Publication Cover
Educational Psychology
An International Journal of Experimental Educational Psychology
Volume 37, 2017 - Issue 4
2,271
Views
37
CrossRef citations to date
0
Altmetric
Articles

Trajectories of self-perceived math ability, utility value and interest across middle school as predictors of high school math performance

&
Pages 438-456 | Received 16 Jan 2015, Accepted 23 Jul 2015, Published online: 20 Aug 2015

References

  • Akaike, H. (1985). Information theory and an extension of the maximum likelihood principle. In B. N. Petrov & F. Csaki (Eds.), Second international symposium on information theory (pp. 599–624). Budapest: Akademiai Kiado.
  • Assor, A., & Connell, J. P. (1992). The validity of students’ self-reports as measures of performance affecting self-appraisals. In D. H. Schunk & J. L. Meece (Eds.), Student self-perceptions in the classroom (pp. 25–47). Hillsdale, NJ: Lawrence Erlbaum Associates.
  • Beilock, S. L., Gunderson, E. A., Ramirez, G., & Levine, S. C. (2010). Female teachers’ math anxiety affects girls math achievement. Proceedings of the National Academy of Sciences, 107, 1860–1863.10.1073/pnas.0910967107
  • 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, 246–263.10.1111/cdev.2007.78.issue-1
  • Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit. In K. Bollen & J. S. Long (Eds.), Testing structural equation models (pp. 136–162). Newbury Park, CA: Sage.
  • Carnevale, A. P., Smith, N., Stone, J., Kotamraju, P., Steuernagel, B., & Green, K. A. (2011). Career clusters: Forecasting demand for high school through college jobs, 2008–2018 (State data). Georgetown University Center on Education and the Workforce.
  • Crombie, G., Sinclair, N., Silverthorn, N., Byne, B. M., DuBois, D. L., & Trineer, A. (2005). Predictors of young adolescents' math grades and course enrollment intentions: Gender similarities and differences. Sex Roles, 52, 351–367.
  • Duncan, T. E., Duncan, S. C., & Strycker, L. A. (2006). An introduction to latent variable growth curve modeling (2nd ed.). Mahwah, NJ: Lawrence Erlbaum Associates.
  • Dweck, C., & Elliot, E. S. (1983). Achievement motivation. In P. H. Mussen (Ed.), Handbook of child psychology (3rd ed., Vol. IV, pp. 643–691). New York, NY: Wiley.
  • Dweck, C. S., & Leggett, E. L. (1988). A social-cognitive approach to motivation and personality. Psychological Review, 95, 256–273.10.1037/0033-295X.95.2.256
  • Eccles (Parsons), J., Adler, T. F., Futterman, R., Goff, S. B., Kaczala, C. M., Meece, J. L., & Midgley, C. (1983). Expectancies, values, and academic behaviors. In T. J. T. Spence (Ed.), Achievement and achievement motivation (pp. 75–146). San Francisco, CA: W. H. Freeman.
  • Eccles, J. S., Wigfield, A., & Sciefele, U. (1998). Motivation to succeed. In N. Eisenberg & W. Damon (Eds.), Handbook of child psychology (5th ed., Vol. 3, pp. 1017–1095). New York, NY: Wiley.
  • Enders, C. K., & Bandalos, D. L. (2001). The relative performance of full information maximum likelihood estimation for missing data in structural equation models. Structural Equation Modeling: A Multidisciplinary Journal, 8, 430–457.10.1207/S15328007SEM0803_5
  • Fredricks, J. A., & Eccles, J. S. (2002). Children’s competence and value beliefs from childhood through adolescence: Growth trajectories in two male-sex-typed domains. Developmental Psychology, 38, 519–533.10.1037/0012-1649.38.4.519
  • Freeman, P., & Aspray, W. (1999). The supply of information technology workers in the United States. Computing Research Foundation. Retrieved from http://files.eric.ed.gov/fulltext/ED459346.pdf
