You are here:

Gender Differences in Conceptualizations of STEM Career Interest: Complementary Perspectives from Data Mining, Multivariate Data Analysis and Multidimensional Scaling
ARTICLE

, University of North Texas

Journal of STEM Education Volume 16, Number 4, ISSN 1557-5284 Publisher: Laboratory for Innovative Technology in Engineering Education (LITEE)

Full Text

Abstract

Data gathered from 325 middle school students in four U.S. states indicate that both male (p < .0005, RSQ = .33) and female (p < .0005, RSQ = .36) career aspirations for being a scientist are predictable based on knowledge of dispositions toward mathematics, science and engineering, plus self-reported creative tendencies. For males, strong predictors are creative tendencies (beta = .348) and dispositions toward science (beta = .326), while dispositions toward mathematics is a weaker (beta = .137) but still significant (p < .05) predictor. For females, significant (p < .05) predictors ordered by strength of contribution are dispositions toward science (beta = .360), creative tendencies (beta = .253) and dispositions toward mathematics (beta = .200). Additional analyses indicate that engineering appears to be more closely aligned with STEM career aspirations for females than for males. These findings contribute to the growing body of knowledge indicating that at the middle school level major contributors to choosing a path toward a STEM career differ for boys versus girls.

Citation

Knezek, G. (2015). Gender Differences in Conceptualizations of STEM Career Interest: Complementary Perspectives from Data Mining, Multivariate Data Analysis and Multidimensional Scaling. Journal of STEM Education, 16(4),. Laboratory for Innovative Technology in Engineering Education (LITEE). Retrieved July 2, 2022 from .

© 2015 Laboratory for Innovative Technology in Engineering Education (LITEE)

Keywords

References

View References & Citations Map
  1. Bowdich, S. (2009). Analysis of Research Exploring Culturally Responsive Curricula in Hawaii. Paper presented to the Hawaii Educational Research Association Annual Conference, February 7, 2009.
  2. Choi, N., & Chang, M. (2009). Performance of middle school students. Comparing U.S and Japanese inquiry-based science practices in middle schools. Middle Grades Research Journal, 6(1), 15.
  3. Choi, N., & Chang, M. (2011). Interplay among school climate, gender, attitude toward mathematics, and mathematics performance of middle school students. Middle Grades Research Journal, 6, 14.
  4. Christensen, R., Knezek, G., & Tyler-Wood, T. (2015). A retrospective analysis of STEM career interest among mathematics and science academy students. International Journal of Learning, Teaching and Educational Research, 10(1), 45-58.
  5. Christensen, R., Knezek, G., Tyler-Wood, T., & Gibson, D. (2013). Persistence of cognitive constructs fostered by hands-on science activities in middle school
  6. DeVellis, R.F. (1991). Scale development. Newbury Park, NJ: Sage Publications.
  7. Dubetz, T., & Wilson, J.A. (2013). Girls in engineering, mathematics and science, GEMS: A science outreach program for middle-school female students. Journal of STEM Education, 14(3), 41-47.
  8. Dunn-Rankin, P., Knezek, G.A., Wallace, S., & Zhang, S. (2004). Scaling methods (2nd ed.). Mahwah, NJ: Lawrence Erlbaum.
  9. Gafoor, K.A., & Narayan, S. (2012). Out-of-school experience categories influencing interest in science of upper primary students by gender and locale: Exploration on an Indian sample. Science Education International, 23(3), 191-204.
  10. George, R. (2006). Across-domain analysis of change in students’ attitudes toward science and attitudes about utility of science. International Journal of Science Education, 28(6), 571-589.
  11. Gorham, D. (2002). Engineering and standards for technological literacy. The Technology Teacher, 61(7), 29.
  12. Hirsch, L., Carpinelli, J., Kimmel, H., Rockland, R., & Bloom, J. (2007). The differential effects of pre-engineering curricula on middle school students’ attitudes to and knowledge of engineering careers. Presented at the 37th ASEE/IEEE Frontiers in Education Conference. Retrieved from http://fie-conference.org/fie2007/Papers/1205.pdf
  13. Knezek, G., & Christensen, R. (2014). Tools for Analyzing Quantitative Data. J.M. Spector, M.D. Merrill, J. Elen, and M.J. Bishop (Eds.) Handbook of Research on Educational Communications and Technology (4th ed.) Springer Academic.
  14. Knezek, G., Christensen, R., Tyler-Wood, T., & Periathiruvadi, S. (2013). Impact of environmental power monitoring on middle school student perceptions of STEM. Science Education International, 24(1), 98123.
  15. Knezek, G., Christensen, R., Miyashita, K., & Ropp, M. (2000). Instruments for assessing educator progress
  16. Quinn, F., & Lyons, T. (2011). High school students’ perceptions of school science and science careers: A critical look at a critical issue. Science Education International, 22(4), 225-238.
  17. Knezek, G., & Christensen, R. (1998, March). Internal consistency reliability for the teachers’ attitudes toward information technology (TAT) questionnaire. In S.
  18. Rodrigues, S., Jindal-Snape, D. & Snape, J.B. (2011). Factors that influence student pursuit of science careers; the role of gender, ethnicity, family and friends. Science Education International, 22(4), 266-273.
  19. Koza, J.R. (1992). Genetic Programming: On the Programming of Computers by Means of Natural Selection. Cambridge, MA: The MIT Press.
  20. Schmidt, M., & Lipson, H. (2009). Symbolic regression of implicit equations. Genetic programming theory and practice, 7(Chap 5), 73-85.
  21. Liu, M., Horton, L., Olmanson, J., & Toprac, P. (2011). A study of learning and motivation in a new media enriched environment for middle school science. Educational Technology Research and Development, 59(2), 249-265.
  22. Mills, L. (2013). Middle school predictors of STEM career interest: Indicators of STEM career interest among
  23. Mills, L., Wakefield, J., Najmi, A., Surface, D., Christensen, R., & Knezek, G. (2011). Validating the Computer Attitude Questionnaire NSF ITEST (CAQ N/I). In M.
  24. Stiller, T., De Miranda, M., & Whaley, D. (2007). Engineering education partnership. The International Journal of Engineering Education, 23(1), 58.
  25. Tai, R.H., Liu, C.Q., Maltese, A.V., & Fan, X. (2006). Planning early for careers in science. Science, 312, 1143-1144.
  26. Tyler-Wood, T., Knezek, G., & Christensen, R. (2010). Instruments for assessing interest in stem content and careers. Journal of Technology and Teacher Education, 18(2), 341-363.
  27. U.S. Department of Energy. (2011). When to turn off personal computers. Retrieved from http://www.energysavers.gov/your_home/
  28. Witten, F., Eibe, F., & Hall, M. (2011). Data mining: Practical machine learning tools and techniques (3rd edition). Burlington, MA: Elsevier.
  29. Zaichkowsky, J.L. (1985). Measuring the involvement construct. Journal of Consumer Research, 12(3), 341-352.
  30. Modi, K., Schoenberg, J., & Salmond, K. (2012). Generation STEM: What girls say about science, technology, engineering and math. New York: Girl Scout Research Institute.
  31. Packard, B.W.L., & Nguyen, D. (2003). Science careerrelated possible selves of adolescent girls: A longitudinal study. Journal of Career Development, 29(4), 251-263. of STEM Education Volume 16 • Issue 4

These references have been extracted automatically and may have some errors. Signed in users can suggest corrections to these mistakes.

Suggest Corrections to References

Links

Also Read

Related Collections