1932

Abstract

Improving science, technology, engineering, and mathematics (STEM) education, especially for traditionally disadvantaged groups, is widely recognized as pivotal to the United States's long-term economic growth and security. In this article, we review and discuss current research on STEM education in the United States, drawing on recent research in sociology and related fields. The reviewed literature shows that different social factors affect the two major components of STEM education attainment: () attainment of education in general, and () attainment of STEM education relative to non-STEM education conditional on educational attainment. Cognitive and social-psychological characteristics matter for both components, as do structural influences at the family, neighborhood, school, and broader cultural levels. However, whereas commonly used measures of socioeconomic status (SES) predict the attainment of general education, social-psychological factors are more important influences on participation and achievement in STEM versus non-STEM education. Domestically, disparities by family SES, race, and gender persist in STEM education. Internationally, American students lag behind those in some countries with fewer economic resources. Explanations for group disparities within the United States and the mediocre international ranking of US student performance require more research, a task that is best accomplished through interdisciplinary approaches.

Keyword(s): educationgenderinequalityraceSTEM
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2015-08-14
2024-12-06
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Literature Cited

  1. Adelman C. 2006. The toolbox revisited: paths to degree completion from high school through college US Dep. Educ., Washington, DC. http://files.eric.ed.gov/fulltext/ED490195.pdf [Google Scholar]
  2. Alexander KL, Entwisle DR, Bedinger SD. 1994. When expectations work: race and socioeconomic differences in school performance. Soc. Psychol. Q. 57:4283–99 [Google Scholar]
  3. Allen WR. 1992. The color of success: African-American college student outcomes at predominantly White and historically Black public colleges and universities. Harv. Educ. Rev. 62:126–45 [Google Scholar]
  4. Alon S, DiPrete TA. 2013. Orientation versus behavior: gender differences in field of study choice set Work. Pap., Dep. Sociol., Columbia Univ. [Google Scholar]
  5. Archer L, DeWitt J, Osborne J, Dillon J, Willis B, Wong B. 2012. Science aspirations, capital, and family habitus: how families shape children's engagement and identification with science. Am. Educ. Res. J. 49:5881–908 [Google Scholar]
  6. Aronson J, McGlone MS. 2008. Stereotype and social identity threat. The Handbook of Prejudice, Stereotyping, and Discrimination TD Nelson 153–78 New York: Psychology Press [Google Scholar]
  7. Aschbacher P, Li E, Roth EJ. 2010. Is science me? High school students' identities, participation and aspirations in science, engineering, and medicine. J. Res. Sci. Teach. 47:5564–82 [Google Scholar]
  8. Astone NM, McLanahan SS. 1991. Family structure, parental practices and high school completion. Am. Sociol. Rev. 56:3309–20 [Google Scholar]
  9. Autor DH, Katz LF, Kearney MS. 2008. Trends in U.S. wage inequality: revising the revisionists. Rev. Econ. Stat. 90:300–23 [Google Scholar]
  10. Baron-Cohen S. 2003. The Essential Difference: The Truth about the Male and Female Brain New York: Basic Books [Google Scholar]
  11. Becker GS. 1964. Human Capital: A Theoretical and Empirical Analysis, with Special Reference to Education New York: Columbia Univ. Press [Google Scholar]
  12. Becker GS. 1991. A Treatise on the Family Cambridge, MA: Harvard Univ. Press [Google Scholar]
  13. Beilock SL, Gunderson EA, Ramirez G, Levine SC. 2010. Female teachers' math anxiety affects girls' math achievement. PNAS 107:1860–63 [Google Scholar]
  14. Ben-David J. 1971. The Scientist's Role in Society Eaglewood Cliffs, NJ: Prentice Hall [Google Scholar]
  15. Blake J. 1981. Family size and quality of children. Demography 18:421–42 [Google Scholar]
  16. Blake J. 1989. Family Size and Achievement Los Angeles: Univ. Calif. Press [Google Scholar]
  17. Blau PM, Duncan OD. 1967. The American Occupational Structure New York: Wiley [Google Scholar]
  18. Bodovski K, Farkas G. 2008. Concerted cultivation and unequal achievement in elementary school. Soc. Sci. Res. 37:903–19 [Google Scholar]
  19. Boudon R. 1974. Education, Opportunity and Social Inequality New York: Wiley [Google Scholar]
  20. Bourdieu P. 1977. Cultural reproduction and social reproduction. Power and Ideology in Education J Karabel, AH Halsey 487–511 New York: Oxford Univ. Press [Google Scholar]
  21. Bowles S, Gintis H. 1976. Schooling in Capitalist America: Educational Reform and the Contradictions of Economic Life New York: Basic Books [Google Scholar]
