June 24, 2017
June 24, 2017
June 28, 2017
Educational Research and Methods
This research study details the development of two new scales to measure how students develop an inclusive engineering identity. Inclusive engineering identity is displayed by engineers who value diversity in engineering and promote inclusive behaviors within the profession.
In fall 2015, we developed new curriculum to promote inclusive engineering identities within first year engineering courses at a large public university. To assess the impact of the new curriculum, we used two previously developed scales: Appreciation of Cultural and Ethnic Diversity scale (Price et al., 2011) and Science Identity survey (Chemers et al. 2010; Estrada et al., 2011) adapted for engineering. While these two scales addressed diversity more broadly and general engineering identity, the two scales did not capture how students valued diversity within engineering specifically or how likely students were to enact inclusive behaviors. Thus, we created two new scales: Valuing Diversity within Engineering and Inclusive Behaviors in Engineering.
For both scales, we examined the relevant literature to determine what constructs needed to be addressed. For the Valuing Diversity Scale, three constructs surfaced. Specifically, engineers should value diversity to (a) address issues of social justice, (b) improve the bottom line, and (c) improve the work environment (Fouad, 2014). For the Inclusive Behaviors scale, three constructs also surfaced. Namely, engineers should engage in behaviors that (a) value all team members, (b) create an environment free of discrimination and bias, and (c) leverage diversity to improve teams (Finelli et al., 2011; Tonso, 2006).
After writing multiple items per construct for each scale, the items were reviewed by four content experts. After incorporating their feedback, we piloted the surveys with first year engineering students in fall 2016. Out of 400 invitations to participate, 276 students responded to the survey. We applied exploratory factor analysis (EFA) with principal axis factoring (Thompson, 2004) to the data from the two surveys separately. We examined Kaiser-Guttman rule, scree plot, parallel analysis (Hayton et al., 2004), and Velicer’s minum average partial (MAP; Velicer et al., 2000) test to determine the number of factors.
Valuing Diversity within Engineering. Results indicated a two factor solution. The resulting two factors were a collapsed version of the original three. The two factors were engineers should value diversity to: (a) promote a healthy workplace (inward focus) with 6 items, r = .90, and (b) serve customers better (outward focus) with three items, r = .81. The extracted factors explained 63% of the variance in the data.
Inclusive Behaviors in Engineering. Results indicated a two factor solution. The two factors were engineers should (a) challenge discriminatory behavior with five items, r = .89 and (b) promote a healthy team culture with ten items, r = .85. The extracted factors explained 52% of the variance in the data.
We intend to use the new scales in conjunction with the two original diversity and identity scales to determine how the curricular interventions impacted student appreciation for diversity and inclusive engineering identity development. Future studies include collecting more data for a confirmatory factor analysis. References
Chemers, M. M., Syed, M., Goza, B. K., Zurbriggen, E. L., Bearman, S., Crosby, F. J., & Morgan, E. M. (2010). The role of self-efficacy and identity in mediating the effects of science support programs (Technical Report No. 5). Santa Cruz, CA: University of California Estrada, M., Woodcock, A., Hernandez, P. R., & Schultz, P. W. (2011). Toward a Model of Social Influence That Explains Minority Student Integration into the Scientific Community. Journal of Educational Psychology, 103(1), 206-222. doi: Doi 10.1037/A0020743 Finelli, C.J., Bergom, I., and Mesa, V. (2011). Student teams in the engineering classroom and beyond: Setting up students for success. Center for Research on Learning and Teaching Occasional Papers, 29, University of Michigan. Fouad, N. A. (August, 2014). Leaning in, but getting pushed back (and out). American Psychological Association Annual convention, Washington, D.C. 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, 191-205. Thompson, B. (2004). Exploratory and confirmatory factor analysis. Washington, DC: American Psychological Association. Tonso, K.L. (2006). Teams that work: Campus culture, engineering identity, and social interactions. Journal of Engineering Education, 95(1), 25-37. Velicer, W. F., Eaton, C. A., & Fava, J. L. (2000). Construct explication through factor or component analysis: A review and evaluation of alternative procedures for determining the number of factors or components. In R. D. Goffin & E. Helmes (Eds.), Problems and solutions in human assessment: Honoring Douglas Jackson at seventy (pp. 41-71). Boston, MA: Kluwer.
Rambo-Hernandez, K. E., & Atadero, R. A., & Paguyo, C., & Schwartz, J. C. (2017, June), Inclusive Engineering Identities; Two New Surveys to Assess First-Year Students' Inclusive Values and Behaviors Paper presented at 2017 ASEE Annual Conference & Exposition, Columbus, Ohio. 10.18260/1-2--28502
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