Research in Higher Education

, Volume 55, Issue 8, pp 810–832

Breaking it Down: Engineering Student STEM Confidence at the Intersection of Race/Ethnicity and Gender

  • Elizabeth Litzler
  • Cate C. Samuelson
  • Julie A. Lorah
Article

Abstract

It is generally accepted that engineering requires a strong aptitude for mathematics and science; therefore, students’ judgments regarding their competence in these areas as well as engineering likely influence their confidence in engineering. Little is known about how self-confidence in science, mathematics, and engineering courses (STEM confidence) varies at the intersection of race/ethnicity and gender. To fill this gap, this study examined the STEM confidence of multiple groups in undergraduate engineering programs. Results indicated that although some underrepresented groups may have lower STEM confidence overall, this finding no longer applies to all groups after controlling for personal, environmental, and behavioral factors. Specifically, African-American and Hispanic men report higher average STEM confidence than White men after controlling for these associated measures. In addition, White women continue to report lower average STEM confidence than White men after controlling for these measures, while other groups do not differ from White men. Further, many elements of student perception, including student views of professors, comparisons to peers, perceptions of the field as rewarding, and desirability of chosen major are positively associated with student STEM confidence. The changing patterns of significance for race/ethnicity and gender groups between the two models indicate that personal, environmental, and behavioral factors have different relationships with STEM confidence levels for different groups. This study contributes an understanding that gender differences in STEM confidence are not indifferent to racial and ethnic context. Social-cognitive theory provides a valuable framework for studying student academic confidence and would improve future self-confidence research.

Keywords

Engineering students Self-confidence STEM confidence Intersectionality Race/ethnicity Gender 

References

  1. Agresti, A., & Finlay, B. (1997). Statistical methods for the social sciences (3rd ed.). Upper Saddle River, NJ: Prentice Hall, Inc.Google Scholar
  2. Allison, P. D. (2002). Missing data. Sage University Papers Series on Quantitative Applications in the Social Sciences, Series No. 07-136. Thousand Oaks, CA: Sage.Google Scholar
  3. American Society for Engineering Education (ASEE). (2008). 2008 undergraduate engineering enrollment data [Data mining tool]. Retrieved from http://edms.asee.org/.
  4. Anderson, E. L., Kim, D., & American Council on Education. (2006). Increasing the success of minority students in science and technology. Washington, DC: American Council on Education.Google Scholar
  5. Aranda, M. P., Castaneda, I., Pey-Jinan, L., & Sobel, E. (2001). Stress, social support, and coping as predictors of depressive symptoms: Gender differences among Mexican Americans. Social Work Research, 25, 37–49.CrossRefGoogle Scholar
  6. Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84, 191–215.CrossRefGoogle Scholar
  7. Bandura, A. (1978). The self system in reciprocal determinism. American Psychologist, 33(4), 344.CrossRefGoogle Scholar
  8. Bandura, A. (1982). Self-efficacy mechanism in human agency. American Psychology, 37, 122–147.CrossRefGoogle Scholar
  9. Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Englewood Cliffs, NJ: Prentice Hall.Google Scholar
  10. Bandura, A. (1989). Social cognitive theory. In R. Vasta (Ed.), Annals of child development (Vol. 6, pp. 1–60). Greenwich, CT: JAI.Google Scholar
  11. Bandura, A. (1997). Self-efficacy: The exercise of control. New York: Freeman.Google Scholar
  12. Bates, D., Maechler, M. & Bolker, B. (2011). lme4: Linear mixed-effects models using S4 classes. R package version 0.999375-42. http://CRAN.R-project.org/package=lme4.
