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Expanding the Pipeline: the Effect of Participating in Project Lead the Way on Majoring in a STEM Discipline

Abstract

Meeting the current demand for STEM graduates requires significantly increasing the number of students majoring in STEM fields. One program designed to increase the number of STEM majors is Project Lead The Way (PLTW). Using statewide data from Indiana, this research examined the effects of PLTW participation in high school on the likelihood of majoring in STEM during college. Propensity score matching and weighting were used to provide a rigorous evaluation of PLTW that would allow causal inferences to be made about program effectiveness. Results indicated that PLTW participation significantly increased the likelihood that students who attend college will major in a STEM discipline. The results also indicated a dosage effect for PLTW participation. Specifically, completing one PLTW course increased the likelihood of majoring in STEM by 0.16, and completing two PLTW courses increased the likelihood of majoring in STEM by 0.27. Completing three or more PLTW courses increased the likelihood of majoring in STEM by 0.38. Tests of the conditional independence assumption also revealed that it was unlikely that these results were the product of external, unmeasured variables. Thus, it appears likely that PLTW participation has a direct, causal effect on majoring in a STEM discipline during college.

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Fig. 1

Notes

  1. 1.

    Separate analyses were conducted using only students from high schools that offered PLTW courses, and the results replicated the findings of the current study.

References

  1. Achieve, Inc. (2015). Next generation science standards. Retrieved from http://www.achieve.org/next-generation-science-standards. Retrieved 30 May 2016.

  2. Afifi, A., May, S., & Clark, V. A. (2012). Practical multivariate analysis (5th ed.). Boca Raton: CRC Press.

    Google Scholar 

  3. Angrist, J. D., & Pischke, J. (2009). Mostly harmless econometrics: An empiricist’s companion. Princeton: Princeton University Press.

    Book  Google Scholar 

  4. Archer, L., Dewitt, J., Osborne, J., Dillon, J., Willis, B., & Wong, B. (2010). “Doing” science versus “being” a scientist: Examining 10/11-year-old school children’s construction of science through the lens of identity. Science Education, 94, 617–639.

    Article  Google Scholar 

  5. Becker, S. O., & Caliendo, M. (2007). Mhbounds—Sensitivity analysis for average treatment effects. The Stata Journal, 7, 71–83.

    Article  Google Scholar 

  6. Bottoms & Anthony. (2005, May). Project Lead the way: A pre-engineering curriculum that works. Atlanta: Southern Regional Education Board.

    Google Scholar 

  7. Bottoms & Uhn. (2007, September). Project Lead the way works: A new type of career and technical program. Atlanta: Southern Regional Education Board Research Report.

    Google Scholar 

  8. Brophy, S., Klein, S., Portsmore, M., & Rogers, C. (2008). Advancing engineering education in P-12 classrooms. Journal of Engineering Education, 97, 369–387.

    Article  Google Scholar 

  9. Business-Higher Education Forum (2011, August). Creating the workforce of the future: The STEM interest and proficiency challenge. Washington, DC: Author. Retrieved from http://www.bhef.com/sites/g/files/g829556/f/brief_2011_stem_inerest_proficiency.pdf. Retrieved 22 May 2014.

  10. Business-Higher Education Forum (2012, May). STEM interest among college students: Where they enroll. Washington, DC: Author. Retrieved from http://www.bhef.com/sites/g/files/g829556/f/brief_2012_stem_interest_enrollment.pdf. Retrieved 22 May 2014.

  11. Business-Higher Education Forum, & American College Testing (2014, May). Building the talent pipeline: Policy recommendations for ‘The Condition of STEM 2013.’ Washington, DC: Business-Higher Education Forum. Retrieved from http://www.bhef.com/sites/g/files/g829556/f/201406/2014_brief_BHEF_ACT_0.pdf. Retrieved 22 May 2014.

  12. Caliendo, M., & Kopeing, S. (2005, May). Some practical guidance for the implementation of propensity score matching. [IZA discussion paper no. 1588]. Bonn, Germany: Institute for the Study of labor. Retrieved from http://ftp.iza.org/dp1588.pdf. Retrieved 21 Aug 2014.

  13. Carnevale, A. P., & Rose, S. J. (2011). The undereducated American. Washington, DC: Georgetown University Center on Education and the Workforce. Retrieved from http://www9.georgetown.edu/grad/gppi/hpi/cew/pdfs/undereducatedamerican.pdf. Retrieved 14 Nov 2012.

