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11 - Does priority matter?

Gendered patterns of subjective task values across school subject domains

Published online by Cambridge University Press:  05 October 2014

By
Angela Chow  and
Katariina Salmela-Aro
Edited by
Ingrid Schoon  and
Jacquelynne S. Eccles
Angela Chow
Affiliation:
University of Alberta
Katariina Salmela-Aro
Affiliation:
University of Jyväskylä
Ingrid Schoon
Affiliation:
Institute of Education, University of London
Jacquelynne S. Eccles
Affiliation:
University of Michigan, Ann Arbor
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Summary

Abstract

This chapter examines patterns in subjective task values across different school subjects from a person-centered perspective. According to the Eccles expectancy-value model of behavioral choice, individuals’ perceived values on school subjects or activities, the so-called subjective task values (STVs or simply task values), are important motivational sources that play an influential role in shaping behaviors and choices over and above ability concepts and actual capabilities (Eccles (Parsons), 1983; Eccles, Wigfield, & Schiefele, 1998). Going beyond previous studies that focused on associations between STVs and outcomes within one particular school subject area, we compare how boys and girls in a sample of Finnish high school students (N = 398) prioritize values across three subject domains, including (1) math and science, (2) Finnish, and (3) social sciences. Moreover, the relationships between these priority patterns and their educational aspirations to hard sciences are examined. It is argued that focusing on gender differences in internal hierarchies (i.e., the ordering of preferences) can help to improve our understanding of gender differences in educational aspirations and subsequent career paths.

Type
Chapter
Information
Gender Differences in Aspirations and Attainment
A Life Course Perspective
 , pp. 247 - 265
Publisher: Cambridge University Press
Print publication year: 2014

