Abstract
There is growing evidence that science capital (science-related forms of social and cultural capital) and family habitus (dispositions for science) influence STEM career decisions by youth. This study presents reliability and validity evidence for a survey of factors that influence career aspirations in science. Psychometric properties of the NextGen Scientist Survey were evaluated with 889 youth in grades 6–8. An exploratory factor analysis (EFA) found four factors (Science Expectancy Value, Science Experiences, Future Science Task Value, and Family Science Achievement Values). Using confirmatory factor analysis (CFA), four separate factor models were tested. The CFA affirmed that the four-factor solution extracted during the EFA was the best-fitting model. The analyses also found acceptable internal consistency for each of the four factors. The results validate the use of the NextGen Scientist Survey for measuring science capital for middle school youth.
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21 October 2021
A Correction to this paper has been published: https://doi.org/10.1007/s11165-021-10016-8
References
Archer, L., & DeWitt, J. (2013). ASPIRES: young people’s science and career aspirations, age 10–14. Final project report. London: Department of Education and Professional Studies, King’s College London.
Archer, L., & DeWitt, J. (2016). Understanding young people’s science aspirations: how students form ideas about ‘becoming a scientist.’. London: Routledge.
Archer, L., DeWitt, J., Osborne, J., Dillon, J., Willis, B., & Wong, B. (2012). Science aspirations, capital, and family habitus: how families shape children’s engagement and identification with science. American Educational Research Journal, 49(5), 881–908.
Archer, L., Dawson, E., DeWitt, J., Seakins, A., & Wong, B. (2015). “Science capital”: a conceptual, methodological, and empirical argument for extending bourdieusian notions of capital beyond the arts. Journal of Research in Science Teaching, 52(7), 922–948.
Aschbacher, P. R., Ing, M., & Tsai, S. M. (2014). Is science me? Exploring middle school students’ STE-M career aspirations. Journal of Science Education and Technology, 23(6), 735–743.
Aydeniz, M., & Kotowski, M. R. (2014). Conceptual and methodological issues in the measurement of attitudes towards science. Electronic Journal of Science Education, 18(3).
Bandura, A. (1978). Self-efficacy: toward a unifying theory of behavioral change. Advances in Behaviour Research and Therapy, 1(4).
Bandura, A. (1982). Self-efficacy mechanism in human agency. American Psychologist, 37(2), 122–147.
Bentler, P. M., & Bonett, D. G. (1980). Significance tests and goodness of fit in the analysis of covariance structures. Psychological Bulletin, 88(3), 588.
Bourdieu, P., & Passeron, J. C. (1979). The inheritors: French students and their relation to culture. Chicago: University of Chicago Press.
Bourdieu, P., & Richardson, J. G. (1986). Handbook of theory and research for the sociology of education. The Forms of Capital, 241–258.
Britner, S. L., & Pajares, F. (2001). Self-efficacy beliefs, motivation, race, and gender in middle school science. Journal of Women and Minorities in Science and Engineering, 7(4), 269–283.
Brown, T. A. (2015). Confirmatory factor analysis for applied research. New York, NY: Guilford Press.
Cedefop (2018). Insights into skill shortages and skill mismatch: learning from Cedefop’s European skills and jobs survey. Luxembourg: Publications Office. Cedefop reference series; No 106. Retrieved from: http://www.cedefop.europa.eu/en/publications-and-resources/publications/3075.
Chestnutt, K., Jones, K. G., Hite, R., Cayton, E., Ennes, M., Corin, E. (2018). Next generation crosscutting themes: Factors that contribute to students, understanding of size and scale. Journal of Research in Science Teaching, 55(6), 876–900.
Claussen, S., & Osborne, J. (2013). Bourdieu’s notion of cultural capital and its implications for the science curriculum. Science Education, 97(1), 58–79.
Cooper, K. M., Krieg, A., & Brownell, S. E. (2018). Who perceives they are smarter? Exploring the influence of student characteristics on student academic self-concept in physiology. Advances in Physiology Education, 42(2), 200–208.
