Tính không đồng nhất về chủng tộc và dân tộc trong tác động của MESA đến khóa học STEM AP và khát vọng ngành STEM của sinh viên đại học

Adams, S. (2014). The best jobs for 2014. New York: Forbes. Afterschool Alliance. (2011). STEM learning in afterschool: an analysis of impact and outcomes. Washington, DC: Afterschool Alliance. Allison, P. (2002). Missing data. Thousand Oaks: Sage Publications. Arpino, B., & Mealli, F. (2008). The specification of the propensity score in multilvel observational studies. Computational Statistics & Data Analysis, 55, 1770–1780. Aud, S., Hussar, W., Johnson, F., Kena, G., Roth, E., Manning, E., et al. (2012). The condition of education 2012 (NCES 2012-045). Washington, DC: U.S. Department of Education, National Center for Education Statistic. Becker, S. O., & Ichino, A. (2002). Estimation of average treatment effects based on propensity scores. STATA Journal, 2, 358–377. Bottia, M. C., Stearns, E., Mickelson, R. A., Moller, S., & Parker, A. D. (2015). The relationships among high school STEM learning experiences and students’ intent to declare and declaration of a STEM major in college. Teachers College Record, 117, 1–46. Bureau of Labor Statistics (2013). Labor force characteristics by race and ethnicity, 2012. BLS Reports. Caliendo, M., & Kopeinig, S. (2008). Some practical guidance for the implementation of propensity score matching. Journal of Economic Surveys, 22, 31–72. Catsambis, S. (1995). Gender, race, ethnicity, and science-education in the middle grades. Journal of Research in Science Teaching, 32, 243–257. Chemers, M., Zurbriggen, E., Syed, M., Goza, B., & Bearman, S. (2011). The role of efficacy and identity in science career commitment among underrepresented minority students. Journal of Social Issues, 67, 469–491. Chen, X. (2009). Students who study science, technology, engineering, and mathematics (STEM) in postsecondary education. In Stats in Brief (NCES 2009-161). National Center for Educational Statistics. Coleman, J. S., Campbell, E. Q., Hobson, C., McPartland, J., Mood, A., Weinfeld, F. D., et al. (1966). Equality of educational opportunity. Washington, DC: Government Printing Office. Correll, S. J. (2004). Constraints into preferences: Gender, status, and emerging career aspirations. American Sociological Review, 69, 93–113. Crosnoe, R., & Schneider, B. (2010). Social capital, information, and socioeconomic disparities in math course work. The American Journal of Education, 117, 79–107. D’Agostino, R. B. (1998). Propensity score methods for bias reduction in the comparison of a treatment to a non-randomized control group. Statistics in Medicine, 17, 2265–2281. Dalton, B., Ingels, S. J., Downing, J., Bozick, R., & Owings, J. (2007). Advanced mathematics and science coursetaking in the spring high school senior classes of 1982, 1992, and 2004. Washington, DC: National Center for Educational Statistics, Institute of Education Sciences, U.S. Department of Education. Davidson, A. (2012). Making it in America. Boston, MA: The Atlantic. Dehejia, R. (2005). Practical propensity score matching: A reply to Smith and Todd. Journal of Economic Theory, 125, 355–364. 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 (pp. 271–310). Weinheim: Wiley-VCH Verlag Gmbh. Heckman, J. J. (2006). Skill formation and the economics of investing in disadvantaged children. Science, 312, 1900–1902. Heckman, J. J., Ichimura, H., & Todd, P. (1998). Matching as an econometric evaluation estimator. Review of Economic Studies, 65, 261–294. Hewson, P. W., Kahle, J. B., Scantlebury, K., & Davies, D. (2001). Equitable science education in urban middle schools: Do reform efforts make a difference?*. Journal of Research in Science Teaching, 38, 1130–1144. Hinojosa, T., Rapaport, A., Jaciw, A., LiCalsi, C., & Zacamy, J. (2016). Exploring the foundations of the future STEM workforce: K–12 indicators of postsecondary STEM success. Washington, DC: U.S. Department of Education, Institute of Education Sciences, National Center for Education Evaluation and Regional Assistance, Regional Educational Laboratory Southwest. Hoepner, C. C. (2010). Advanced placement math and science courses: Influential factors and predictors for success in college STEM majors. Proquest Dissertation Publication. Ichino, A., Mealli, F., & Nannicini, T. (2008). From