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Journal of Science Education and Technology

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19-06-2020

Development and Initial Validation of the Student Interest and Choice in STEM (SIC-STEM) Survey 2.0 Instrument for Assessment of the Social Cognitive Career Theory Constructs

Authors: Sarah A. Roller, Sandra A. Lampley, Monica L. Dillihunt, Michael P. J. Benfield, Sampson E. Gholston, Matthew W. Turner, Andrew M. Davis

Published in: Journal of Science Education and Technology | Issue 5/2020

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Abstract

In response to a growing need for STEM professionals, this study reports the results of the initial validation of a refined survey instrument that purports to measure the five constructs of the social cognitive career theory framework within the subjects of mathematics, science, engineering and technology. To investigate the instrument’s reliability and psychometric properties, we administered this 45-item survey to students in grades 4–12. Reliability and validity (content and construct) were assessed using Cronbach’s coefficient alphas and structural equation modeling. Path coefficients for the five constructs indicated weak to moderate influences on the subscales, and the goodness-of-fit indices demonstrated that the model is acceptable. Initial results indicate the survey has the potential to collect reliable and valid data and suggest the instrument may be helpful in measuring students’ interests and choices in STEM careers for research, partnerships, and curricular development. Additionally, results highlight two areas for further investigation, which include the impact conscientious or random responders have on the survey’s psychometric properties and what constitutes satisfactory Cronbach’s alpha for results to be interpreted in a significant way.

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Adelson, J. L., & McCoach, D. B. (2011). Development and psychometric properties of the math and me survey: measuring third through sixth graders’ attitudes toward mathematics. Measurement and Evaluation in Counseling and Development, 44(4), 225–247.CrossRef

American College Testing (ACT). (2017). STEM education in the U.S.: Where we are and what we can do. Retrieved from: www.act.org/content/dam/act/unsecured/documents/STEM/2017/STEM-Education-in-the-US-2017.pdf

Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Englewood Cliffs: Prentice-Hall.

Bandura, A. (1997). Self-efficacy: The exercise of control. New York: Freeman.

Burton, L. J., & Mazerolle, S. M. (2011). Survey instrument validity part 1: principles of survey instrument development and validation in athletic training education research. Athletic Training Education Journal, 6(1), 27–35.CrossRef

Capobianco, B. (2015). Engineering identity development scale (EIDS). Purdue University Research Repository. https://doi.org/10.4231/R7WH2MX6.

Carvalho, J., & Chima, F. O. (2014). Applications of structural equation modeling in social sciences research. American International Journal of Contemporary Research, 4(1), 6–11.

Cho, E., & Kim, S. (2015). Cronbach’s coefficient alpha: well known but poorly understood. Organizational Research Methods, 18(2), 207–230.CrossRef

Churchill Jr., G. A., & Peter, J. P. (1984). Research design effects on the reliability of rating scales: a meta-analysis. Journal of Marketing Research, 21(4), 360–375.CrossRef

Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). New York: Lawrence Erlbaum Associates.

Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16(3), 297–334.CrossRef

Curran, P. G. (2016). Methods for the detection of carelessly invalid responses in survey data. Journal of Experimental Social Psychology, 66, 4–19.CrossRef

Erkut, S., & Marx, F. (2005). 4 schools for WIE (evaluation report). Wellesley: Wellesley College, Center for Research on Women.

Faber, M., Unfried, A., Wiebe, E. N., Corn, J., Townsend, L. W., & Collins, T. L. (2013). Student attitudes toward STEM: The development of upper elementary school and middle/high school student surveys. In Proceedings of the 2013 American Society for Engineering Education annual conference & exposition. Washington, DC: ASEE.

Germann, P. J. (1988). Development of the attitude toward science in school assessment and its use to investigate the relationship between science achievement and attitude toward science in school. Journal of Research in Science Teaching, 25(8), 689–703. https://doi.org/10.1002/tea.3660250807.CrossRef

Guzey, S., Moore, T., & Harwell, M. (2014). Development of an instrument to measure students’ attitudes toward STEM. School Science and Mathematics, 114(6), 271–279.CrossRef

Hair, J. H., Tatham, R. L., & Anderson, R. E. (1998). Multivariate data analysis (5th ed.). New York: Prentice Hall International.

Henseler, J., Ringle, C. M., & Sinkovics, R. (2009). The use of partial least squares path modeling in international marketing. In R. R. Sinkovics & P. N. Ghauri (Eds.), Advances in International Marketing: Vol. 20. New Challenges to International Marketing (pp. 277-319). https://doi.org/10.1108/S1474-7979(2009)0000020014.

Kennedy, J., Quinn, F., & Taylor, N. (2016). The school science attitude survey: a new instrument for measuring attitudes towards school science. International Journal of Research & Method in Education, 39(4), 422–445. https://doi.org/10.1080/1743727X.2016.1160046.CrossRef

Kier, M. W., Blanchard, M. R., Osborne, J. W., & Albert, J. L. (2013). The development of the STEM career interest survey (STEM-CIS). Research in Science Education, 44(3), 461–481.CrossRef

Kline, R. B. (2011). Principles and practice of structural equation modeling (2nd ed.). New York: The Guilford Press.

Lent, R. W. (2005). A social cognitive view of career development and counseling. In S. D. Brown & R. W. Lent (Eds.), Career development and counseling: putting theory and research to work (pp. 101–127). Hoboken, NJ: John Wiley & Sons Inc.

