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6 - A Behavioral Genetic Perspective on Non-Cognitive Factors and Academic Achievement

Published online by Cambridge University Press:  06 October 2017

By
Elliot M. Tucker-Drob  and
K. Paige Harden
Edited by
Susan Bouregy ,
Elena L. Grigorenko ,
Stephen R. Latham  and
Mei Tan
Susan Bouregy
Affiliation:
Yale University, Connecticut
Elena L. Grigorenko
Affiliation:
Yale University, Connecticut
Stephen R. Latham
Affiliation:
Yale University, Connecticut
Mei Tan
Affiliation:
University of Texas, Houston
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Genetics, Ethics and Education , pp. 134 - 158
Publisher: Cambridge University Press
Print publication year: 2017

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References

Ackerman, P. L. (1996). A theory of adult intellectual development: Process, personality, interests, and knowledge. Intelligence, 22, 227–257.CrossRefGoogle Scholar
Blackwell, L. S., Trzesniewski, K. H., & Dweck, C. S. (2007). Implicit theories of intelligence predict achievement across an adolescent transition: A longitudinal study and an intervention. Child Development, 78, 246–263.Google Scholar
Bouchard, T. J. (1997). Experience producing drive theory: How genes drive experience and shape personality. Acta Paediatrica, 86, 60–64.Google Scholar
Bouchard, T. J., & Mc Gue, M. (2003). Genetic and environmental influences on human psychological differences. Journal of Neurobiology, 54(1), 4–45.Google Scholar
Bronfenbrenner, U., & Ceci, S. J. (1994). Nature-nuture reconceptualized in developmental perspective: A bioecological model. Psychological Review, 101, 568.Google Scholar
Briley, D. A., Harden, K. P., & Tucker-Drob, E. M. (2014). Child characteristics and parental educational expectations: Evidence for transmission with transaction. Developmental Psychology, 50, 2614–2632.Google Scholar
Briley, D. A., & Tucker-Drob, E. M. (2013). Explaining the increasing heritability of cognition over development: A meta-analysis of longitudinal twin and adoption studies. Psychological Science, 24, 1704–1713.Google Scholar
Briley, D. A., (2014). Genetic and environmental continuity in personality development: A meta-analysis. Psychological Bulletin, 140, 1303–1331.CrossRefGoogle ScholarPubMed
Briley, D. A., (2015). Comparing the developmental genetics of cognition and personality over the lifespan (Invited submission). Journal of Personality (Special Issue: The New Look of Behavior Genetics in Social Inequality: Gene-Environment Interplay in Life Chances).Google Scholar
Cacioppo, J. T., & Petty, R. E. (1982). The need for cognition. Journal of Personality and Social Psychology, 42, 116.Google Scholar
Cattell, R. B. (1987). Intelligence: Its structure, growth and action. New York: Elsevier.Google Scholar
Chamorro-Premuzic, T., & Furnham, A. (2003). Personality predicts academic performance: Evidence from two longitudinal university samples. Journal of Research in Personality, 37, 319–338.Google Scholar
Chamorro‐Premuzic, T., & Furnham, A. (2004). A possible model for understanding the personality‐intelligence interface. British Journal of Psychology, 95, 249–264.Google Scholar
Chamorro-Premuzic, T., Harlaar, N., Greven, C. U., & Plomin, R. (2010). More than just IQ: A longitudinal examination of self-perceived abilities as predictors of academic performance in a large sample of UK twins. Intelligence, 38, 385–392.Google Scholar
Cyders, M. A., Smith, G. T., Spillane, N. S., Fischer, S., Annus, A. M., & Peterson, C. (2007). Integration of impulsivity and positive mood to predict risky behavior: Development and validation of a measure of positive urgency. Psychological Assessment, 19, 107.Google Scholar
Cyders, M. A., & Coskunpinar, A. (2011). Measurement of constructs using self-report and behavioral lab tasks: Is there overlap in nomothetic span and construct representation for impulsivity? Clinical Psychology Review, 31, 965–982.Google Scholar
