expert opinion on intelligence: Causes of international differences in cognitive ability tests. Frontiers in Psychology, 7. https://doi.org/10.3389/fpsyg.2016.00399Google Scholar
Robbins, J. M., Vaccarino, V., Zhang, H., & Kasl, S. V. (2001). Socioeconomic status and type 2 diabetes in African American and non-Hispanic white women and men: Evidence from the Third National Health and Nutrition Examination Survey. American Journal of Public Health, 91, 76–83. https://doi.org/10.2105/AJPH.91.1.76Google Scholar
Roid, G. H. (2003). Stanford-Binet Intelligence Scales – Fifth Edition. Rolling Meadows, IL: Riverside.Google Scholar
Rosenberg, N. A., Pritchard, J. K., Weber, J. L., Cann, H. M., Field, K. K., Zhivotovsky, L. A., et al. (2002). Genetic structure of human populations. Science, 298, 2381–2385. https://doi.org/10.1126/science.1078311Google Scholar
Rushton, J. P. (2000). Race, evolution, and behavior: A life-history perspective (3rd ed.). Port Huron, MI: Charles Darwin Research Institute.Google Scholar
Rushton, J. P., Skuy, M., & Fridjohn, P. (2003). Performance on Raven’s Advanced Progressive Matrices by African, East Indian, and White engineering students in South Africa. Intelligence, 31, 123–137. https://doi.org/10.1016/S0160-2896(02)00140-XGoogle Scholar
Schaefer, R. T. (1988). Racial and ethnic groups (3rd ed.). Glenview, IL: Scott Foresman.Google Scholar
Skuy, M., Gewer, A., Osrin, Y., Khunou, D., Fridjohn, P., & Rushton, J. P. (2002). Effects of mediated learning experiences on Raven’s matrices scores of African and non-African university students in South Africa. Intelligence, 30, 221–232. https://doi.org/10.1016/S0160-2896(01)00085-XGoogle Scholar
Smedley, A., & Smedley, B. (2005). Race as biology is fiction, racism as a social problem is real. American Psychologist, 60, 16–26. https://doi.org/10.1037/0003-066X.60.1.16Google Scholar
Smith, J., Brooks-Gunn, J., & Klebanov, P. (1997). Consequences of living in poverty for young children’s cognitive and verbal ability and early school achievement. In Duncan, G. & Brooks-Gunn, J. (Eds.), Consequences of growing up poor (pp. 132–189). New York: Russell Sage.Google Scholar
Steele, C. M., & Aronson, J. (1995). Stereotype threat and the intellectual performance of African Americans. Journal of Personality and Social Psychology, 69, 797–811. https://doi.org/10.1037/0022-3514.69.5.797Google Scholar
Sternberg, R. J. (1997a). Successful intelligence. New York: Plume.Google Scholar
Sternberg, R. J. (1997b). The triarchic theory of intelligence. In Flanaga, D. P.n, Genshaft, J. L., & Harrison, P. L. (Eds.), Contemporary intellectual assessment: Theories, tests, and issues (pp. 92–104). New York: Guilford Press.Google Scholar
Sternberg, R. J. (2000). Implicit theories of intelligence as exemplar stories of success: Why intelligence test validity is in the eye of the beholder. Psychology, Public Policy, and Law, 6, 159–167. https://doi.org/10.1037/1076-8971.6.1.159Google Scholar
Sternberg, R. J., Grigorenko, E. L., & Kidd, K. K. (2005). Intelligence, race, and genetics. American Psychologist, 60(1), 46–59. https://doi.org/10.1037/0003-066X.60.1.46Google Scholar
Sternberg, R. J., Grigorenko, E. L., Ngorosho, D., Tantufuye, E., Mbise, A., Nokes, C., et al. (2002). Assessing intellectual potential in rural Tanzanian school children. Intelligence, 30, 141–162. https://doi.org/10.1016/S0160-2896(01)00091-5Google Scholar
Stoddard, G. D. (1943). The meaning of intelligence. New York: Macmillan.Google Scholar
Telzrow, C. F. (1990). Does PASS pass the test? A critique of the Das-Naglieri Cognitive Assessment System. Journal of Psychoeducational Assessment, 6, 344–355. https://doi.org/10.1177/073428299000800310Google Scholar
Tesich, S. (1992). A government of lies. The Nation, 254(1), 12–14.Google Scholar