  22. Bozick R, Lauff E, Wirt J. 2007. Education Longitudinal Study of 2002 (ELS:2002): a first look at the initial postsecondary experiences of the high school sophomore class of 2002 Natl. Cent. Educ. Stat., Washington, DC. http://nces.ed.gov/pubs2008/2008308.pdf [Google Scholar]
  23. Brand J, Xie Y. 2010. Who benefits most from college? Evidence for negative selection in heterogeneous economic returns to higher education. Am. Sociol. Rev. 75:273–302 [Google Scholar]
  24. Breiner JM, Harkness SS, Johnson CC, Koehler CM. 2012. What is STEM? A discussion about conceptions of STEM in education and partnerships. School Sci. Math. 112:13–11 [Google Scholar]
  25. Brooks-Gunn J, Duncan GJ, Klebanov PK, Sealand N. 1993. Do neighborhoods influence child and adolescent development?. Am. J. Sociol. 99:2353–95 [Google Scholar]
  26. Buchmann C, DiPrete TA, McDaniel A. 2008. Gender inequalities in education. Annu. Rev. Sociol. 34:319–37 [Google Scholar]
  27. Cabrera AF, Nora A, Terenzini PT, Pascarella E, Hagedorn LS. 1999. Campus racial climate and the adjustment of students to college: a comparison between White students and African-American students. J. Higher Educ. 70:2134–60 [Google Scholar]
  28. Cain K, Oakhill J, Bryant P. 2004. Children's reading comprehension ability: concurrent prediction by working memory, verbal ability, and component skills. J. Educ. Psychol. 96:131–42 [Google Scholar]
  29. Card D. 1999. The causal effect of education on earnings. Handbook of Labor Economics O Ashenfelter, D Card 3A1801–63 Amsterdam: Elsevier [Google Scholar]
  30. Carlone HB, Johnson A. 2007. Understanding the science experiences of successful women of color: science identity as an analytic lens. J. Res. Sci. Teach. 44:81187–218 [Google Scholar]
  31. Carrell SE, Page ME, West JE. 2010. Sex and science: how professor gender perpetuates the gender gap. Q. J. Econ. 125:31101–44 [Google Scholar]
  32. Catsambis S. 1994. The path to math: gender and racial-ethnic differences in mathematics participation from middle school to high school. Sociol. Educ. 67:3199–215 [Google Scholar]
  33. Catsambis S, Beveridge AA. 2001. Does neighborhood matter? Family, neighborhood, and school influences on eighth-grade mathematics achievement. Sociol. Focus 34:3435–57 [Google Scholar]
  34. Cech E, Rubineau B, Silbey S, Seron C. 2011. Professional role confidence and gendered persistence in engineering. Am. Sociol. Rev. 76:641–66 [Google Scholar]
  35. Ceci SJ, Williams WM. 2010. Sex differences in math-intensive fields. Curr. Dir. Psychol. Sci. 19:275–79 [Google Scholar]
  36. Ceci SJ, Williams WM. 2011. Understanding current causes of women's underrepresentation in science. PNAS 108:3157–62 [Google Scholar]
  37. Ceci SJ, Williams WM, Barnett SM. 2009. Women's underrepresentation in science: sociocultural and biological considerations. Psychol. Bull. 135:2218–61 [Google Scholar]
  38. Chang MJ, Cerna O, Han J, Saenz V. 2008. The contradictory roles of institutional status in retaining underrepresented minorities in biomedical and behavioral science majors. Rev. Higher Educ. 31:4433–64 [Google Scholar]
  39. Chang MJ, Eagan MK, Lin MH, Hurtado S. 2011. Considering the impact of racial stigmas and science identity: persistence among biomedical and behavioral science aspirants. J. Higher Educ. 82:5564–96 [Google Scholar]
  40. Chang MJ, Sharkness J, Hurtado S, Newman CB. 2014. What matters in college for retaining aspiring scientists and engineers from underrepresented racial groups. J. Res. Sci. Teach. 51:5555–80 [Google Scholar]
  41. Charles M, Bradley K. 2002. Equal but separate? A cross-national study of sex segregation in higher education. Am. Sociol. Rev. 67:573–99 [Google Scholar]
  42. Charles M, Bradley K. 2006. A matter of degrees: female underrepresentation in computer science programs cross-nationally. Women and Information Technology: Research on the Reasons for Underrepresentation J McGrath, B Aspray 183–203 Cambridge, MA: MIT Press [Google Scholar]
  43. Charles M, Bradley K. 2009. Indulging our gendered selves? Sex segregation by field of study in 44 countries. Am. J. Sociol. 114:924–76 [Google Scholar]
  44. Charles M, Harr B, Cech E, Hendley A. 2014. Who likes math where? Gender differences in eighth-graders' attitudes around the world. Int. Stud. Sociol. Educ. 24:85–112 [Google Scholar]
  45. Chen X. 2009. Students who study science, technology, engineering, and mathematics (STEM) in postsecondary education Natl. Cent. Educ. Stat., Washington, DC. http://nces.ed.gov/pubs2009/2009161.pdf [Google Scholar]
  46. Chen X, Soldner M. 2014. STEM attrition: college students' paths into and out of STEM fields Natl. Cent. Educ. Stat., Washington, DC. http://nces.ed.gov/pubs2014/2014001rev.pdf [Google Scholar]
  47. Cheryan S, Plaut VC, Davies PG, Steele CM. 2009. Ambient belonging: how stereotypical cues impact gender participation in computer science. J. Personal. Soc. Psychol. 97:1045–60 [Google Scholar]
  48. Cheryan S, Siy JO, Vichayapai M, Drury BJ, Kim S. 2011. Do female and male role models who embody STEM stereotypes hinder women's anticipated success in STEM?. Soc. Psychol. Personal. Sci. 2:656–64 [Google Scholar]