temporary help jobs to permanent employment: What can we learn from matching estimators and their sensitivity? Journal of Applied Economics, 23, 305–327. Jackson, S. A. (2003). The quiet crisis: Falling short in producing american scientific and technical talent. San Diego: Building Engineering & Science Talent (BEST). Kaushal, N., Magnuson, K., & Waldfogel, J. (2011). How is family income related to investments in children’s schooling. In G. J. Duncan, & R. J. Murnane (Eds.) (pp. 187–205). New York: Russell Sage Foundation. Kelly, S. (2009). The black–white gap in mathematics course taking. Sociology of Education, 82, 47–69. Kelly, S., & Zhang, Y. (2016). Teacher support and engagement in math and science: Evidence from the high school longitudinal study. High School Journal, 99, 141–165. Kim, C., Tamborini, C. R., & Sakamoto, A. (2015). Field of study in college and lifetime earnings in the United States. Sociology of Education, 88, 320–339. Legewie, J., & DiPrete, T. A. (2014). The high school environment and the gender gap in science and engineering. Sociology of Education, 87, 259–280. Leggon, C. B., & Pearson, W. (2006). Assessing programs to improve minority participation in STEM fields: What we know and what we need to know. In R. Ehrenberg, & C. V. Kuh (Eds.). Ithaca, NY: Cornell University Press. Leuven, E., & Sianesi, B. (2003). PSMATCH2: Stata module to perform full mahalanobis and propensity score matching, common support graphing, and covariate imbalance testing. Lewis, J., Menzies, H., Najera, E., & Page, R. (2009). Rethinking trends in minority participation in the sciences. Science & Education, 93, 961–977. Lucas, S. R. (1999). Tracking inequality. New York: Teachers College Press. Moakler, M. W., & Kim, M. M. (2014). College major choice in STEM: Revisiting confidence and demographic factors. Career Development Quarterly, 62, 128–142. Morgan, S. L. (1996). Trends in black–white differences in educational expectations: 1980–1992. Sociology of Education, 69, 308–319. Morgan, S. L., & Harding, D. J. (2006). Matching estimators of causal effects—Prospects and pitfalls in theory and practice. Sociological Methods & Research, 35, 3–60. Morgan, S. L., & Winship, C. (2014). Counterfactuals and causal inference: Methods and principles for social research (2nd ed.). Cambridge: Cambridge University Press. Muller, P., Stage, F. K., & Kinzie, J. (2001). Science achievement growth trajectories: Understanding factors related to gender and racial-ethnic differences in precollege science achievement. American Educational Research Journal, 38, 981–1012. NSF. (2014). Science and engineering indicators 2014. National Science Foundation (NSB 14-01). NSF and NCSES. (2013). Women, minorities, and persons with disabilities in science and engineering: 2013. Alexandria, VA: National Science Foundation. Oakes, J. (1990). Opportunities, achievement, and choice—Women and minority-students in science and mathematics (p. 16). Educ: Review of Research in Education. Orfield, G., Frankenberg, E., Ee, J., & Kuscera, J. (2014). Brown at 60: Great progress, a long retreat and an uncertain future. Ormerod, M. B., & Duckworth, D. (1975). Pupils’ attitudes to science. Slough: NFER. Owens, J., & Massey, D. S. (2011). Stereotype threat and college academic performance: A latent variables approach. Social Science Research, 40, 150–166. Press, W. H. (2013). What’s so special about science (and how much should we spend on it?). Science, 342, 817–822. Provasnik, S., Kastberg, D., Ferraro, D., Lemanski, N., Roey, S., & Jenkins, F. (2012). Highlights from TIMSS 2011: Mathematics and science achievement of US fourth-and eighth-grade students in an international context. Washington, DC: National Center for Education Statistics. Rakow, S. J., & Walker, C. L. (1985). The status of Hispanic American-students in science—Achievement and exposure. Science & Education, 69, 557–565. Riegle-Crumb, C., Farkas, G., & Muller, C. (2006). The role of gender and friendship in advanced course taking. Sociology of Education, 79, 206–228. Riegle-Crumb, C., & Grodsky, E. (2010). Racial-ethnic differences at the intersection of math course-taking and achievement. Sociology of Education, 83, 248–270. Rosenbaum, P. (2002). Observational studies. New York: Springer. Rosenbaum, J. E., & Harris, L. E. (2001). Low-income