  14. Carnevale, A. P., Smith, N., & Strohl, J. (2010, June). Help wanted: Projections of jobs and education requirements through 2018. Washington, DC: Georgetown University Center on Education and the Workforce. Retrieved from http://cew.georgetown.edu/JOBS2018/. Retrieved 14 Nov 2012.

  15. Cole, B., High, K., & Weinland, K. (2013). High school pre-engineering programs: Do they contribute to college retention? American Journal of Engineering Education, 4, 85–98.

    Google Scholar 

  16. Common Core State Standards Initiative (2015). Preparing America’s students for success. Retrieved from http://www.corestandards.org/. Retrieved 30 May 2016.

  17. Crisp, G., Nora, A., & Taggart, A. (2009). Student characteristics, pre-college, college, and environmental factors as predictors of majoring in and earning a STEM degree: An analysis of students attending a Hispanic serving institution. American Educational Research Journal, 46, 924–942.

    Article  Google Scholar 

  18. D’Agostino, R. B., Jr. (1998). Tutorial in biostatistics: Propensity score methods for bias reduction in the comparison of a treatment to a non-randomized control group. Statistics in Medicine, 17, 2265–2281.

    Article  Google Scholar 

  19. DiPrete, T. A., & Gangl, M. (2004). Assessing bias in the estimation of causal effects: Rosenbaum bounds on matching estimators and instrumental variables estimation with imperfect instruments. Sociological Methodology, 34, 271–310.

    Article  Google Scholar 

  20. Dixon, R. A., & Brown, R. A. (2012). Transfer of learning: Connecting concepts during problem solving. Journal of Technology Education, 24, 2–16.

    Google Scholar 

  21. Dong, N., & Lipsey, M. W. (2018). Can propensity score analysis approximate randomized experiments using pretest and demographic information in pre-K intervention research? Evaluation Review, 42, 34–70.

    Article  Google Scholar 

  22. Griffith, A. L. (2010). Persistence of women and minorities in STEM field majors: Is it the school that matters? Economics of Education Review, 29, 911–922.

    Article  Google Scholar 

  23. Heilbronner, N. N. (2011). Stepping onto the STEM pathway: Factors affecting talented students’ declaration of STEM majors in college. Journal for the Education of the Gifted, 34, 876–899.

    Article  Google Scholar 

  24. Heilbronner, N. N. (2012). The STEM pathway for women: What has changed? Gifted Child Quarterly, 57, 39–55.

    Article  Google Scholar 

  25. Heinrich, C., Maffioli, A., & Vásquez, G. (2010, August). A primer for applying propensity-score matching. [impact-evaluation guidelines technical notes, no. IDB-TN-161.] Washington, D. C.: Inter-American Development Bank.

  26. Imbens, G. W. (2000). The role of the propensity score in estimating dose-response functions. Biometrika, 87, 706.710.

    Article  Google Scholar 

  27. Imbens, G. W., & Wooldridge, J. M. (2009). Recent developments in the econometrics of program evaluation. Journal of Economic Literature, 47, 5–86.

    Article  Google Scholar 

  28. Indiana Department of Education (2018). Learn more Indiana: High school diploma options. Indianapolis, IN: Author. Retrieved from https://learnmoreindiana.org/college/preparing-for-college/high-school-diploma-options/. Retrieved 25 Nov 2018.

  29. Kang, J. D. Y., & Schafer, J. L. (2007). Demystifying double robustness: A comparison of alternative strategies for estimating a population mean from incomplete data. Statistical Science, 22, 523–539.

    Article  Google Scholar 

  30. Maltese, A. V., & Tai, R. H. (2010). Eyeballs in the fridge: Sources of early interest in science. International Journal of Science Education, 32, 669–685.

    Article  Google Scholar 

  31. Morgan, P. L., Farkas, G., Hillemeier, M. M., & Maczuga, S. (2016). Science achievement gaps begin very early, persist, and are largely explained by modifiable factors. Educational Researcher, 45(1), 18–35.

    Article  Google Scholar 

  32. Murnane, R. J., & Willett, J. B. (2011). Methods matter: Improving causal inference in educational and social science research. New York: Oxford University Press.