  49. Chipman SF. 2005. Research on the women and mathematics issue: a personal case history. Gender Differences in Mathematics AM Gallagher, JC Kaufman 1–24 New York: Cambridge Univ. Press [Google Scholar]
  50. Clotfelter CT, Ladd HF, Vigdor J. 2005. Who teaches whom? Race and the distribution of novice teachers. Econ. Educ. Rev. 24:4377–92 [Google Scholar]
  51. Cogan LS, Schmidt WH. 2002. Culture shock: eighth-grade mathematics from an international perspective. Educ. Res. Eval. 8:13–39 [Google Scholar]
  52. Cole D, Espinoza A. 2008. Examining the academic success of Latino students in science technology engineering and mathematics (STEM) majors. J. Coll. Stud. Dev. 49:285–300 [Google Scholar]
  53. Coleman JS. 1968. Equality of educational opportunity Off. Educ., US Dep. Health, Educ., Welf., Washington, DC. http://files.eric.ed.gov/fulltext/ED012275.pdf [Google Scholar]
  54. Coleman JS. 1988. Social capital in the creation of human capital. Am. J. Sociol. 94:S95–S120 [Google Scholar]
  55. Condron DJ, Roscigno VJ. 2003. Disparities within: unequal spending and achievement in an urban school district. Sociol. Educ. 76:18–36 [Google Scholar]
  56. Correll SJ. 2001. Gender and the career choice process: the role of biased self assessments. Am. J. Sociol. 106:1691–730 [Google Scholar]
  57. Correll SJ. 2004. Constraints into preferences: gender, status, and emerging career aspirations. Am. Sociol. Rev. 69:93–113 [Google Scholar]
  58. Crosnoe R. 2009. Low-income students and the socioeconomic composition of public high schools. Am. Sociol. Rev. 74:709–30 [Google Scholar]
  59. Croson R, Gneezy U. 2009. Gender differences in preferences. J. Econ. Lit. 47:1–27 [Google Scholar]
  60. Cunha F, Heckman J. 2009. The economics and psychology of inequality and human development. J. Eur. Econ. Assoc. 7:320–64 [Google Scholar]
  61. Cvencek D, Meltzoff AN, Greenwald AG. 2011. Math-gender stereotypes in elementary school children. Child Dev. 82:3766–79 [Google Scholar]
  62. Dabney KP, Devasmita C, Tai RH. 2013. The association of family influence and initial interest in science. Sci. Educ. 97:3395–409 [Google Scholar]
  63. Darling-Hammond L. 1999. Teacher quality and student achievement: a review of state policy evidence. Educ. Policy Anal. Arch. 8:1 [Google Scholar]
  64. Deary IJ, Strand S, Smith P, Fernandes C. 2007. Intelligence and educational achievement. Intelligence 35:13–21 [Google Scholar]
  65. DeWitt J, Archer L, Osborne J, Dillon J, Willis B, Wong B. 2011. High aspirations but low progression: the science aspirations-careers paradox among minority ethnic students. Int. J. Sci. Math. Educ. 9:2243–71 [Google Scholar]
  66. Dika SL, Singh K. 2002. Applications of social capital in educational literature: a critical synthesis. Rev. Educ. Res. 72:31–60 [Google Scholar]
  67. DiPrete TA, Buchmann C. 2013. The Rise of Women: The Growing Gender Gap in Education and What It Means for American Schools New York: Russell Sage Found. [Google Scholar]
  68. DiPrete TA, Jennings JL. 2012. Social and behavioral skills and the gender gap in early educational achievement. Soc. Sci. Res. 41:11–15 [Google Scholar]
  69. Downey DB. 1995. When bigger is not better: family size, parental resources, and children's educational performance. Am. Sociol. Rev. 60:5746–61 [Google Scholar]
  70. Downey DB. 2008. Black/white differences in school performance: the oppositional culture explanation. Annu. Rev. Sociol. 34:107–26 [Google Scholar]
  71. Downey DB, Von Hippel PT, Broh BA. 2004. Are schools the great equalizer? Cognitive inequality during the summer months and the school year. Am. Sociol. Rev. 69:613–35 [Google Scholar]
  72. Duckworth AL, Seligman ME. 2005. Self-discipline outdoes IQ in predicting academic performance of adolescents. Psychol. Sci. 16:939–44 [Google Scholar]
  73. Duncan GJ, Brooks-Gunn J, Klebanov PK. 1994. Economic deprivation and early childhood development. Child Dev. 65:296–318 [Google Scholar]
  74. Eccles JS. 2011a. Gendered educational and occupational choices: applying the Eccles et al. model of achievement-related choices. Int. J. Behav. Dev. 35:195–201 [Google Scholar]
  75. Eccles JS. 2011b. Understanding women's achievement choices: looking back and looking forward. Psychol. Women Q. 35:510–16 [Google Scholar]
  76. Ellison G, Swanson A. 2010. The gender gap in secondary school mathematics at high achievement levels: evidence from the American mathematics competitions. J. Econ. Perspect. 24:109–28 [Google Scholar]
  77. Else-Quest NM, Hyde JS, Linn MC. 2010. Cross-national patterns of gender differences in mathematics: a meta-analysis. Psychol. Bull. 136:103–27 [Google Scholar]
  78. England P, Allison P, Li S, Mark N, Thompson J. et al. 2007. Why are some academic fields tipping toward female? The sex composition of U.S. fields of doctoral degree receipt, 1971–2002. Sociol. Educ. 80:23–42 [Google Scholar]