Lent, R. (2013). Social cognitive career theory. In S. D. Brown & R. W. Lent (Eds.), Career development and counseling: Putting theory and research to work (pp. 115–146). Hoboken: Wiley.

Lent, R. W., & Brown, S. D. (2006). Integrating person and situation perspectives on work satisfaction: a social-cognitive view. Journal of Vocational Behavior, 69(2), 236–247.CrossRef

Lent, R. W., & Brown, S. D. (2008). Social cognitive career theory and subjective well-being in the context of work. Journal of Career Assessment, 16(1), 6–21.CrossRef

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.CrossRef

Lent, R. W., Brown, S. D., & Hackett, G. (2000). Contextual supports and barriers to career choice: a social cognitive analysis. Journal of Counseling Psychology, 47(1), 36–49.CrossRef

MacCallum, R. C., Browne, M. W., & Sugawara, H. M. (1996) Power analysis and determination of sample size for covariance structure modeling. Psychological Methods, 1(2), 130–149.

Marginson, S., Roberts, K. & Tytler, R. (2013). STEM: Country comparisons: International comparisons of science, technology, engineering, and mathematics (STEM) education. Final Report. Australian Council of Learned Academies, Melbourne, VIC.

Marjanovic, Z., Holden, R., Struthers, W., Cribbie, R., & Greenglass, E. (2015). The inter-item standard deviation (ISD): an index that discriminates between conscientious and random responders. Personality and Individual Differences, 84, 79–83.CrossRef

National Research Council. (2011). Successful STEM education: A Workshop summary. A. Beatty, Rapporteur. Committee on Highly Successful Schools or Programs for K-12 STEM Education, Board on Science Education and Board on Testing and Assessment. Division of Behavioral and Social Sciences and Education. Washington, DC: The National Academies Press.

Noonan, R. Office of the Chief Economist, Economics and Statistics Administration, U.S. Department of Commerce. (2017). STEM Jobs: 2017 update (ESA Issue Brief # 02–17). Retrieved from https://files.eric.ed.gov/fulltext/ED594354.pdf

Nunnally, J. C. (1978). Psychometric theory (2nd ed.). New York: McGraw-Hill.

Peterson, R. A. (1994). A meta-analysis of Cronbach’s coefficient alpha. Journal of Consumer Research, 21(2), 381–391.CrossRef

Price, J. (2010). The effect of instructor race and gender on student persistence in STEM fields. Economics of Education Review, 29(6), 901–910.CrossRef

Roller, S. A., Lampley, S. A., Dillihunt, M. L., Benfield, M. P., & Turner, M. W. (2018). Student attitudes toward STEM: a revised instrument of social cognitivecareer theory constructs (Fundamental). In Proceedings of the 2018 American society for engineering education annual conference & exposition. Salt Lake City, UT: ASEE.

Schmitt, N. (1996). Uses and abuses of coefficient alpha. Psychological Assessment, 8(4), 350–353.CrossRef

Scholtes, V. A., Terwee, C. B., & Poolman, R. W. (2011). What makes a measurement instrument valid and reliable? Injury, 42(3), 236–240.CrossRef

Shapiro, C. A., & Sax, L. J. (2011). Major selection and persistence for women in STEM. New Directions for Institutional Research, 2011(152), 5–18.CrossRef

Taber, K. S. (2018). The use of cronbach’s alpha when developing and reporting research instruments in science education. Research in Science Education, 48(6), 1273–1296.CrossRef

Tapia, M., & Marsh, G. E. (2004). An instrument to measure mathematics attitudes. Academic Exchange Quarterly, 8(2), 16–21.

The President’s Council of Advisors on Science and Technology (PCAST). (2012). Report to the president—Engage to excel: Producing one million additional college graduates with degrees in science, technology, engineering, and mathematics. Washington, DC: U.S,. Government Printing Office. Retrieved from https://obamawhitehouse.archives.gov/sites/default/files/microsites/ostp/pcast-engage-to-excel-final_2-25-12.pdf

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), 345–368.

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.CrossRef

Washington, A.N., Grays, S. & Dasmohapatra, S. (2016). The computer science attitude and identify survey (CSAIS): A novel tool for measuring the impact of ethnic identity in underrepresented computer science students. Presented at the American Society for Engineering Education, New Orleans, LA.

Wilson, R.A., Sofroniou, N., Beaven, R., May-Gillings, M., Perkins, S., Lee, M. ... Pirie, J. (2016). Working Futures 2014-2024: UKCES evidence report no 100. Retrieved from https://www.gov.uk/government/publications/uk-labour-market-projections-2014-to-2024

Yilmaz, V., Celik, H. E., & Ekiz, E. H. (2006). Investigation of the factors that affect the Authority’s commitment with structural equation modeling; a sample of bank of primary and government. Journal of Social Sciences, 2, 171–184.

Title: Development and Initial Validation of the Student Interest and Choice in STEM (SIC-STEM) Survey 2.0 Instrument for Assessment of the Social Cognitive Career Theory Constructs
Authors: Sarah A. Roller
Sandra A. Lampley
Monica L. Dillihunt
Michael P. J. Benfield
Sampson E. Gholston
Matthew W. Turner
Andrew M. Davis
Publication date: 19-06-2020
Publisher: Springer Netherlands
Published in: Journal of Science Education and Technology / Issue 5/2020
Print ISSN: 1059-0145
Electronic ISSN: 1573-1839
DOI: https://doi.org/10.1007/s10956-020-09843-7

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