DeWitt, J., Archer, L., Osborne, J., Dillon, J., Willis, B., & Wong, B. (2011). High aspirations but low progression: the science aspirations–careers paradox amongst minority ethnic students. International Journal of Science and Mathematics Education, 9(2), 243–271.
Ford, J. K., MacCallum, R. C., & Tait, M. (1986). The application of exploratory factor analysis in applied psychology: a critical review and analysis. Personnel Psychology, 39(2), 291–314.
Fraser, B. J. (1978). Development of a test of science-related attitudes. Science Education, 62(4), 509–515.
Fraser, B. J. (1981). Tosra: test of science-related attitudes: handbook. Australian Council for Educational Research.
Fuligni, A. J. (1997). The academic achievement of adolescents from immigrant families: The role of family background, attitudes, and behavior. Child development, 68(2),351–363.
Gorsuch, R. L. (2015). Factor analysis: classic edition. New York, NY: Routledge.
Hayton, J. C., Allen, D. G., & Scarpello, V. (2004). Factor retention decisions in exploratory factor analysis: a tutorial on parallel analysis. Organizational Research Methods, 7(2), 191–205.
Hazari, Z., Sonnert, G., Sadler, P. M., & Shanahan, M. C. (2010). Connecting high school physics experiences, outcome expectations, physics identity, and physics career choice: a gender study. Journal of Research in Science Teaching, 47(8), 978–1003.
Hill, C., Corbett, C., & St Rose, A. (2010). Why so few? Women in science, technology, engineering, and mathematics. American Association of University Women (Vol. 5). Retrieved from http://eric.ed.gov/ERICWebPortal/recordDetail?accno=ED509653.
Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. https://doi.org/10.1080/10705519909540118.
Jones, M. G., Lee, T., Chesnutt, K., Carrier, S., Ennes, M., Caytone, E., Madden, L. & Huff, P. (2019). Enclothed cognition: Putting lab coats ot the test. International Journal of Science Education, 42(14), 1962–1976.
Kenny (2015). Measuring model fit.Retrieved fromhttp://www.davidakenny.net/cm/fit.htm.
Kier, M. W., Blanchard, M. R., Osborne, J. W., & Albert, J. L. (2014). The development of the STEM career interest survey (STEM-CIS). Research in Science Education, 44(3), 461–481.
Lent, R. W., Brown, S. D., & Hackett, G. (1994). Toward a unifying social cognitive theory of career and academic interest, choice, and performance. Journal of Vocational Behavior, 45(1), 79–122.
Lent, R. W., Brown, S. D., Schmidt, J., Brenner, B., Lyons, H., & Treistman, D. (2003). Relation of contextual supports and barriers to choice behavior in engineering majors: test of alternative social cognitive models. Journal of Counseling Psychology, 50(4), 458–465.
Marsh, H. W., Martin, A. J., Yeung, A. S., & Craven, R. G. (2016). Competence self-perceptions: a cornerstone of achievement motivation and the positive psychology movement. In A. Elliot & C. S. Dweck (Eds.), Handbook of competence and motivation (2nd ed.). New York: Guilford Publications.
Meade, A. W., Johnson, E. C., & Braddy, P. W. (2008). Power and sensitivity of alternative fit indices in tests of measurement invariance. Journal of Applied Psychology, 93, 568–592.
National Science Foundation, National Center for Science and Engineering Statistics. (2017). Women, Minorities, and Persons with Disabilities in Science and Engineering: 2017. Special Report NSF 17-310. Arlington, VA. Retrieved from: www.nsf.gov/statistics/wmpd/.
Orlikowski, C. & Lozinak, A. (n.d.). Skilled talent: it’s at your fingertips. As organizations report the highest talent shortage since 2007, employers look to develop their own workforces to fill in-demand roles. Retrieved from: https://www.manpowergroup.com/wps/wcm/connect/8ccb11cb-1ad4-4634-84ea-1656ee74b3ed/GlobalTalentShortageSurvey-PressRelease.pdf?MOD=AJPERES&ContentCache=NONE&.