  79. England P, Li S. 2006. Desegregation stalled: the changing gender composition of college majors, 1971–2002. Gender Soc. 20:657–77 [Google Scholar]
  80. Fang Z, Grant LW, Xu X, Stronge JH, Ward TJ. 2013. An international comparison investigating the relationship between national culture and student achievement. Educ. Assess. Eval. Account. 25:1–19 [Google Scholar]
  81. Farkas G. 2003. Cognitive skills and noncognitive traits and behaviors in stratification processes. Annu. Rev. Sociol. 29:541–62 [Google Scholar]
  82. Fischer CS, Hout M. 2006. Century of Difference: How America Changed in the Last One Hundred Years New York: Russell Sage Found. [Google Scholar]
  83. Fischer CS, Hout M, Sánchez M, Jankowski SR, Lucas AS, Vos K. 1996. Inequality by Design: Cracking the Bell Curve Myth Princeton, NJ: Princeton Univ. Press [Google Scholar]
  84. Fredricks JA, Eccles JS. 2002. Children's competence and value beliefs from childhood through adolescence: growth trajectories in two male-sex-typed domains. Dev. Psychol. 38:519–33 [Google Scholar]
  85. Frome PM, Alfeld CJ, Eccles JS, Barber BL. 2006. Why don't they want a male-dominated job? An investigation of young women who changed their occupational aspirations. Educ. Res. Eval. 12:359–72 [Google Scholar]
  86. Frome PM, Archer L, Eccles JS, Barber BL. 2008. Is the desire for a family-flexible job keeping young women out of male-dominated occupations?. Gender and Occupational Outcomes: Longitudinal Assessments of Individual, Social, and Cultural Influences HMG Watt, JS Eccles 195–214 Washington, DC: Am. Psychol. Assoc. [Google Scholar]
  87. Fryer RG, Levitt SD. 2004. Understanding the black-white test score gap in the first two years of school. Rev. Econ. Stat. 86:447–64 [Google Scholar]
  88. Fuchs T, Wößmann L. 2007. What accounts for international differences in student performance? A re-examination using PISA data. Empir. Econ. 32:433–62 [Google Scholar]
  89. Gallagher AM, Levin J, Calahan C. 2002. Cognitive patterns of gender differences on mathematics admission tests. GRE Board Rep. No 96–17P Educ. Test. Serv., Princeton, NJ [Google Scholar]
  90. Gamoran A, Mare RD. 1989. Secondary school tracking and educational inequality: compensation, reinforcement, or neutrality?. Am. J. Sociol. 94:51146–83 [Google Scholar]
  91. Gerber TP, Cheung SY. 2008. Horizontal stratification in postsecondary education: forms, explanations, and implications. Annu. Rev. Sociol. 34:299–318 [Google Scholar]
  92. Gneezy U, Niederle M, Rustichini A. 2003. Performance in competitive environments: gender differences. Q. J. Econ. 118:1049–74 [Google Scholar]
  93. Gneezy U, Rustichini A. 2004. Gender and competition at a young age. Am. Econ. Rev. 94:377–81 [Google Scholar]
  94. Goldin CD, Katz LF. 2008. The Race Between Education and Technology Cambridge, MA: Belknap [Google Scholar]
  95. Goldring R, Gray L, Bitterman A, Broughman S. 2013. Characteristics of public and private elementary and secondary school teachers in the United States: results from the 2011–12 Schools and Staffing Survey Natl. Cent. Educ. Stat., Washington, DC. http://nces.ed.gov/pubs2013/2013314.pdf [Google Scholar]
  96. Gonzales P, Calsyn C, Jocelyn L, Mak K, Kastberg D. et al. 2000. Pursuing excellence: comparisons of international eighth-grade mathematics and science achievement from a U.S. perspective, 1995 and 1999. Natl. Cent. Educ. Stat., Washington, DC. http://nces.ed.gov/pubs2001/2001028.pdf [Google Scholar]
  97. Gonzalez HB, Kuenzi JJ. 2012. Science, technology, engineering, and mathematics (STEM) education: a primer Congr. Res. Serv. 7-5700, Washington, DC. http://www.fas.org/sgp/crs/misc/R42642.pdf [Google Scholar]
  98. Graham MJ, Byars-Winston A, Hunter A-B, Handelsman J. 2013. Increasing persistence of college students in STEM. Science 341:1455–56 [Google Scholar]
  99. Grandy J. 1998. Persistence in science of high-ability minority students: results of a longitudinal study. J. Higher Educ. 69:6589–620 [Google Scholar]
  100. Greenman E, Bodovski K, Reed K. 2011. Neighborhood characteristics, parental practices and children's math achievement in elementary school. Soc. Sci. Res. 40:1434–44 [Google Scholar]
  101. Greenwald R, Hedges LV, Laine RD. 1996. The effect of school resources on student achievement. Rev. Educ. Res. 66:361–96 [Google Scholar]
  102. Grodsky E, Warren JR, Felts E. 2008. Testing and social stratification in American education. Annu. Rev. Sociol. 34:385–404 [Google Scholar]
  103. Guiso L, Monte F, Sapienza P, Zingales L. 2008. Culture, gender, and math. Science 320:1164–65 [Google Scholar]
  104. Hallinan MT. 1988. Equality of educational opportunity. Annu. Rev. Sociol. 14:249–68 [Google Scholar]
  105. Halpern DF. 2002. Sex differences in achievement scores: Can we design assessments that are fair, meaningful, and valid for girls and boys?. Issues Educ. 8:2 [Google Scholar]
  106. Halpern DF, Benbow CP, Geary DC, Gur RC, Hyde JS, Gernsbacher MA. 2007. The science of sex differences in science and mathematics. Psychol. Sci. Public Interest 8:1–51 [Google Scholar]