  13. Besterfield-Sacre, M., Moreno, M., Shuman, L. J., & Atman, C. J. (2001). Gender and ethnicity differences in freshmen engineering student attitudes: A cross-institutional study. Journal of Engineering Education, 90(4), 477–489.CrossRefGoogle Scholar
  14. Betz, N. (1997). What stops women and minorities from choosing and completing majors in science and engineering? In D. Johnson (Ed.), Minorities and girls in school: Effects on achievement and performance (pp. 105–140). Thousand Oaks, CA: Sage.Google Scholar
  15. Betz, N. (2001). Career self-efficacy. In F. Leong & A. Barak (Eds.), Contemporary models in vocational psychology (pp. 55–78). Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
  16. Betz, N. E., & Gwilliam, L. R. (2002). The utility of measures of self-efficacy for the Holland themes in African American and European American college students. Journal of Career Assessment, 10, 283–300.CrossRefGoogle Scholar
  17. Betz, N. E., & Hackett, G. (1981). The relationship of career-related self-efficacy expectations to perceived career options in college women and men. Journal of Counseling Psychology, 28, 399–410.CrossRefGoogle Scholar
  18. Betz, N. E., & Hackett, G. (1997). Applications of self-efficacy theory to the career assessment of women. Journal of Career Assessment, 5, 383–402.CrossRefGoogle Scholar
  19. Bliese, P. (2012). Multilevel: Multilevel functions. R package version 2.4. http://CRAN.R-project.org/package=multilevel.
  20. Bonous-Hammarth, M. (2000). Education pathways to success: Affirming opportunities for science, mathematics, and engineering majors. The Journal of Negro Education, 69(1/2), 92–111.Google Scholar
  21. Brainard, S. G., & Carlin, L. (1998). A six-year longitudinal study of undergraduate women in engineering and science. Journal of Engineering Education, 87(4), 369–375.CrossRefGoogle Scholar
  22. Brainard, S. G., Metz, S. S., & Gillmore, G. M. (1999). National WEPAN pilot climate survey: Exploring the environment for undergraduate engineering students. In 1999 WEPAN national conference proceedings.Google Scholar
  23. Brown, S. D., Lent, R. W., & Larkin, K. C. (1989). Self-efficacy as a moderator of scholastic aptitude–academic performance relationships. Journal of Vocational Behavior, 35, 64–75.CrossRefGoogle Scholar
  24. Brown, A. R., Morning, C., & Watkins, C. (2005). Influence of African American engineering student perceptions of campus climate on graduation rates. Journal of Engineering Education, 94(4), 263–271.CrossRefGoogle Scholar
  25. Burtner, J. (2004). Critical-to-quality factors associated with engineering student persistence: The influence of freshman attitudes. In ASEE/IEEE frontiers in education conference proceedings.Google Scholar
  26. Busch, T. (1995). Gender differences in self-efficacy and attitudes towards computers. Journal of Educational Computing Research, 12(2), 147–158.CrossRefGoogle Scholar
  27. Byars, A. M., & Hackett, G. (1998). Applications of social cognitive theory to the career development of women of color. Applied and Preventative Psychology, 7, 255–267.CrossRefGoogle Scholar
  28. Campbell, N. K., & Hackett, G. (1986). The effects of mathematics task performance on math self-efficacy and task interest. Journal of Vocational Behavior, 28, 149–162.CrossRefGoogle Scholar
  29. Cassidy, S., & Eachus, S. (2002). Developing the computer user self-efficacy (CUSE) scale: Investigating the relationship between computer self-efficacy, gender and experience with computers. Journal of Educational Computing Research, 26(2), 133–153.CrossRefGoogle Scholar
  30. Cech, E., Rubineau, B., Silbey, S., & Seron, C. (2011). Professional role confidence and gendered persistence in engineering. American Sociological Review, 76(5), 641–666.CrossRefGoogle Scholar
  31. Colbeck, C. L., Cabrerea, A. F., & Terenzini, P. T. (2006). Learning professional confidence: Linking teaching practices, students’ self-perceptions, and gender. The Review of Higher Education, 24(2), 173–191.CrossRefGoogle Scholar
  32. Cole, D., & Espinoza, A. (2008). Examining the academic success of Latino students in science technology engineering and mathematics (STEM) majors. Journal of College Student Development, 49(4), 285–300.CrossRefGoogle Scholar
  33. Commission on Professionals in Science and Technology. (2009). Data derived from Engineering Workforce Commission (EWC), Engineering and Technology Enrollments, 2008. Provided to University of Washington Center for Workforce Development.Google Scholar
  34. Committee on Public Understanding of Engineering Messages, National Academy of Engineering. (2008). Changing the conversation: Messages for improving public understanding of engineering. PDF downloaded from http://www.nap.edu/catalog/12187.html.