Publications Office. (2016). EU cooperation in education and training (ET 2020). Retrieved from: http://eur-lex.europa.eu/legal-content/EN/TXT/?uri=LEGISSUM:ef0016.
Revelle, W., & Zinbarg, R. E. (2009). Coefficients alpha, beta, omega, and the glb: comments on Sijtsma. Psychometrika, 74, 145–154.
Safavian, N., & Conley, A. (2016). Expectancy-value beliefs of early-adolescent Hispanic and non-Hispanic youth: predictors of mathematics achievement and enrollment. AERA Open, 2(4), 2332858416673357.
Schreiner, C., & Sjøberg, S. (2004). Sowing the seeds of ROSE: background, rationale, questionnaire development and data collection for ROSE (The Relevance of Science Education): a comparative study of students’ views of science and science education. Acta Didactica, 4, 1–120.
Shavelson, R. J., Hubner, J. J., & Stanton, G. C. (1976). Self-concept: validation of construct interpretations. Review of Educational Research, 46(3), 407–441.
Sjøberg, S., & Schreiner, C. (2010). The ROSE project: an overview and key findings. Oslo: University of Oslo, 1–31.
STELAR. (n.d.) Instruments: Test of science related attitudes (TOSRA). Retrieved from: http://stelar.edc.org/instruments/test-science-related-attitudes-tosra.
Tretter, T. R., Jones, M. G., Andre, T., Negishi, A., & Minogue, J. (2006). Conceptual boundaries and distances: Student, and adults, concepts of the scale of scientific phenomena. Journal of Research in Science Teaching, 83, 282–319.
Unfried, A., Faber, M., Stanhope, D. S., & Wiebe, E. (2015). The development and validation of a measure of student attitudes toward science, technology, engineering, and math (S-STEM). Journal of Psychoeducational Assessment, 33(7), 622–639.
Usher, E. L. (2009). Sources of middle school students’ self-efficacy in mathematics: a qualitative investigation. American Educational Research Journal, 46(1), 275–314.
Vandenberg, R. J., & Lance, C. E. (2000). A review and synthesis of the measurement invariance literature: suggestions, practices, and recommendations for organizational research. Organizational Research Methods, 3(1), 4–70.
Vilorio, D. (2014). STEM 101: intro to tomorrow’s jobs. Occupational Outlook Quarterly, 58(1), 2–12. Washington, DC: Bureau of Labor Statistics.
Waller, N. G. (2018). Direct Schmid–Leiman transformations and rank-deficient loadings matrices. Psychometrika, 83(4), 858–870.
Wigfield, A., & Eccles, J. S. (2000). Expectancy–value theory of achievement motivation. Contemporary Educational Psychology, 25(1), 68–81.
Wigfield, A., & Eccles, J. S. (2002). The development of competence beliefs, expectancies for success, and achievement values from childhood through adolescence. In Development of achievement motivation (pp. 91-120). Academic press.
Wigfield, A., Rosenzweig, E., & Eccles, J. (2017). Achievement values: Interactions, interventions, and future directions. In A. J. Elliot, C. S. Dweck, & D. S. Yeager (Eds.), Handbook of competence and motivation: Theory and application (pp. 116–134). New York, NY: Guilford Publications.
Yosso, T. J. (2005). Whose culture has capital? A critical race theory discussion of community cultural wealth. Race Ethnicity and Education, 8(1), 69–91.
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The original online version of this article was revised: Due to an oversight by the Publisher during the typesetting stage, “Author” citations were still present in the originally published paper. The paper has now been updated with the correct citations in the text and inclusion of the missing complete bibliographic entries. Full information regarding the corrections made can be found in the erratum/correction for this article.
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Jones, M.G., Ennes, M., Weedfall, D. et al. The Development and Validation of a Measure of Science Capital, Habitus, and Future Science Interests. Res Sci Educ 51, 1549–1565 (2021). https://doi.org/10.1007/s11165-020-09916-y
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DOI: https://doi.org/10.1007/s11165-020-09916-y