  107. Hanushek EA. 1989. Expenditures, efficiency, and equity in education: the federal government's role. Am. Econ. Rev. 79:2F46–F51 [Google Scholar]
  108. Hanushek EA, Peterson PE, Woessman L. 2010. US math performance in global perspective: how well does each state do at producing high-achieving students? PEPG Rep. No. 10-19., Harvard Univ. [Google Scholar]
  109. Harackiewicz JM, Rozek CR, Hulleman CS, Hyde JS. 2012. Helping parents to motivate adolescents in mathematics and science: an experimental test of a utility-value intervention. Psychol. Sci. 40:1–8 [Google Scholar]
  110. Harding DJ. 2003. Counterfactual models of neighborhood effects: the effect of neighborhood poverty on dropping out and teenage pregnancy. Am. J. Sociol. 109:676–719 [Google Scholar]
  111. Hattie J. 2008. Visible Learning: A Synthesis of Over 800 Meta-Analyses Relating to Achievement New York: Routledge [Google Scholar]
  112. Hauser RM, Tsai S-L, Sewell WH. 1983. A model of stratification with response error in social and psychological variables. Sociol. Educ. 56:20–46 [Google Scholar]
  113. Heckman JJ, Pinto R, Savelyev PA. 2013. Understanding the mechanisms through which an influential early childhood program boosted adult outcomes. Am. Econ. Rev. 103:62052–86 [Google Scholar]
  114. Hedges LV, Laine RD, Greenwald R. 1994. Does money matter? A meta-analysis of studies of the effects of differential school inputs on student outcomes. Educ. Res. 23:5–14 [Google Scholar]
  115. Hedges LV, Nowell A. 1995. Sex differences in mental test scores, variability, and numbers of high-scoring individuals. Science 269:41–45 [Google Scholar]
  116. Hedges LV, Nowell A. 1999. Changes in the Black-White gap in achievement test scores. Sociol. Educ. 72:2111–35 [Google Scholar]
  117. Herbert J, Stipek D. 2005. The emergence of gender differences in children's perceptions of their academic competence. J. Appl. Dev. Psychol. 26:276–95 [Google Scholar]
  118. Herrnstein RJ, Murray C. 1994. The Bell Curve: Intelligence and Class Structure in American Life New York: Free Press [Google Scholar]
  119. Hill HC, Rowan B, Ball DL. 2005. Effects of teachers' mathematical knowledge for teaching on student achievement. Am. Educ. Res. J. 42:371–406 [Google Scholar]
  120. Hill NE, Tyson DF. 2009. Parental involvement in middle school: a meta-analytic assessment of the strategies that promote achievement. Dev. Psychol. 45:740–63 [Google Scholar]
  121. Honey M, Pearson G, Schweingruber H. 2014. STEM Integration in K-12 Education: Status, Prospects, and an Agenda for Research Washington, DC: Natl. Acad. Press [Google Scholar]
  122. Hsin A, Xie Y. 2014. Explaining Asian Americans' academic advantage over whites. PNAS 111:238416–21 [Google Scholar]
  123. Hurtado S. 1992. The campus racial climate: contexts of conflict. J. Higher Educ. 35:1539–69 [Google Scholar]
  124. Hurtado S, Carter DF. 1997. Effects of college transition and perceptions of the campus racial climate on Latino college students' sense of belonging. Sociol. Educ. 70:4324–45 [Google Scholar]
  125. Hyde JS. 2005. The gender similarities hypothesis. Am. Psychol. 60:581–92 [Google Scholar]
  126. Hyde JS, Fennema E, Lamon SJ. 1990a. Gender differences in mathematics performance: a meta-analysis. Psychol. Bull. 107:139–55 [Google Scholar]
  127. Hyde JS, Feenema E, Ryan M, Frost LA, Hopp C. 1990b. Gender comparisons of mathematics attitudes and affect: a meta-analysis. Psychol. Women Q. 14:299–324 [Google Scholar]
  128. Hyde JS, Lindberg SM, Linn MC, Williams CC. 2008. Gender similarities characterize math performance. Science 321:494–95 [Google Scholar]
  129. Hyde JS, Mertz JE. 2009. Gender, culture, and mathematics performance. PNAS 106:8801–7 [Google Scholar]
  130. Jacobs JE, Chhin CS, Bleeker MM. 2006. Enduring links: parents' expectations and their young adult children's gender-typed occupational choices. Educ. Res. Eval. 12:395–407 [Google Scholar]
  131. Jacobs JE, Davis-Kean P, Bleeker MM, Eccles JS, Malanchuk O. 2005. “I can, but I don't want to”: the impact of parents, interests, and activities on gender differences in math. Gender Differences in Mathematics AM Gallagher, JC Kaufman 73–98 New York: Cambridge Univ. Press [Google Scholar]
  132. Jæger MM. 2011. Does cultural capital really affect academic achievement? New evidence from combined sibling and panel data. Sociol. Educ. 84:281–98 [Google Scholar]
  133. Jæger MM. 2012. The extended family and children's educational success. Am. Sociol. Rev. 77:6903–22 [Google Scholar]
  134. Jencks C, Bartlett S, Corcoran M, Crouse J, Eaglesfield D. et al. 1979. Who Gets Ahead? The Determinants of Economic Success in America New York: Basic Books [Google Scholar]
  135. Jencks C, Phillips M. 1998. The Black-White Test Score Gap Washington, DC: Brookings Inst. Press [Google Scholar]
