The Development of the STEM Career Interest Survey (STEM-CIS)
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Internationally, efforts to increase student interest in science, technology, engineering, and mathematics (STEM) careers have been on the rise. It is often the goal of such efforts that increased interest in STEM careers should stimulate economic growth and enhance innovation. Scientific and educational organizations recommend that efforts to interest students in STEM majors and careers begin at the middle school level, a time when students are developing their own interests and recognizing their academic strengths. These factors have led scholars to call for instruments that effectively measure interest in STEM classes and careers, particularly for middle school students. In response, we leveraged the social cognitive career theory to develop a survey with subscales in science, technology, engineering, and mathematics. In this manuscript, we detail the six stages of development of the STEM Career Interest Survey. To investigate the instrument's reliability and psychometric properties, we administered this 44-item survey to over 1,000 middle school students (grades 6–8) who primarily were in rural, high-poverty districts in the southeastern USA. Confirmatory factor analyses indicate that the STEM-CIS is a strong, single factor instrument and also has four strong, discipline-specific subscales, which allow for the science, technology, engineering, and mathematics subscales to be administered separately or in combination. This instrument should prove helpful in research, evaluation, and professional development to measure STEM career interest in secondary level students.
KeywordsSTEM interest Instrument Survey Social cognitive career theory STEM careers Confirmatory factor analysis
The authors wish to thank Michael D. Cobb for his helpful suggestions on the initial development of this instrument and all of the collaborators on this project who participated in this research. This research was funded by an ITEST grant (2010–2013) from the National Science Foundation (award number 1031118). The opinions expressed are those of the authors and do not represent the views of the National Science Foundation or North Carolina State University.
- American Association of State Colleges and Universities (2005, November/December). Strengthening the science and mathematics pipeline for a better America. Policy Matters, 2(11), 1–4. Retrieved from http://www.aascu.org/uploadedFiles/AASCU/Content/Root/PolicyAndAdvocacy/PolicyPublications/STEM%20Pipeline.pdf/.
- American College Testing (2011). The condition of college and career readiness 2011. Retrieved January 3, 2012 from http://www.act.org/research/policymakers/cccr11/.
- Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Englewood Cliffs: Prentice-Hall.Google Scholar
- Blanchard, M.R., Albert, J.L., Alsbury, T.L., Williams, B. (2012). NSF ITEST Annual Project Outcomes Report: Innovative technology experiences for students and teachers. STEM Teams: Promoting Science, Technology, Engineering, and Mathematics (STEM) career interest, skills, and knowledge through Strategic Teaming. Arlington: National Science Foundation.Google Scholar
- Bowdich, S. (2009). Analysis of research exploring culturally responsive curriculum Hawaii. Paper presented to the Hawaii Educational Research Association Annual Conference, February 7, 2009.Google Scholar
- Business Europe (2011). Plugging the skills gap: The clock is ticking. Retrieved September 6, 2013 from http://www.businesseurope.eu/Content/Default.asp?pageid=568&docid=28659.
- Byrne, B. M. (2010). Structural equation modeling with AMOS: Basic concepts, applications, and programming. New York: Routledge.Google Scholar
- Cataldi, E.F., Green, C., Henke, R., Lew, T., Woo, J., Shepherd, B., and Siegel, P. (2011). 2008–09 Baccalaureate and Beyond Longitudinal Study (BB:08/09): First Look (NCES 2011–236). US Department of Education. Washington: National Center for Education Statistics. Retrieved February 4, 2012 from http://nces.ed.gov/pubs2011/2011236.pdf.
- Change the Equation. (2010). Change the Equation: Improving learning in science, technology, engineering, and mathematics. Retrieved December 28, 2012 at www.changetheequation.org.
- Clark, L.A., & Watson, D. (1995). Constructing validity: Basic issues in objective scale development. Psychological Assessment, 7(3), 309–319.Google Scholar
- Drew, C., (2011, November 4). Why science majors change their minds (It's just so darn hard). The New York Times. Retrieved from http://www.nytimes.com/2011/11/06/education/edlife/why-science-majors-change-their-mind-its-just-so-darn-hard.html?pagewanted=all.
- Eccles, J. S. (1994). Understanding women’s educational and occupational choices: Applying the Eccles et al. model of achievement related choices. Psychology of Women Quarterly, 18, 585–609.Google Scholar
- Fouad, N. A., Smith, P. L., & Enoch, L. (1997). Reliability and validity evidence for the Middle School Self-Efficacy Scale. Measurement and Evaluation in Counseling and Development, 30(1), 17–31.Google Scholar
- Healy, J., Mavromaras, K., Zhu, R. (2011). Consultant report securing Australia's future STEM: Country comparisons. Retrieved September 6, 2011 from http://www.acolasecretariat.org.au/ACOLA/PDF/SAF02Consultants/Consultant%20Report%20-%20Australian%20Labour%20Market.pdf.