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References

Aldridge, A., & Roesch, S. (2008). Developing coping typologies of minority adolescents: A latent profile analysis. Journal of Adolescence, 31, 499–517.CrossRefGoogle ScholarPubMed
Atwater, M. M., Wiggins, J., & Gardner, C. M. (1995). A study of urban middle school students with high and low attitudes toward science. Journal of Research in Science Teaching, 32, 665–677.CrossRefGoogle Scholar
Baron, R. M., & Kenny, D. A. (1986). The moderator–mediator distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51, 1173–1182.CrossRefGoogle ScholarPubMed
Bergman, L. R. (2001). A person approach in research on adolescence: Some methodological challenges. Journal of Adolescent Research, 16, 28–53.CrossRefGoogle Scholar
Bergman, L. R., & El-Khouri, B. M. (2003). A person-oriented approach: Methods for today and methods for tomorrow. New Directions for Child and Adolescent Development, 101, 25–38.CrossRefGoogle Scholar
Bowen, N. K., Lee, J.-S., & Weller, B. (2007). Childreport social environmental risk and protection: A typology with implications for practice in elementary schools. Children & Schools, 29, 229–242.CrossRefGoogle ScholarPubMed
Chow, A., Eccles, J., & Salmela-Aro, K. (2012). Task value profiles across subjects and aspirations to physical and IT-related sciences in the United States and Finland. Developmental Psychology, 48(6), 1612–1628.CrossRefGoogle Scholar
Chow, A., & Salmela-Aro, K. (2011). Task values across subject domains: A gender comparison using a person-centred approach. International Journal of Behavioral Development, 35(3), 202–209.CrossRefGoogle Scholar
Crombie, G., Sinclair, N., Silverthorn, N., Byrne, B., DuBois, D., & Trinneer, A. (2005). Predictors of young adolescents’ math grades and course enrollment intentions: Gender similarities and differences. Sex Roles, 52, 351–367.CrossRefGoogle Scholar
Durik, A. M., Vida, M., & Eccles, J. S. (2006). Task values and ability beliefs as predictors of high school literacy choices: A developmental analysis. Journal of Educational psychology, 98(2), 382–393.CrossRefGoogle Scholar
Eccles, J. S. (2011). Gendered educational and occupational choices: Applying the Eccles et al. model of achievement-related choices. International Journal of Behavioural Development, 35(3), 195–201.CrossRefGoogle Scholar
Eccles, J. S., Wigfield, A., & Schiefele, U. (1998). Motivation to succeed. In Damon, W. & Eisenberg, N. (Eds.), Handbook of child psychology: Social, emotional, and personality development (5th ed., No. 3, pp. 1017–1095). New York: Wiley.Google Scholar
Eccles (Parsons), J. S. (1983). Expectancies, values, and academic behaviours. In Spence, J. T. (Ed.), Achievement and achievement motives: Psychological and sociological approaches (pp. 75–146). San Francisco, CA: W. H. Freeman.Google Scholar
Herman, K. C., Ostrander, R., Silva, S., March, J., & Walkup, J. (2007). Empirically derived subtypes of adolescent depression: Latent profile analysis of co-occurring symptoms in the Treatment for Adolescents with Depression Study (TADS). Journal of Consulting and Clinical Psychology, 75, 716–728.CrossRefGoogle Scholar
Herzig, A. H. (2004). Becoming mathematicians: Women and students of color choosing and leaving doctoral mathematics. Review of Educational Research, 74, 171–214.CrossRefGoogle Scholar
Jacobs, J. E. (2005). Twenty-five years of research on gender and ethnic differences on math and science career choices: What have we learned?New Directions for Child and Adolescent Development, 110, 85–94.CrossRefGoogle Scholar
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.CrossRefGoogle ScholarPubMed
Lo, Y., Mendell, N., & Rubin, D. (2001). Testing the number of components in a normal mixture. Biometrika, 88, 767–778.CrossRefGoogle Scholar
Lubke, G. H., & Muthén, B. (2007). Performance of factor mixture models as a function of model size, criterion measure effects, and class-specific parameters. Structural Equation Modeling, 14, 26–47.CrossRefGoogle Scholar
Marsh, H. W., Lüdtke, O., Trautwein, U., & Morin, A. J. S. (2009). Classical latent profile analysis of academic self-concept dimensions: Synergy of person- and variable-centered approaches to theoretical models of self-concept. Structural Equation Modeling, 16, 191–225.CrossRefGoogle 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, 60–70.CrossRefGoogle Scholar
Ministry of Education and Culture. (2004). Classification of educational field. Retrieved from .
Muthén, B. (2002). Beyond SEM: General latent variable modeling. Behaviormetrika, 29, 81–117.CrossRefGoogle Scholar
Muthén, L., & Muthén, B. (2010). Mplus (version 6.1). Los Angeles, CA: Muthén & Muthén.Google Scholar
Niemivirta, M. (2002). Individual differences and developmental trends in motivation: Integrating person-centered and variable-centered methods. In Pintrich, P. R. & Maehr, M. L. (Eds.), Advances in motivations and achievement: Vol. 12. New directions in measures and methods (pp. 241–275). Amsterdam: JAI Press.Google Scholar
Nylund, K., Asparouhov, T., & Muthén, B. (2007). Deciding on the number of classes in latent class analysis and growth mixture modeling. A Monte Carlo simulation study. Structural Equation Modeling, 14, 535–569.CrossRefGoogle Scholar
Nylund, K., Bellmore, A., Nishina, A., & Graham, S. (2007). Subtypes, severity, and structural stability of peer victimization: What does latent class analysis say?Child Development, 78(6), 1706–1722.CrossRefGoogle ScholarPubMed
Schwartz, B. (2004). The paradox of choice: Why more is less. New York: HarperCollins.Google Scholar
Simpkins, S. D., Davis-Kean, P. E., & Eccles, J. S. (2006). Math and science motivation: A longitudinal examination of the links between choices and beliefs. Developmental Psychology, 42(1), 70–83.CrossRefGoogle ScholarPubMed
Smith, G. W., & Shevlin, M. (2008). Patterns of alcohol consumption and related behaviour in Great Britain: A latent class analysis of the Alcohol Use Disorder Identification Test (AUDIT). Alcohol & Alcoholism, 43(5), 590–594.CrossRefGoogle Scholar
Tuominen-Soini, H., Salmela-Aro, K., & Niemivirta, M. (2008). Achievement goal orientations and subjective well-being: A person-centered analysis. Learning and Instruction, 18, 251–266.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
Vermunt, J. K., & Magidson, J. (2002). Latent class cluster analysis. In Hagenaars, J. A. & McCutcheon, A. L. (Eds.), Applied latent class analysis (pp. 89–106). Cambridge University Press.CrossRefGoogle Scholar
Viljaranta, J., Nurmi, J.-E., Aunola, K., & Salmela-Aro, K. (2009). The role of task values in adolescents’ educational tracks: A person-oriented approach. Journal of Research on Adolescence, 19(4), 786–798.CrossRefGoogle Scholar
Wigfield, A., & Guthrie, J. T. (1997). Relations of children’s motivation for reading to the amount and breadth of their reading. Journal of Educational Psychology, 89, 420–432.CrossRefGoogle Scholar
Wohlwill, J. F. (1973). The study of behavioral development. Orlando, FL: Academic Press.Google Scholar
Xie, Y., & Shauman, K. (2003). Women in science. Cambridge, MA: Harvard University Press.Google Scholar
Yli-Piipari, S., Kiuru, N., Jaakkola, T., Liukkonen, J., & Watt, A. (2011). The role of peer groups in male and female adolescents’ task values and physical activity. Psychological Reports, 108(1), 75–93.CrossRefGoogle ScholarPubMed
Zarrett, N. R., & Malanchuk, O. (2005). Who’s computing? Gender and race differences in young adults’ decisions to pursue an information technology career. New Directions for Child & Adolescent Development, 110, 65–84.CrossRefGoogle Scholar

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