Tishkoff, S. A., & Kidd, K. K. (2004). Implications of biogeography of human populations for “race” and medicine. Nature Genetics, 36(11, Suppl.), S21–S27. https://doi.org/10.1038/ng1438Google Scholar
Toga, A. W., & Thompson, P. M. (2005). Genetics of brain structure and intelligence. Annual Review of Neuroscience, 28, 1–23. https://doi.org/10.1146/annurev.neuro.28.061604.135655Google Scholar
Turkheimer, E., Haley, A., Waldron, M., D’Onofrio, B., Gottesman, I. (2003). Socioeconomic status modifies heritability of IQ in young children. Psychological Science, 14, 623–628. https://doi.org/10.1046/j.0956-7976.2003.psci_1475.xGoogle Scholar
von Stumm, S., & Plomin, R. (2015). Socioeconomic status and the growth of intelligence from infancy through adolescence. Intelligence, 48, 30–36. https://doi.org/10.1016/j.intell.2014.10.002Google Scholar
Watkins, T. J. (1997). Teacher communications, child achievement, and parent traits in parent involvement models. Journal of Educational Research, 91, 3–14. https://doi.org/10.1080/00220679709597515Google Scholar
Wechsler, D. (1958). The measurement and appraisal of adult intelligence (4th ed.). Baltimore, MD: Williams & Wilkins.Google Scholar
Wechsler, D. (2002). Wechsler Preschool and Primary Scale of Intelligence – Third Edition. San Antonio, TX: Pearson/PsychCorp.Google Scholar
Wechsler, D. (2003). Wechsler Intelligence Scale for Children – Fourth Edition. San Antonio, TX: Pearson/PsychCorp.Google Scholar
Wechsler, D. (2008). Wechsler Adult Intelligence Scale – Fourth Edition. San Antonio, TX: Pearson/PsychCorp.Google Scholar
Wenglinsky, H. (1998). Finance equalization and within-school equity: The relationship between education spending and the social distribution of achievement. Educational Evaluation and Policy Analysis, 20, 269–283. https://doi.org/10.3102/01623737020004269Google Scholar
Wicherts, J. M., Borsboom, D., & Dolan, C. V. (2010a). Evolution, brain size, and the national IQ of peoples around 3,000 years BC. Personality and Individual Differences, 48, 104–106. https://doi.org/10.1016/j.paid.2009.08.020Google Scholar
Wicherts, J. M., Borsboom, D., & Dolan, C. V. (2010b). Why national IQs do not support evolutionary theories of intelligence. Personality and Individual Differences, 48, 91–96. https://doi.org/10.1016/j.paid.2009.05.028Google Scholar
Wilson, D. K., Kirtland, K. A., Ainsworth, B. E., & Addy, C. L. (2004). Socioeconomic status and perceptions of access and safety for physical activity. Annals of Behavioral Medicine, 28, 20–28. https://doi.org/10.1207/s15324796abm2801_4Google Scholar
Wilson, L. C., & Williams, D. R. (1998). Issues in the quality of data on minority groups. In McLoyd, V. C. & Steinberg, L. (Eds.), Studying minority adolescents: Conceptual, methodological, and theoretical issues (pp. 237–250). Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
Wilson, W. J. (1987). The hidden agenda. In Wilson, W. J. (Ed.), The truly disadvantaged: The inner city, the underclass and public policy (pp. 140–164). Chicago: University of Chicago Press.Google Scholar
Wolgemuth, J. R., Koro-Ljungberg, M., Marn, T. M., Onwuegbuzie, A. J., & Dougherty, S. M. (Eds.) (2018a). Rethinking education policy and methodology in a post-truth era. Special issue, Education Policy Analysis Archives, 26(145).Google Scholar
Wolgemuth, J. R., Koro-Ljungberg, M., Marn, T. M., Onwuegbuzie, A. J., & Dougherty, S. M. (2018b). Start here, or here, no here: Introductions to rethinking education policy and methodology in a post-truth era. Education Policy Analysis Archives, 26(145), 1–8.Google Scholar
Woodcock, R. W., McGrew, K. S., & Mather, N. (2007). Woodcock-Johnson III NU Tests of Cognitive Abilities. Rolling Meadows, IL: Riverside.Google Scholar

Save book to Kindle

To save this book to your Kindle, first ensure coreplatform@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×