  • Harackiewicz, J., Rozek, C., Hulleman, C., & Hyde, J. (2012). Helping parents to motivate adolescents in mathematics and science: An experimental test of a utility-value intervention. Psychological Science, 23, 899–906.10.1177/0956797611435530
  • Harackiewicz, J. M., Tibbetts, Y., Canning, E., & Hyde, J. S. (2014). Harnessing values to promote motivation in education. In S. A. Karabenick & T. C. Urdan (Eds.), Motivational interventions (in the series advances in motivation and achievement) (Vol. 18, pp. 71–105). Bingley: Emerald Group.
  • Hill, J. P., & Lynch, M. E. (1983). The intensification of gender-related role expectations during early adolescence. In J. Brooks-Gunn & A. Petersen (Eds.), Girls at puberty (pp. 201–228). New York, NY: Pelum.10.1007/978-1-4899-0354-9
  • Hoyle, R. H. (1995). Structural equation modeling. Thousand Oaks, CA: Sage.
  • Hyde, J. S. (2005). The gender similarities hypothesis. American Psychologist, 60, 581–592.10.1037/0003-066X.60.6.581
  • Hyde, J. S. (2014). Gender similarities and differences. Annual Review of Psychology, 65, 373–398.10.1146/annurev-psych-010213-115057
  • Hyde, J. S., Klein, M. H., Essex, M. J., & Clark, R. (1995). Maternity leave and women’s mental health. Psychology of Women Quarterly, 19, 257–285.10.1111/pwqu.1995.19.issue-2
  • Jacobs, J. E., Lanza, S., Osgood, D. W., Eccles, J. S., & Wigfield, A. (2002). Changes in children’s self-competence and values: Gender and domain differences across grades one through twelve. Child Development, 73, 509–527.10.1111/cdev.2002.73.issue-2
  • Kaplan, D. (2010). Structural equation modeling: Foundations and extensions. Thousand Oaks, CA: Sage.
  • Kim, R. Y., Ham, S. H., & Paine, L. W. (2011). Knowledge expectations in mathematics teacher preparation programs in South Korea and the United States: Towards international dialogue. Journal of Teacher Education, 62, 48–61.10.1177/0022487110381999
  • Kind, P. M. (2013). Conceptualizing the science curriculum: 40 years of developing assessment frameworks in three large-scale assessments. Science Education, 97, 671–694.10.1002/sce.2013.97.issue-5
  • Koretz, D. (2009). How do American students measure up? Making sense of international comparisons. The Future of Children, 19, 37–51.10.1353/foc.0.0023
  • Lindberg, S. M., Hyde, J. S., & Hirsch, L. M. (2008). Gender and mother–child interactions during mathematics homework: The importance of individual differences. Merrill-Palmer Quarterly, 54, 232–255.10.1353/mpq.2008.0017
  • Lindberg, S. M., Hyde, J. S., Petersen, J., & Linn, M. C. (2010). New trends in gender and mathematics performance: A meta-analysis. Psychological Bulletin, 136, 1123–1135.10.1037/a0021276
  • Mangan, K. (2013). Community colleges respond to demand for STEM graduate. Chronicle of Higher Education, 59, 12–13.
  • Marsh, H. W., Hau, K.-T., & Grayson, D. (2005). Goodness of fit evaluation in structural equation modeling. In A. Maydeu-Olivares & J. McCardle (Eds.), Contemporary psychometrics: A festschrift to Roderick P. McDonald (pp. 275–340). Mahwah, NJ: Lawrence Erlbaum Associates.
  • 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, 60–70.10.1037/0022-0663.82.1.60
  • Meredith, W. (1993). Measurement invariance, factor analysis and factorial invariance. Psychometrika, 58, 525–543.10.1007/BF02294825
  • Moss-Racusin, C. A., Dovidio, J. F., Brescoll, V. L., Graham, M. J., & Handelsman, J. (2012). Science faculty’s subtle gender biases favor male students. Proceedings of the National Academy of Sciences, 109, 16474–16479.10.1073/pnas.1211286109