Dick, D. M., Smith, G., Olausson, P., Mitchell, S. H., Leeman, R. F., O’Malley, S. S., et al. (2010). Review: Understanding the construct of impulsivity and its relationship to alcohol use disorders. Addiction Biology, 15, 217–226.CrossRefGoogle ScholarPubMed
Dickens, W. T., & Flynn, J. R. (2001). Heritability estimates versus large environmental effects: The IQ paradox resolved. Psychological Review, 108(2), 346.Google Scholar
Duckworth, A., & Gross, J. J. (2014). Self-control and grit related but separable determinants of success. Current Directions in Psychological Science, 23, 319–325.Google Scholar
Duckworth, A. L., Peterson, C., Matthews, M. D., & Kelly, D. R. (2007). Grit: Perseverance and passion for long-term goals. Journal of Personality and Social Psychology, 92, 1087–1101.Google Scholar
Duckworth, A. L., & Seligman, M. E. (2005). Self-discipline outdoes IQ in predicting academic performance of adolescents. Psychological Science, 16, 939–944.Google Scholar
Duckworth, A. L., & Yeager, D. S. (2015). Measurement matters: Assessing personal qualities other than cognitive ability for educational purposes. Educational Researcher, 44, 237–251.Google Scholar
Dweck, C. S. (1999). Self-theories: Their role in motivation, personality, and development. Philadelphia, PA: Psychology Press.Google Scholar
Dweck, C. S., & Leggett, E. L. (1988). A social-cognitive approach to motivation and personality. Psychological Review, 95, 256.Google Scholar
Ellingson, J. M., Verges, A., Littlefield, A. K., Martin, N. G., & Slutske, W. S. (2013). Are bottom-up and top-down traits in dual-systems models of risky behavior genetically distinct? Behavior Genetics, 43, 480–490.CrossRefGoogle ScholarPubMed
Farrington, C. A., Roderick, M., Allensworth, E., Nagaoka, J., Keyes, T. S., Johnson, D. W., et al. (2012). Teaching adolescents to become learners: The role of noncognitive factors in shaping school performance – A critical literature review. Chicago, IL: Consortium on Chicago School Research.Google Scholar
Friedman, N. P., Miyake, A., Young, S. E., DeFries, J. C., Corley, R. P., & Hewitt, J. K. (2008). Individual differences in executive functions are almost entirely genetic in origin. Journal of Experimental Psychology: General, 137, 201.Google Scholar
Goff, M., & Ackerman, P. L. (1992). Personality-intelligence relations: Assessment of typical intellectual engagement. Journal of Educational Psychology, 84, 537.Google Scholar
Greven, C. U., Harlaar, N., Kovas, Y., Chamorro-Premuzic, T., & Plomin, R. (2009). More than just IQ school achievement is predicted by self-perceived abilities – But for genetic rather than environmental reasons. Psychological Science, 20(6), 753–762.Google Scholar
Heaven, P. C., Ciarrochi, J., & Vialle, W. (2007). Conscientiousness and Eysenckian psychoticism as predictors of school grades: A one-year longitudinal study. Personality and Individual Differences, 42, 535–546.Google Scholar
Hayes, K. J. (1962). Genes, drives, and intellect. Psychological Reports, 10, 299–342.Google Scholar
Heckman, J. J., & Rubinstein, Y. (2001). The importance of noncognitive skills: Lessons from the GED testing program. American Economic Review, 91(2), 145–149.Google Scholar
Hulleman, C. S., Barron, K. E., Kosovich, J. J., & Lazowski, R. A. (2014). Student motivation: Current theories, constructs, and interventions. In Lipnevich, A., Preckel, F., & Roberts, R. (Eds.), Psychosocial skills and school systems in the twenty-first century: Theory, research, and applications. New York: Springer.Google Scholar
Hulleman, C. S., Schrager, S. M., Bodmann, S. M., & Harackiewicz, J. M. (2010). A meta-analytic review of achievement goal measures: Different labels for the same constructs or different constructs with similar labels? Psychological Bulletin, 136, 422.Google Scholar
Jacobs, J. E., & Eccles, J. S. (2000). Parents, task values, and real-life achievement-related choices. In Sansone, C. & Harackiewicz, J. M. (Eds.), Intrinsic motivation (pp. 405–439). San Diego, CA: Academic Press.Google Scholar