  136. Jeynes WH. 2005. A meta-analysis of the relation of parental involvement to urban elementary school student academic achievement. Urban Educ. 40:237–69 [Google Scholar]
  137. Kao G, Thompson J. 2003. Racial and ethnic stratification in educational achievement and attainment. Annu. Rev. Sociol. 29:417–42 [Google Scholar]
  138. Kaushal N, Magnuson K, Waldfogel J. 2011. How is family income related to investments in children's learning?. Wither Opportunity? Rising Inequality, Schools, and Children's Life Chances GJ Duncan, RJ Murnane 187–205 New York: Russell Sage Found. [Google Scholar]
  139. Kelly D, Xie H, Nord CW, Jenkins F, Chan JY, Kastberg D. 2013. Performance of U.S. 15-year-old students in mathematics, science, and reading literacy in an international context: first look at PISA 2012. Natl. Cent. Educ. Stat., Washington, DC. https://nces.ed.gov/pubs2014/2014024rev.pdf [Google Scholar]
  140. Kelly S. 2009. The black-white gap in mathematics course taking. Sociol. Educ. 82:47–69 [Google Scholar]
  141. Kenney-Benson GA, Pomerantz EM, Ryan AM, Patrick H. 2006. Sex differences in math performance: the role of children's approach to schoolwork. Dev. Psychol. 42:11–26 [Google Scholar]
  142. Kiefer AK, Sekaquaptewa D. 2007. Implicit stereotypes, gender identification, and math-related outcomes: prospective study of female college students. Psychol. Sci. 18:13–18 [Google Scholar]
  143. Killewald A, Xie Y. 2013. American science education in its global and historical contexts. Bridge 43:15–23 [Google Scholar]
  144. Kim HS. 2011. Consequences of parental divorce for child development. Am. Sociol. Rev. 76:487–511 [Google Scholar]
  145. Knobloch-Westerwick S, Glynn CJ, Huge M. 2013. The Matilda effect in science communication: an experiment on gender bias in publication quality perceptions and collaboration interest. Sci. Commun. 35:603–25 [Google Scholar]
  146. Koenig KA, Frey MC, Detterman DK. 2008. ACT and general cognitive ability. Intelligence 36:153–60 [Google Scholar]
  147. Koput KW, Gutek BA. 2010. Gender Stratification in the IT Industry: Sex, Status and Social Capital Northampton, MA: Edward Elgar [Google Scholar]
  148. Krueger AB. 2003. Economic considerations and class size. Econ. J. 113:485F34–F63 [Google Scholar]
  149. Langen AV, Dekkers H. 2005. Cross-national differences in participating in tertiary science, technology, engineering and mathematics education. Comp. Educ. 41:329–50 [Google Scholar]
  150. Lareau A. 2011. Unequal Childhoods: Class, Race, and Family Life, with an Update a Decade Later Berkeley: Univ. Calif. Press [Google Scholar]
  151. Lauen DL, Gaddis SM. 2013. Exposure to classroom poverty and test score achievement: contextual effects or selection?. Am. J. Sociol. 118:943–79 [Google Scholar]
  152. Lavy V, Sand E. 2015. On the origins of gender human capital gaps: short and long term consequences of teachers' stereotypical biases NBER Work. Pap. Ser. No. 20909. http://www.nber.org/papers/w20909 [Google Scholar]
  153. Legewie J, DiPrete TA. 2014a. Pathways to science and engineering bachelor's degrees for men and women. Sociol. Sci. 1:41–48 [Google Scholar]
  154. Legewie J, DiPrete TA. 2014b. The high school environment and the gender gap in science and engineering. Sociol. Educ. 87:259–80 [Google Scholar]
  155. Leslie S, Cimpian A, Meyer M, Freeland W. 2015. Expectations of brilliance underlie gender distributions across academic disciplines. Science 347:262–65 [Google Scholar]
  156. Lindberg SM, Hyde JS, Petersen JL. 2010. New trends in gender and mathematics performance: a meta-analysis. Psychol. Bull. 136:1123–35 [Google Scholar]
  157. Linn MC, Petersen AC. 1985. Emergence and characterization of sex differences in spatial ability: a meta-analysis. Child Dev. 56:1479–98 [Google Scholar]
  158. Logan JR, Minca E, Adar S. 2012. The geography of inequality: why separate means unequal in American public schools. Sociol. Educ. 85:3287–301 [Google Scholar]
  159. Logel C, Walton GM, Spencer SJ, Iserman EC, von Hippel W, Bell AE. 2009. Interacting with sexist men triggers social identity threat among female engineers. J. Personal. Soc. Psychol. 96:1089–103 [Google Scholar]
  160. Lubinski DS, Benbow CP. 2006. Study of mathematically precocious youth after 35 years: uncovering antecedents for the development of math-science expertise. Perspect. Psychol. Sci. 1:4316–45 [Google Scholar]
  161. Lynn R. 1996. Racial and ethnic differences in intelligence in the United States on the Differential Ability Scale. Personal. Individ. Differ. 20:2271–73 [Google Scholar]
  162. Lynn R, Mikk J. 2009. National IQs predict educational attainment in math, reading and science across 56 nations.. Intelligence 37:3305–10 [Google Scholar]
  163. Ma Y. 2009. Family socioeconomic status, parental involvement, and college major choices: gender, race/ethnic, and nativity patterns. Sociol. Perspect. 52:211–34 [Google Scholar]
  164. Ma Y. 2011. Gender differences in the paths leading to a STEM baccalaureate. Soc. Sci. Q. 92:1169–90 [Google Scholar]