    Google Scholar 

  33. National Academy of Sciences, National Academy of Engineering, & Institute of Medicine. (2007). Rising above the gathering storm: Energizing and employing America for a brighter economic future. Washington, DC: National Academic Press.

    Google Scholar 

  34. National Academy of Sciences, National Academy of Engineering, & Institute of Medicine. (2010). Rising above the gathering storm, revisited: Rapidly approaching category 5. Washington, DC: National Academies Press.

    Google Scholar 

  35. National Assessment of Educational Progress (2017). The nation’s report card. Washington, DC: U. S. Department of Education. Retrieved from https://www.nationsreportcard.gov/math_2017/states/scores/?grade=8. Retrieved 25 Nov 2018.

  36. National Center for Education Statistics (n.d.). CIP 2010: What is CIP? Retrieved from https://nces.ed.gov/ipeds/cipcode/default.aspx?v=55. Retrieved 21 Sept 2013.

  37. National Science Foundation (2011, Fall). GSS-CIP crosswalk. Washington, DC.: Author. Retrieved from http://www.nsf.gov/statistics/nsf13331/pdf/2011_GSS_CIP_Crosswalk.pdf. Retrieved 30 May 2014.

  38. National Science Foundation (2013). Women, minorities, and persons with disabilities in science and engineering: 2013. Washington, DC: Author. Retrieved from http://www.nsf.gov/statistics/wmpd/2013/pdf/nsf13304_digest.pdf. Retrieved 30 May 2014.

  39. National Student Clearinghouse (2018). Student tracker. Herndon, VA: Author. Retrieved from http://www.studentclearinghouse.org/colleges/studenttracker/. Retrieved 24 Nov 2018.

  40. Olitsky, N. H. (2014). How do academic achievement and gender affect the earnings of STEM majors? A propensity score matching approach. Research in Higher Education, 55, 245–271.

    Article  Google Scholar 

  41. Ost, B. (2010). The role of peers and grades in determining major persistence in the sciences. Economics of Education Review, 29, 923–934.

    Article  Google Scholar 

  42. Pike, G. R., & Robbins, K. (2014, March). Using propensity scores to evaluate education programs. Paper presented at the annual meeting of the Indiana Association for Institutional Research. Indianapolis.

  43. President’s Council of Advisors on Science and Technology. (2010). Prepare and inspire: K-12 science, technology, engineering, and math (STEM) education for America’s future. Washington, DC.: Executive Office of the President. Retrieved from http://www.whitehouse.gov/sites/default/files/microsites/ostp/pcast-stem-ed-final.pdf. Retrieved 22 May 2014.

  44. Project Lead The Way (2018a). About PLTW. Indianapolis, IN: Author. Retrieved from https://www.pltw.org/about-us. Retrieved 25 Nov 2018.

  45. Project Lead The Way (2018b). Our approach. Indianapolis, IN: Author. Retrieved from https://www.pltw.org/about-us/our-approach. Retrieved 25 Nov 2018.

  46. Project Lead The Way (2018c). Our programs. Indianapolis, IN: Author. Retrieved from https://www.pltw.org/our-programs. Retrieved 25 Nov 2018.

  47. Rask, K. (2010). Attrition in STEM fields at a liberal arts college: The importance of grades and pre-college preferences. Economics of Education Review, 29, 892–900.

    Article  Google Scholar 

  48. Reynolds, C. L., & DesJardins, S. L. (2009). The use of matching methods in higher education research: Answering whether attendance at a 2-year institution results in differences in educational attainment. In J. C. Smart (Ed.), Higher education: Handbook of theory and research (Vol. XXIV, pp. 47–104). Dordrecht: Springer.

    Chapter  Google Scholar 

  49. Rhoades, G. (2012). The incomplete completion agenda: Implications for academe and the academy. Liberal Education, 98(1), 18–25.

    Google Scholar 

  50. Riegle-Crumb, C., King, B., Grodsky, E., & Muller, C. (2012). The more things change, the more they stay the same? Prior achievement fails to explain gender inequality in entry into STEM college majors over time. American Educational Research Journal, 49, 1048–1073.

    Article  Google Scholar 

  51. Robinson, M. (2003). Student enrollment in high school AP sciences and calculus: How does it correlated with STEM careers? Bulletin of Science, Technology, & Society, 23, 265–273.