families in their new neighborhoods. Journal of Family Issues, 22, 183–210. Rosenbaum, P., & Rubin, D. B. (1983a). Assessing sensitivity to an unobserved binary covariate in an observational study with binary outcome. Journal of the Royal Statistical Society, Series B, 45, 212–218. Rosenbaum, P., & Rubin, D. B. (1983b). The Central role of the propensity score in observational studies for causal effects. Biometrika, 70, 41–55. Rosenbaum, P., & Rubin, D. B. (1985). Constructing a control group using multivariate matched sampling methods that incorporate the propensity score. The American Statistician, 39, 33–38. Royal Society. (2006). Taking a leading role—scientists survey. London: The Royal Society. Rubin, D. (1974). Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology, 66, 688–701. Rubin, D. B. (1978). Bayesian inference for causal effects: The role of randomization. Annals of Statistics, 6, 34–58. Rubin, D. (1980). Discussion of “randomization analysis of experimental data in the fisher randomization test” by Basu. Journal of American Statistical Association, 75, 591–593. Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and quasi-experimental designs for generalized inference. Boston: Houghton Mifflin. Simpkins, S. D., Price, C. D., & Garcia, K. (2015). Parental support and high school students’ motivation in biology, chemistry, and physics: Understanding differences among latino and caucasian boys and girls. Journal of Research in Science Teaching, 52, 1386–1407. Simpson, R. D., & Oliver, J. S. (1990). A Summary of major influences on attitude toward and achievement in science among adolescent students. Science & Education, 74, 1–18. Stake, J. E., & Mares, K. R. (2001). Science enrichment programs for gifted high school girls and boys: Predictors of program impact on science confidence and motivation. Journal of Research in Science Teaching, 38, 1065–1088. Staniec, J. F. O. (2004). The effects of race, sex, and expected returns on the choice of college major. Eastern Economic Journal, 30, 549–562. Steele, C., & Aronson, J. (1995). Stereotype threat and the intellectual test performance of African Americans. Journal of Personality and Social Psychology, 69, 797. Stuart, E., & Rubin, D. B. (2008). Best practices in quasi-experimental designs: Matching methods for causal inference. In J. Osborne (Ed.) (pp. 155–176). Thousand Oaks: Sage Publications. Summers, M. F., & Hrabowski, F. A. (2006). Preparing minority scientists and engineers. Institutions, 17, 18. Tai, R. H., Liu, C. Q., Maltese, A. V., & Fan, X. T. (2006). Planning early for careers in science. Science, 312, 1143–1144. Tyson, W., Lee, R., Borman, K. M., & Hanson, M. A. (2007). Science, technology, engineering, and mathematics (STEM) pathways: High school science and math coursework and postsecondary degree attainment. Journal of Education for Students Placed at Risk, 12, 243–270. von Hippel, P. T. (2007). Regression with missing Y’s: An improved strategy for analyzing multiply imputed data. Sociological Methodology, 37, 83–117. Wai, J., Lubinski, D., Benbow, C. P., & Steiger, J. H. (2010). Accomplishment in science, technology, engineering, and mathematics (STEM) and its relation to STEM educational dose: A 25-year longitudinal study. Journal of Educational Psychology, 102, 860. Walker, C. L., & Rakow, S. J. (1985). The status of Hispanic American-students in Science—attitudes. Hispanic Journal of Behavioral Sciences, 7, 225–245. Wang, X. (2013). Why students choose STEM Majors: Motivation, high school learning, and postsecondary context of support. American Educational Research Journal, 50, 1081–1121. Wigfield, A., & Eccles, J. S. (2000). Expectancy-value theory of achievement motivation. Contemporary Educational Psychology, 25, 68–81. Xie, Y., & Killewald, A. (2012). Is American Science in Decline?. Cambridge: Cambridge Harvard University Press. Yoder, B. L. (2012). Engineering by the Numbers. St. Joseph, MI: American Society of Agricultural and Biological Engineers. Yonezawa, S., Wells, A. S., & Serna, I. (2002). Choosing tracks:“Freedom of Choice” in detracking schools. American Educational Research Journal, 39, 37–67. Zhou, X. (2005). The institutional logic of occupational prestige ranking: Reconceptualization and reanalyses. American Journal of Sociology, 111, 90–140.