  165. Maltese AV, Tai RH. 2010. Eyeballs in the fridge: sources of early interest in science. Int. J. Sci. Educ. 32:669–85 [Google Scholar]
  166. Maltese AV, Tai RH. 2011. Pipeline persistence: examining the association of educational experiences with earned degrees in STEM among US students. Sci. Educ. 95:877–907 [Google Scholar]
  167. Mann A, DiPrete TA. 2013. Trends in gender segregation in the choice of science and engineering majors. Soc. Sci. Res. 42:1519–41 [Google Scholar]
  168. Maple SA, Stage FK. 1991. Influences on the choice of math/science major by gender and ethnicity. Am. Educ. Res. J. 28:37–60 [Google Scholar]
  169. Marks GN. 2013. Education, Social Background and Cognitive Ability: The Decline of the Social New York: Taylor & Francis [Google Scholar]
  170. Massey DS. 1993. American Apartheid: Segregation and the Making of the Underclass. Cambridge, MA: Harvard Univ. Press [Google Scholar]
  171. Mau WC. 2003. Factors that influence persistence in science and engineering career aspirations. Career Dev. Q. 51:234–43 [Google Scholar]
  172. Mayer SE. 1997. What Money Can't Buy: Family Income and Children's Life Chances Cambridge, MA: Harvard Univ. Press [Google Scholar]
  173. McLanahan S, Sandefur GD. 1994. Growing up with a Single Parent: What Hurts, What Helps Cambridge, MA: Harvard Univ. Press [Google Scholar]
  174. McLoyd VC. 1998. Socioeconomic disadvantage and child development. Am. Psychol. 53:185–204 [Google Scholar]
  175. Merton RK. 1973 (1942). The normative structure of science. In The Sociology of Science: Theoretical and Empirical Investigations R Merton 267–78 Chicago: Univ. Chicago Press [Google Scholar]
  176. Miller DI, Wai J. 2015. The bachelor's to Ph.D. STEM pipeline no longer leaks more women than men: a 30-year analysis. Front. Psychol. 6:37 [Google Scholar]
  177. Miller JD, Kimmel LG. 2012. Pathways to a STEMM profession. Peabody J. Educ. 87:26–45 [Google Scholar]
  178. Miller PH, Blessing JS, Schwartz S. 2006. Gender differences in high-school students' views about science. Int. J. Sci. Educ. 28:363–81 [Google Scholar]
  179. Mincer J. 1974. Schooling, Experience, and Earnings. New York: Columbia Univ. Press [Google Scholar]
  180. Morgan SL, Gelbgiser D, Weeden KA. 2013. Feeding the pipeline: gender, occupational plans, and college major selection. Soc. Sci. Res. 42:989–1005 [Google Scholar]
  181. Moss-Racusin CA, Dovidio JF, Brescoll VL, Graham MJ, Handelsman J. 2012. Science faculty's subtle gender biases favor male students. PNAS 109:4116474–79 [Google Scholar]
  182. Mulligan GM, Hastedt S, McCarroll JC. 2012. First-time kindergartners in 2010-11: first findings from the kindergarten rounds of the Early Childhood Longitudinal Study, Kindergarten Class of 2010-11 (ECLS-K:2011). Natl. Cent. Educ. Stat., Washington, DC. https://nces.ed.gov/pubs2012/2012049.pdf
  183. Murphy MC, Steele CM, Gross J. 2007. Signaling threat: how situational cues affect women in math, science, and engineering settings. Psychol. Sci. 18:879–85 [Google Scholar]
  184. Museus SD, Palmer RT, Davis RJ, Maramba DC. 2011. Special issue: racial and ethnic minority students' success in STEM education. ASHE Higher Educ. Rep. 36:1–140 [Google Scholar]
  185. NAS (Natl. Acad. Sci.), NAE (Natl. Acad. Eng.), IM (Inst. Med.) 2007. Rising Above the Gathering Storm: Energizing and Employing America for a Brighter Economic Future Washington, DC: Natl. Acad. Press [Google Scholar]
  186. NCES (Natl. Cent. Educ. Stat.) 2013. The Nation's Report Card: Trends in Academic Progress 2012 Washington, DC: Natl. Cent. Educ. Stat. [Google Scholar]
  187. Neal D. 2005. Why has black-white skill convergence stopped? NBER Work. Pap. 11090 [Google Scholar]
  188. Newcombe NS. 2010. Picture this: increasing math and science learning by improving spatial thinking. Am. Educ. 34:229–35 43 [Google Scholar]
  189. NGSS (Next Generation Science Standards) 2015. http://www.nextgenscience.org/
  190. Nguyen H-HD, Ryan AM. 2008. Does stereotype threat affect test performance of minorities and women? A meta-analysis of experimental evidence. J. Appl. Psychol. 93:1314–34 [Google Scholar]
  191. Niederle M, Vesterlund L. 2007. Do women shy away from competition? Do men compete too much?. Q. J. Econ. 122:1067–101 [Google Scholar]
  192. Niederle M, Vesterlund L. 2010. Explaining the gender gap in math test scores: the roles of competition. J. Econ. Perspect. 24:129–44 [Google Scholar]
  193. Nisbett RE. 2009. Intelligence and How to Get It: Why Schools and Cultures Count New York: Norton [Google Scholar]
  194. Nord C, Roey S, Perkins R, Lyons M, Lemanski N. et al. 2011. The Nation's Report Card. America's high school graduates: results of the 2009 NAEP High School Transcript Survey. Natl. Cent. Educ. Stat., Washington, DC. http://nces.ed.gov/nationsreportcard/pdf/studies/2011462.pdf [Google Scholar]
  195. Nosek BA, Banaji MR, Greenwald AG. 2002. Math = male, me = female, therefore math ≠ me. J. Personal. Soc. Psychol. 83:44–59 [Google Scholar]