  35. Cordero, E. D., Porter, S. H., Israel, T., & Brown, M. T. (2010). Math and science pursuits: A self-efficacy intervention comparison study. Journal of Career Assessment, 18, 362.CrossRefGoogle Scholar
  36. Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16, 297–334.CrossRefGoogle Scholar
  37. Crothers, L. M., Hughes, T. L., & Morine, K. A. (2008). Theory and cases in school-based consultation. New York: Routledge.Google Scholar
  38. Davis, M., Dias-Bowie, Y., & Greenberg, K. (2004). “A fly in the buttermilk:” Descriptions of university life by successful Black undergraduate students at a predominately white southeastern university. The Journal of Higher Education, 75(4), 420–445.CrossRefGoogle Scholar
  39. French, B. F., Immekus, J. C., & Oakes, W. C. (2005). An examination of indicators of engineering students’ success and persistence. Journal of Engineering Education, 94(4), 419–425.CrossRefGoogle Scholar
  40. Gainor, K. A. (2006). Twenty-five years of self-efficacy in career assessment and practice. Journal of Career Assessment, 14, 161–178.CrossRefGoogle Scholar
  41. Gainor, K. A., & Lent, R. W. (1998). Social cognitive expectations and racial identity attitudes in predicting the math choice intentions of Black college students. Journal of Counseling Psychology, 45, 403–413.CrossRefGoogle Scholar
  42. Garrido, L. E., Abad, F. J., & Ponsoda, V. (2011). Performance of Velicer’s minimum average partial factor retention method with categorical variables. Educational and Psychological Measurement, 17(2), 551–570.CrossRefGoogle Scholar
  43. Gelman, A., & Hill, J. (2007). Data analysis using regression and multilevel/hierarchical models. New York: Cambridge University Press.Google Scholar
  44. Gibbons, M. (2008). Profiles of engineering and engineering technology colleges. Washington, DC: American Society for Engineering Education.Google Scholar
  45. Gist, M. E. (1987). Self-efficacy: Implications for organizational behavior and human resource management. Academy of Management Review, 12, 472–485.Google Scholar
  46. Gist, M. E., Schwoerer, C., & Rosen, B. (1989). Effects of alternative training methods on self-efficacy and performance in computer software training. Journal of Applied Psychology, 74, 884–891.CrossRefGoogle Scholar
  47. Gloria, A. M., & Segura-Herrera, T. A. (2004). Ambrocia and Omar go to college: A psychosociocultural examination of Chicana/os in higher education. In R. J. Velásquez, L. M. Arellano, & B. W. McNeill (Eds.), The handbook of Chicana/o psychology and mental health (pp. 401–425). Mahwah, NJ: Lawrence Erlbaum.Google Scholar
  48. Gloria, A. M., Castellanos, J., Scull, N. C., & Villegas, F. J. (2009). Psychological coping and well-being of male Latino undergraduates : Sobreviviendo la universida. Hispanic Journal of Behavioral Sciences, 31(3), 317–339.CrossRefGoogle Scholar
  49. Goodman, I. F., Cunningham, C. M., Lachapelle, C., Thompson, M., Bittinger, K., & Brennan, R. T. et al. (2002). Final report of women’s experiences in college engineering (WECE) project. Retrieved November 10, 2003, from http://www.gginc.com.