Jentsch, J. D., Ashenhurst, J. R., Cervantes, M. C., Groman, S. M., James, A. S., & Pennington, Z. T. (2014). Dissecting impulsivity and its relationships to drug addictions. Annals of the New York Academy of Sciences, 1327, 1–26.CrossRefGoogle ScholarPubMed
John, O. P., Naumann, L. P., & Soto, C. J. (2008). Paradigm shift to the integrative big five trait taxonomy. In John, O. P., Robins, R. W., & Pervin, L. A. (Eds.), Handbook of personality: Theory and research (3rd ed., pp. 114–158). New York, NY: Guilford Press.Google Scholar
Johnson, W., Turkheimer, E., Gottesman, I. I., & Bouchard, T. J. (2009). Beyond heritability twin studies in behavioral research. Current Directions in Psychological Science, 18(4), 217–220.Google Scholar
Johnson, W. (2010). Extending and testing Tom Bouchard’s experience producing drive theory. Personality and Individual Differences, 49, 296–301.Google Scholar
Kandler, C., Bleidorn, W., Riemann, R., Angleitner, A., & Spinath, F. M. (2012). Life events as environmental states and genetic traits and the role of personality: A longitudinal twin study. Behavior Genetics, 42, 57–72.Google Scholar
Kaplan, A., & Maehr, M. L. (2007). The contributions and prospects of goal orientation theory. Educational Psychology Review, 19, 141–184.Google Scholar
Kovas, Y., Garon-Carrier, G., Boivin, M., Petrill, S. A., Plomin, R., Malykh, S. B., et al. (2015). Why children differ in motivation to learn: Insights from over 13,000 twins from 6 countries. Personality and Individual Differences, 80, 51–63.CrossRefGoogle Scholar
Luciano, M., Wainwright, M. A., Wright, M. J., & Martin, N. G. (2006). The heritability of conscientiousness facets and their relationship to IQ and academic achievement. Personality and Individual Differences, 40, 1189–1199.Google Scholar
Marsh, H. W., & Craven, R. G. (2006). Reciprocal effects of self-concept and performance from a multidimensional perspective: Beyond seductive pleasure and unidimensional perspectives. Perspectives on Psychological Science, 1(2), 133–163.Google Scholar
Marsh, H. W., Trautwein, U., Lüdtke, O., Köller, O., & Baumert, J. (2005). Academic self‐concept, interest, grades, and standardized test scores: Reciprocal effects models of causal ordering. Child Development, 76, 397–416.Google Scholar
Meece, J. L., Anderman, E. M., & Anderman, L. H. (2006). Classroom goal structure, student motivation, and academic achievement. Annual Review of Psychology, 57, 487–503.Google Scholar
Meece, J. L., Wigfield, A., & Eccles, J. S. (1990). Predictors of math anxiety and its influence on young adolescents’ course enrollment intentions and performance in mathematics. Journal of Educational Psychology, 82, 60.Google Scholar
Moffitt, T. E., Arseneault, L., Belsky, D., Dickson, N., Hancox, R. J., Harrington, H., et al. (2011). A gradient of childhood self-control predicts health, wealth, and public safety. Proceedings of the National Academy of Sciences, 201010076.Google Scholar
Murayama, K., Elliot, A. J., & Yamagata, S. (2011). Separation of performance-approach and performance-avoidance achievement goals: A broader analysis. Journal of Educational Psychology, 103, 238.Google Scholar
Mussel, P. (2010). Epistemic curiosity and related constructs: Lacking evidence of discriminant validity. Personality and Individual Differences, 49(5), 506–510.CrossRefGoogle Scholar
Nagengast, B., Marsh, H. W., Scalas, L. F., Xu, M. K., Hau, K. T., & Trautwein, U. (2011). Who took the “×” out of expectancy-value theory? A psychological mystery, a substantive-methodological synergy, and a cross-national generalization. Psychological Science, 22(8), 1058–1066.Google Scholar
Plomin, R., DeFries, J. C., & Loehlin, J. C. (1977). Genotype-environment interaction and correlation in the analysis of human behavior. Psychological Bulletin, 84(2), 309.Google Scholar
Poropat, A. E. (2009). A meta-analysis of the five-factor model of personality and academic performance. Psychological Bulletin, 135, 322–338.Google Scholar
Scarr, S., & McCartney, K. (1983). How people make their own environments: A theory of genotype greater than environment effects. Child Development, 424–435.Google Scholar