    Article  Google Scholar 

  52. Rosenbaum, P. R. (2002). Observational studies (springer series in statistics (2nd ed.). New York: Springer.

    Book  Google Scholar 

  53. Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70, 41–55.

    Article  Google Scholar 

  54. Rosenbaum, P. R., & Rubin, D. B. (1985a). Constructing a control group using multivariate matched sampling methods that incorporate the propensity score. The American Statistician, 39, 33–38.

    Google Scholar 

  55. Rosenbaum, P. R., & Rubin, D. B. (1985b). The bias due to incomplete matching. Biometrics, 41, 103–116.

    Article  Google Scholar 

  56. Rubin, D. B. (1974). Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology, 66, 688–701.

    Article  Google Scholar 

  57. Sax, L. J., Kanny, M. A., Riggers-Piehl, T. A., Whang, H., & Paulson, L. N. (2015). “But I’m not good at math”: The changing salience of mathematical self-concept in shaping women’s and men’s STEM aspirations. Research in Higher Education, 56, 813–842.

    Article  Google Scholar 

  58. Schenk Jr., T., Laanan, F. S., Starobin, S. S., & Rethwisch, D. (2012). An evaluation of Iowa project Lead the way on student outcomes: Summary report. Ames, IA: Iowa State University Community College Leadership Program. Retrieved from http:///www.cclp.hs.iastate.edu/research/pltw.php. Retrieved 22 May 2014.

  59. Schneider, B., Carnoy, M., Kilpatrick, J., Schmidt, W. H., & Shavelson, R. J. (2007). Estimating causal effects using experimental and observational designs. Washington, DC: American Educational Research Association.

    Google Scholar 

  60. Shadish, W. R., Clark, M. H., & Steiner, P. M. (2008). Can nonrandomized experiments yield accurate answers? A randomized experiment comparing random and nonrandom assignements. Journal of the American Statistical Association, 103, 1334–1343.

    Article  Google Scholar 

  61. Starobin, S. S., Schenk, Jr., T., Laanan, F. S., & Rethwisch, D. (2013, June). Evaluation research of the Iowa project plead the way: Final project report. Ames, IA: Iowa State University Community College Leadership Program. Retrieved from http:///www.cclp.hs.iastate.edu/research/pltw.php. Retrieved 22 May 2014.

  62. StataCorp. (2017). Treatment effects. College Station: Author.

    Google Scholar 

  63. Van Overschschelde, J. P. (2013). Project Lead the way students more prepared for higher education. American Journal of Engineering Education, 4, 1–11.

    Google Scholar 

  64. Wang, X. (2013a). Modeling entrance into STEM fields of study among students beginning at community colleges and four-year institutions. Research in Higher Education, 54, 664–692.

    Article  Google Scholar 

  65. Wang, X. (2013b). Why students choose STEM majors: Motivation, high school learning, and postsecondary context of support. American Educational Research Journal, 50, 1081–1121.

    Article  Google Scholar 

  66. Watt, W. (2015, March 23). Indiana leads the way in STEM education. Retrieved from https://www.alec.org/article/indiana-leads-the-way-in-stem-education/. Retrieved 25 Nov 2018.

  67. What Works Clearinghouse (2014, March). What works Clearinghouse procedures and standards manual version 3.0. Washington, DC: U.S. Department of Education. Retrieved from http://ies.ed.gov/ncee/wwc/pdf/reference_resources/wwc_procedures_v3_0_standards_handbook.pdf. Retrieved 6 May 2018.

  68. Wiggins, G., & McTighe, J. (2005). Understanding by design. Alexandria: Association for Supervision and Curriculum Development.

    Google Scholar 

  69. Wiggins, G., & McTighe, J. (2008). Put understanding first. Educational Leadership, 65(1), 36–41.

    Google Scholar 

  70. Wooldridge, J. M. (2007). Inverse probability weighted estimation for general missing data problems. Journal of Econometrics, 141, 1281–1301.

    Article  Google Scholar 

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Pike, G.R., Robbins, K. Expanding the Pipeline: the Effect of Participating in Project Lead the Way on Majoring in a STEM Discipline. Journal for STEM Educ Res 2, 14–34 (2019). https://doi.org/10.1007/s41979-019-00013-y

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Keywords

  • Learning by design
  • Program evaluation
  • Project Lead the way
  • Propensity score matching
  • STEM