  50. Greenstein, B. (2000). Students’ perceptions of the racial climate on campus and in the classroom and the relationship with academic self-efficacy and academic and intellectual development. Dissertation Abstracts International, 61(4), 1316A (UMI No. 9967904).Google Scholar
  51. Griffin, K. (2006). Striving for success: A qualitative exploration of competing theories of high-achieving Black college students’ academic motivation. Journal of College Student Development, 47(4), 384–400.CrossRefGoogle Scholar
  52. Gwilliam, L. R., & Betz, N. E. (2001). Validity of measures of math- and science-related self-efficacy for African Americans and European Americans. Journal of Career Assessment, 9, 261–281.CrossRefGoogle Scholar
  53. Hackett, G., & Betz, N. E. (1981). A self-efficacy approach to the career development of women. Journal of Vocational Behavior, 18, 326–339.CrossRefGoogle Scholar
  54. Hackett, G., & Campbell, N. K. (1987). Task self-efficacy and task interest as a function of performance on a gender-neutral task. Journal of Vocational Behavior, 30, 203–215.CrossRefGoogle Scholar
  55. Hackett, G., Betz, N. E., Casas, J. M., & Rocha-Singh, I. (1992). Gender, ethnicity, and social cognitive factors predicting the academic achievement of students in engineering. Journal of Counseling Psychology, 39, 527–538.CrossRefGoogle Scholar
  56. Hawks, B. K., & Spade, J. Z. (1998). Women and men engineering students: Anticipation of family and work roles. Journal of Engineering Education, 87(3), 249–256.CrossRefGoogle Scholar
  57. Huang, P. M., & Brainard, S. G. (2001). Identifying determinants of academic self-confidence among science, math, engineering and technology students. Journal of Women and Minorities in Science and Engineering, 7, 317–339.CrossRefGoogle Scholar
  58. Hutchison, M. A., Follman, D. K., Sumpter, M., & Bodner, G. M. (2006). Factors influencing the self-efficacy beliefs of first-year engineering students. Journal of Engineering Education, 95(1), 39–47.CrossRefGoogle Scholar
  59. Jiang, X., & Freeman, S. (2011). An analysis of the effect of cognitive factors on students’ attritions in engineering: A literature review. In Proceedings of the American Society for Quality STEM agenda conference.Google Scholar
  60. Johnson, A. C. (2007). Unintended consequences: How science professors discourage women of color. Science Education, 91(5), 805–821.CrossRefGoogle Scholar
  61. Kaplowitz, M. D., Hadlock, T. D., & Levine, R. (2004). A comparison of web and mail survey response rates. Public Opinion Quarterly, 68, 94–101.CrossRefGoogle Scholar
  62. Kissinger, J., Campbell, R. C., Lombrozo, A., & Wilson, D. (2009). The role of gender in belonging and sense of community. In Proceedings of the 39th IEEE international frontiers in education conference.Google Scholar
  63. Kwak, N., & Radler, B. (2002). A comparison between mail and web surveys: Response pattern, respondent profile and data quality. Journal of Official Statistics, 18, 257–273.Google Scholar
  64. Laanan, F. S. (2004). Studying transfer students: Part I: Instrument design and implications. Community College Journal of Research and Practice, 28, 331–351.CrossRefGoogle Scholar
  65. Lapan, R. T., Boggs, K. R., & Morrill, W. H. (1989). Self-efficacy as a mediator of investigative and realistic General Occupational Themes on the Strong-Campbell Interest Inventory. Journal of Counseling Psychology, 36(2), 176–182.CrossRefGoogle Scholar
  66. Lent, R. W., Brown, S. D., & Larkin, K. C. (1986). Self-efficacy in the prediction of academic performance and perceived career options. Journal of Counseling Psychology, 33, 165–169.CrossRefGoogle Scholar
  67. Lent, R. W., Brown, S. D., & Hackett, R. G. (1994). Toward a unifying social cognitive theory of career and academic interest, choice, and performance [Monograph]. Journal of Vocational Behavior, 45, 79–122.CrossRefGoogle Scholar