Schiefele, U., Krapp, A., & Winteler, A. (1992). Interest as a predictor of academic achievement: A meta-analysis of research. In Schiefele, U., Krapp, A., & Winteler, A. R. (Eds.), The role of interest in learning and development (pp. 183–212). Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
Spinath, B., Spinath, F. M., Harlaar, N., & Plomin, R. (2006). Predicting school achievement from general cognitive ability, self-perceived ability, and intrinsic value. Intelligence, 34, 363–374.Google Scholar
Spinath, B., Spinath, F. M., Riemann, R., & Angleitner, A. (2003). Implicit theories about personality and intelligence and their relationship to actual personality and intelligence. Personality and Individual Differences, 35, 939–951.Google Scholar
Tucker-Drob, E. M., & Briley, D. A. (2012). Socioeconomic status modifies interest-knowledge associations among adolescents. Personality and Individual Differences, 53, 9–15.Google Scholar
Tucker-Drob, E. M., (2014). Continuity of genetic and environmental influences on cognition across the life span: A meta-analysis of longitudinal twin and adoption studies. Psychological Bulletin, 140, 949–979.Google Scholar
Tucker-Drob, E. M., Briley, D. A., & Harden, K. P. (2013). Genetic and environmental influences on cognition across development and context. Current Directions in Psychological Science, 22, 349–355.Google Scholar
Tucker-Drob, E. M., Cheung, A. K., & Briley, D. A. (2014). Gross domestic product, science interest, and science achievement: A person x nation interaction. Psychological Science, 25, 2047–2057.Google Scholar
Tucker-Drob, E. M., & Harden, K. P. (2012a). Intellectual interest mediates gene-by-socioeconomic status interaction on adolescent academic achievement. Child Development, 83, 743–757.CrossRefGoogle Scholar
Tucker-Drob, E. M., (2012b). Learning motivation mediates gene-by-socioeconomic status interaction on early mathematics achievement. Learning and Individual Differences, 22, 37–45.Google Scholar
Tucker-Drob, E. M., (2012c). Early childhood cognitive development and parental cognitive stimulation: Evidence for reciprocal gene-environment transactions. Developmental Science, 15, 250–259.Google Scholar
Turkheimer, E., & Horn, E. E. (2014). Interactions between socioeconomic status and components of variation in cognitive ability. In Finkel, D. & Reynolds, C. A. (Eds.), Behavior genetics of cognition across the lifespan (pp. 41–68). New York: Springer.Google Scholar
Turkheimer, E., & Waldron, M. (2000). Nonshared environment: A theoretical, methodological, and quantitative review. Psychological Bulletin, 126, 78.Google Scholar
Von Stumm, S., Hell, B., & Chamorro-Premuzic, T. (2011). The hungry mind intellectual curiosity is the third pillar of academic performance. Perspectives on Psychological Science, 6, 574–588.Google Scholar
Wainwright, M. A., Wright, M. J., Luciano, M., Geffen, G. M., & Martin, N. G. (2008). Genetic covariation among facets of openness to experience and general cognitive ability. Twin Research and Human Genetics, 11, 275–286.Google Scholar
Whiteside, S. P., & Lynam, D. R. (2001). The five factor model and impulsivity: Using a structural model of personality to understand impulsivity. Personality and Individual Differences, 30, 669–689.Google Scholar
Wigfield, A., & Eccles, J. S. (2000). Expectancy–value theory of achievement motivation. Contemporary educational psychology, 25, 68–81.Google Scholar
Woo, S. E., Harms, P. D., & Kuncel, N. R. (2007). Integrating personality and intelligence: Typical intellectual engagement and need for cognition. Personality and Individual Differences, 43, 1635–1639.Google Scholar
Zhang, Y., Haddad, E., Torres, B., & Chen, C. (2011). The reciprocal relationships among parents’ expectations, adolescents’ expectations, and adolescents’ achievement: A two-wave longitudinal analysis of the NELS data. Journal of Youth and Adolescence, 40, 479–489.Google Scholar

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