- Hill, H. C. (2011). The nature and effects of middle school mathematics teacher learning experiences. Teachers College Record, 113(1), 205–234.Google Scholar
- Hill, C., Corbett, C., St Rose, A. (2010). Why so few? Women in science, technology, engineering, and mathematics. Washington: American Association of University Women. Retrieved March 22, 2011 at http://www.aauw.org/files/2013/02/Why-So-Few-Women-in-Science-Technology-Engineering-and-Mathematics.pdf.
- Kier, M.W. (2013). Examining the effects of a STEM career video intervention on the interests and STEM professional identities of rural, minority middle school students. Dissertation study, NC State University.Google Scholar
- Marsh, H. W., Hau, K. T., & Wen, Z. (2004). In search of golden rules: Comment on hypothesis-testing approaches to setting cutoff values for fit indexes and dangers in overgeneralizing Hu and Bentler's (1999) findings. Structural Equation Modeling, 11(3), 320–341.Google Scholar
- National Academy of Sciences, Global Affairs, & Institute Of Medicine. (2011). Expanding underrepresented minority participation: America's science and technology talent at the crossroads. Washington: National Academy Press.Google Scholar
- National Science Board (2010). Science and engineering indicators. Arlington: National Science Foundation (NSB 10-01).Google Scholar
- National Science Foundation. (2009). Women, minorities, and persons with disabilities in science and engineering: 2009. Arlington: National Science Foundation. Retrieved January 8, 2013 at http://www.nsf.gov/statistics/wmpd.
- Regisford, K. (2012, November 20). Life and work in a global city—The need to improve STEM education. The Recruitment & Employment Confederation. Retrieved November 2, 2012 from http://www.rec.uk.com/press/news/2253.
- Schwab, K., & Sala-i-Martín, X. (2012). Insight Report: The Global Competitiveness Report 2012–2013. Geneva: World Economic Forum. Google Scholar
- Scott, A. & Martin, A. (2012). Dissecting the data 2012: Examining STEM opportunities and outcomes for underrepresented students in California. Retrieved from May 15, 2012 from http://toped.svefoundation.org/wp-content/uploads/2012/04/Achieve-LPFIstudy032812.pdf.
- Skamp, K. (2007). Conceptual learning in the primary and middle years: The interplay of heads, hearts, and hands-on science. Teaching Science, 53(3), 18–22.Google Scholar
- STEMconnector® (2012). Where are the STEM students? National Report, Washington. Retrieved September 6, 2012 from http://www.stemconnector.org/sites/default/files/store/STEM-Students-STEM-Jobs-Executive-Summary.pdf.
- Stone, D.L., Johnson, R.D., Stone-Romero, E.F., Navas, D. (2005, February). Hispanic American and Anglo American beliefs, attitudes, and intentions to pursue careers in information technology. In E. McChrystal, A.Gujar, & C.Harmon (Chairs), Proceedings. Presentation conducted at the 26th annual Industrial Organizational/Organizational Behavior (IOOB) conference, Indialantic, FL.Google Scholar
- TechWomen. (2013). www.techwomen.org.
- Thomas, T., & Allen, A. (2006). Gender differences in students' perceptions of information technology as a career. Journal of Information Technology Education, 5, 165–178.Google Scholar
- 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.Google Scholar
- US Bureau of Labor Statistics (2010). Occupational outlook handbook, (2010–2011 ed.). Office of Occupational Statistics and Employment Projections. Retrieved January 9, 2012 from http://www.bls.gov/oco/oco2003.htm.
- Usher, E. L. (2009). Sources of middle school students' self-efficacy in mathematics: A qualitative investigation. American Educational Research Journal, 46(1), 275–314.Google Scholar
- Wells, B., Sanchez, A., & Attridge, J. (2007). Modeling student interest in science, technology, engineering and mathematics. IEEE Summit. “Meeting the growing demand for engineers and their educators,” Munich, Germany.Google Scholar
- White House Office of Science and Technology Policy (2012, February 13). Preparing a 21st Century workforce: Science, Technology, Engineering, and Mathematics (STEM) education in the 2013 Budget. Retrieved May 30, 2012 from http://www.whitehouse.gov/sites/default/files/microsites/ostp/fy2013rd_stem.pdf.
- Whitfield, A., Feller, R., & Wood, C. (2008). A counselor's guide to career assessment instruments. Broken Arrow: National Career Development Association.Google Scholar