  68. Lent, R. W., Hackett, G., & Brown, S. D. (1996). A social cognitive framework for studying career choice and transition to work. Journal of Vocational Education Research, 21(4), 3–31.Google Scholar
  69. Lindley, L. D. (2006). The paradox of self-efficacy: Research with diverse populations. Journal of Career Assessment, 14, 143–160.CrossRefGoogle Scholar
  70. Litzler, E., & Young, J. (2012). Understanding the risk of attrition in undergraduate engineering: Results from the project to assess climate in engineering. Journal of Engineering Education, 101(2), 319–345.CrossRefGoogle Scholar
  71. Marra, R. M., Schuurman, M., Moore, C., & Bogue, B. (2005). Women engineering students’ self-efficacy beliefs—The longitudinal picture. In Proceedings of the annual meeting of the American Society for Engineering Education annual conference and exposition, Portland, OR.Google Scholar
  72. Marra, R. M., Rodgers, K. A., Shen, D., & Bogue, B. (2009). Women engineering students and self-efficacy: A multi-year, multi-institution study of women engineering student self-efficacy. Journal of Engineering Education, 98(1), 27–38.CrossRefGoogle Scholar
  73. Marra, R. M., Shen, D., Rodgers, K. A., & Bogue, B. (2012). Leaving engineering: A multi-year single institution study. Journal of Engineering Education, 101(1), 6–27.CrossRefGoogle Scholar
  74. May, G. S., & Chubin, D. E. (2003). A retrospective on undergraduate engineering success for underrepresented minority students. Journal of Engineering Education, 92(1), 27–39.CrossRefGoogle Scholar
  75. Meece, J. L., & Courtney, D. P. (1992). Gender differences in students’ perceptions: Consequences for achievement-related choices. In D. H. Schunk & J. L. Meece (Eds.), Students’ perceptions in the classroom (pp. 209–228). Hillsdale, NJ: Lawrence Erlbaum.Google Scholar
  76. Metz, S. S., Brainard, S., & Gillmore, G. (1999). WEPAN pilot climate survey: Exploring the environment for undergraduate engineering students. In Proceedings of the 1999 IEEE/ISTAS conference on women and technology: Historical and professional perspective (pp. 61–71).Google Scholar
  77. Multon, K. D., Brown, S. D., & Lent, R. W. (1991). Relation of self-efficacy beliefs to academic outcomes: A meta-analytic investigation. Journal of Counseling Psychology, 38(1), 30–38.CrossRefGoogle Scholar
  78. Nauta, M. M., Epperson, D., & Kahn, J. (1998). A multiple-groups analysis of predictors of higher level career aspirations among women in mathematics, science, and engineering majors. Journal of Counseling Psychology, 45(4), 483–496.CrossRefGoogle Scholar
  79. Ngambeki, I. B., & Evangelou, D. (2011). Exploring the motivations for migration among engineering students. In 2011 ASEE conference proceedings.Google Scholar
  80. Pajares, F. (1996). Self-efficacy beliefs in academic settings. Review of Educational Research, 66(4), 543–578.CrossRefGoogle Scholar
  81. Pajares, F., & Kranzler, J. (1995). Self-efficacy beliefs and general mental ability in mathematical problem-solving. Contemporary Educational Psychology, 20(4), 426–443.CrossRefGoogle Scholar
  82. Pajares, F., & Miller, M. D. (1994). Role of self-efficacy and self-concept beliefs in mathematical problem solving: A path analysis. Journal of Educational Psychology, 86(2), 193–203.CrossRefGoogle Scholar
  83. Pascarella, E. T., Smart, J. C., Ethington, C., & Nettles, M. (1987). The influence of college on self-concept: A consideration of race and gender differences. American Educational Research Journal, 24(1), 49–77.CrossRefGoogle Scholar
  84. Ponton, M. K., Edmister, J. H., Ukeiley, L. S., & Seiner, J. M. (2001). Understanding the role of self-efficacy in engineering education. Journal of Engineering Education, 90(2), 247–251.CrossRefGoogle Scholar
  85. R Development Core Team. (2011). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna. ISBN 3-900051-07-0. http://www.R-project.org/.