  • Muthén, L. K., & Muthén, B. O. (2007). Mplus user’s guide (5th ed.). Los Angeles, CA: Author.
  • Nagy, G., Watt, H. M. G., Eccles, J. S., Trautwein, U., Ludtke, O., & Baumert, J. (2010). The development of students’ mathematics self-concept in relation to gender: Different countries, different trajectories? Journal of Research on Adolescence, 20, 482–506.10.1111/jora.2010.20.issue-2
  • National Science Foundation. (2012). Science and engineering indicators 2012. Retrieved from www.nsf.gov/statistics/seind12/
  • Petersen, J. L., & Hyde, J. S. (2014). Gender-related academic and occupational goals. In L. Liben & R. Bigler (Eds.), Advances in child development and behavior (pp. 43–76). Cambridge, MA: Elsevier.
  • Priess, H., Lindberg, S. M., & Hyde, J. S. (2009). Adolescent gender-role identity and mental health: Gender intensification revisited. Child Development, 80, 1531–1544.10.1111/cdev.2009.80.issue-5
  • Sells, L. W. (1980). Mathematics: The invisible filter. Engineering Education, 70, 340–341.
  • Steele, C. M., & Aronson, J. (1995). Stereotype threat and the intellectual test performance of African Americans. Journal of Personality and Social Psychology, 69, 797–811.10.1037/0022-3514.69.5.797
  • Su, R., Rounds, J., & Armstrong, P. (2009). Men and things, women and people: A meta-analysis of sex differences in interests. Psychological Bulletin, 135, 859–884.10.1037/a0017364
  • Tenenbaum, H. R., & Leaper, C. (2003). Parent–child conversations about science: The socialization of gender inequities? Developmental Psychology, 39, 34–47.10.1037/0012-1649.39.1.34
  • Updegraff, K. A., Eccles, J. S., Barber, B. L., & O’brien, K. M. (1996). Course enrollment as self-regulatory behavior: Who takes optional high school math courses? Learning and Individual Differences, 8, 239–259.10.1016/S1041-6080(96)90016-3
  • U.S. Census Bureau. (2012). State and county quick facts. WI. Retrieved March 25, 2014, from http://quickfacts.census.gov/qfd/states/55000.html
  • Watt, H., & Eccles, J. (2008). Gender and occupational outcomes. Washington, DC: American Psychological Association.
  • Watt, H. M. G. (2004). Development of adolescents’ self-perceptions, values, and task perceptions according to gender and domain in 7th- through 11th-grade Australian students. Child Development, 75, 1556–1574.10.1111/cdev.2004.75.issue-5
  • Watt, H. M. G., Shapka, J. D., Morris, Z. A., Durik, A. M., Keating, D. P., & Eccles, J. S. (2012). Gendered motivational processes affecting high school mathematics participation, educational aspirations, and career plans: A comparison of samples from Australia, Canada, and the United States. Developmental Psychology, 48, 1594–1611.10.1037/a0027838
  • Wigfield, A., & Eccles, J. S. (1992). The development of achievement task value: A theoretical analysis. Developmental Psychology, 12, 265–310.
  • Wigfield, A., (1994). Expectancy-value theory of achievement motivation: A developmental perspective. Educational Psychology Review, 6, 49–78.10.1007/BF02209024
  • Wigfield, A., & Eccles, J. S. (2000). Expectancy-value theory of achievement motivation. Contemporary Educational Psychology, 25, 68–81.10.1006/ceps.1999.1015
  • Wigfield, A., & Eccles, J. S. (2002). Development of achievement motivation. San Diego, CA: Academic Press.
  • Willet, J., & Sayer, A. S. (1994). Using covariance structure analysis to detect correlates and predictors of individual change over time. Psychological Bulletin, 116, 363–381.10.1037/0033-2909.116.2.363

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.