  86. Raftery, A. E. (1995). Bayesian model selection in social research. Sociological Methodology, 25, 111–163.CrossRefGoogle Scholar
  87. Reichert, M., & Absher, M. (1997). Taking another look at educating African American engineers: The importance of undergraduate retention. Journal of Engineering Education, 86, 241–253.CrossRefGoogle Scholar
  88. Revelle, W., & Rocklin, T. (1979). Very simple structure: An alternative procedure for estimating the optimal number of interpretable factors. Multivariate Behavioral Research, 14, 403–414.CrossRefGoogle Scholar
  89. Rosenberg, M., & Kaplan, H. B. (1982). Social psychology of the self-concept. Arlington Heights, IL: Harlan Davidson.Google Scholar
  90. Santiago, A. M., & Einarson, M. K. (1998). Background characteristics as predictors of academic self-confidence and academic self-efficacy among graduate, science and engineering students. Research in Higher Education, 39(2), 163–198.CrossRefGoogle Scholar
  91. Schunk, D. H. (1981). Modeling and attributional effects on children’s achievement: A self-efficacy analysis. Journal of Educational Psychology, 73, 93–105.CrossRefGoogle Scholar
  92. Schunk, D. H. (1982). Self-efficacy perspectives on achievement behavior. Based on an address given at the 90th annual convention of the American Psychological Association, Washington, DC, August 23–27, 1982.Google Scholar
  93. Schunk, D. H. (1989). Self-efficacy and cognitive skill learning. In C. Ames & R. Ames (Eds.), Research on motivation in education: Goals and cognitions (Vol. 3, pp. 13–44). San Diego: Academic.Google Scholar
  94. Schunk, D. H. (1991). Self-efficacy and academic motivation. Educational Psychologist, 26, 207–231.CrossRefGoogle Scholar
  95. Seymour, E., & Hewitt, N. M. (1997). Talking about leaving: Why undergraduates leave the sciences. Boulder, CO: Westview Press.Google Scholar
  96. Shavelson, R. J., & Bolus, R. (1982). Self-concept: The interplay of theory and methods. Journal of Educational Psychology, 74, 3–17.CrossRefGoogle Scholar
  97. Silvey, J. (1975). Deciphering data: The analysis of social surveys. New York: Longman Group Limited.Google Scholar
  98. Sinkele, C. N., & Mupinga, D. M. (2011). The effectiveness of engineering workshops in attracting females into engineering fields: A review of the literature. The Clearing House, 84(1), 37–42.Google Scholar
  99. Snijders, T., & Bosker, R. (1999). Multilevel analysis: An introduction to basic and advanced multilevel modeling. London: Sage Publications.Google Scholar
  100. Tate, E. D., & Linn, M. C. (2005). How does identity shape the experiences of women of color engineering students? Journal of Science Education and Technology, 14(5/6), 483–493.CrossRefGoogle Scholar
  101. Terrel, N. (2007). STEM occupations: High-tech jobs for a high-tech economy. Occupational Outlook Quarterly Online, 51(1). Retrieved from http://www.bls.gov/opub/ooq/2007/spring/art04.htm.
  102. Vázquez, L. A., & García-Vázquez, E. (1995). Variables of success and stress with Mexican American students. College Student Journal, 29, 221–226.Google Scholar
  103. Vogt, C. (2003). An account of women’s progress in engineering: A social cognitive perspective. Journal of Women and Minorities in Science and Engineering, 9, 217–238.CrossRefGoogle Scholar
  104. Vogt, K. E. (2005). Asian American women in science, engineering, and mathematics: Background contextual and college environment influences on self-efficacy and academic achievement. Doctoral Dissertation. Retrieved from ProQuest.Google Scholar
  105. Vogt, C. M. (2008). Faculty as a critical juncture in student retention and performance in engineering programs. Journal of Engineering Education, 97(1), 27–36.CrossRefGoogle Scholar
  106. Vogt, C. M., Hocevar, D., & Hagedorn, L. S. (2007). A social cognitive construct validation: Determining women’s and men’s success in engineering programs. The Journal of Higher Education, 78, 337–364.CrossRefGoogle Scholar
  107. Zeldin, A. L., & Pajares, F. (2000). Against the odds: Self-efficacy beliefs of women in mathematical, scientific, and technological careers. American Educational Research Journal, 37(1), 215–246.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Elizabeth Litzler
    • 1
  • Cate C. Samuelson
    • 1
  • Julie A. Lorah
    • 1
  1. 1.University of WashingtonSeattleUSA

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