  196. Nosek BA, Smyth FL, Sriram N, Lindner NM, Devos T. et al. 2009. National differences in gender-science stereotypes predict national sex differences in science and math achievement. PNAS 106:10593–97 [Google Scholar]
  197. NSB (Natl. Sci. Board) 2012. Science and Engineering Indicators 2012 Arlington, VA: Natl. Cent. Sci. Eng. Stat http://www.nsf.gov/statistics/seind12/ [Google Scholar]
  198. NSB 2014. Science and Engineering Indicators 2014 Arlington, VA: Natl. Cent. Sci. Eng. Stat http://www.nsf.gov/statistics/seind14/ [Google Scholar]
  199. Oakes J. 1990. Multiplying Inequalities: The Effects of Race, Social Class, and Tracking on Opportunities to Learn Mathematics and Science Santa Monica: RAND Corp. [Google Scholar]
  200. Oakes J, Saunders M. 2004. Education's most basic tools: access to textbooks and instructional materials in California's public schools. Teach. Coll. Rec. 106:101967–88 [Google Scholar]
  201. OECD (Organ. Econ. Co-op. Dev.) 2010. PISA 2009 Results: Overcoming Social Background. Equity in Learning Opportunities and Outcomes (Volume II) Paris: OECD [Google Scholar]
  202. Osborne J, Simon S, Collins S. 2003. Attitudes towards science: a review of the literature and its implications.. Int. J. Sci. Educ. 25:91049–79 [Google Scholar]
  203. Pavitt K. 1996. National policies for technical change: Where are the increasing returns to economic research?. PNAS 93:12693–700 [Google Scholar]
  204. Penner AM. 2008. Gender differences in extreme mathematical achievement: an international perspective on biological and social factors. Am. J. Sociol. 114:S138–70 [Google Scholar]
  205. Penner AM, Paret M. 2008. Gender differences in mathematics achievement: exploring the early grades and the extremes. Soc. Sci. Res. 37:239–53 [Google Scholar]
  206. Perez T, Cromley JG, Kaplan A. 2014. The role of identity development, values, and costs in college STEM retention. J. Educ. Psychol. 106:1315–29 [Google Scholar]
  207. Perez-Felkner L, McDonald S-K, Schneider B, Grogan E. 2012. Female and male adolescents' subjective orientations to mathematics and the influence of those orientations on postsecondary majors. Dev. Psychol. 48:1658–73 [Google Scholar]
  208. Plomin R, Owen MJ, McGuffin P. 1994. The genetic basis of complex human behaviors. Science 264:51661733–39 [Google Scholar]
  209. Price DJ. 1986. Little Science, Big Science—and Beyond New York: Columbia Univ. Press [Google Scholar]
  210. Provasnik S, Kastberg D, Ferraro D, Lemanski N, Roey S, Jenkins F. 2011. Highlights from TIMSS 2011: mathematics and science achievement of U.S. fourth- and eighth-grade students in an international context Natl. Cent. Educ. Stat., Washington, DC. http://nces.ed.gov/pubs2013/2013009_1.pdf [Google Scholar]
  211. Provasnik S, KewalRamani A, McLaughlin Coleman M, Gilbertson L, Herring W, Xie Q. 2007. Status of education in rural America Natl. Cent. Educ. Stat., Washington, DC. http://nces.ed.gov/pubs2007/2007040.pdf [Google Scholar]
  212. Raftery A, Hout M. 1993. Maximally maintained inequality: expansion, reform, and opportunity in Irish education, 1921–75. Sociol. Educ. 66:41–62 [Google Scholar]
  213. Raudenbush S, Bryk AS. 1986. A hierarchical model for studying school effects. Sociol. Educ. 59:1–17 [Google Scholar]
  214. Reardon SF. 2008. Thirteen ways of looking at the black-white test score gap Work. Pap. 2008-08, Stanford Inst. Res. Educ. Policy Pract., Stanford Univ. [Google Scholar]
  215. Reardon SF. 2011. The widening academic achievement gap between the rich and the poor: new evidence and possible explanations. In Whither Opportunity? Rising Inequality, Schools, and Children's Life Chances GJ Duncan, R Murnane 91–116 New York: Russell Sage Found. [Google Scholar]
  216. Reardon SF, Baker R, Klasik D. 2012. Race, income, and enrollment patterns in highly selective colleges, 1982–2004 Work. Pap., Cent. Educ. Policy Anal., Stanford Univ. [Google Scholar]
  217. Reardon SF, Bischoff K. 2011. Income inequality and income segregation. Am. J. Sociol. 116:1092–153 [Google Scholar]
  218. Reilly D, Neumann DL. 2013. Gender-role differences in spatial ability: a meta-analytic review. Sex Roles 68(9–10)521–35 [Google Scholar]
  219. Reuben E, Sapienza P, Zingales L. 2014. How stereotypes impair women's careers in science. PNAS 111:124403–8 [Google Scholar]
  220. Ridgeway CL. 2014. Why status matters for inequality. Am. Sociol. Rev. 79:1–16 [Google Scholar]
  221. Ridgeway CL, Correll SJ. 2004. Unpacking the gender system: a theoretical perspective on gender beliefs and social relations. Gender Soc. 18:510–31 [Google Scholar]
  222. Riegle-Crumb C, Farkas G, Muller C. 2006. The role of gender and friendship in advanced course taking. Sociol. Educ. 79:3206–28 [Google Scholar]
  223. Riegle-Crumb C, Grodsky E. 2010. Racial-ethnic differences at the intersection of math course-taking and achievement. Sociol. Educ. 83:3248–70 [Google Scholar]
  224. Riegle-Crumb C, Humphries M. 2012. Exploring bias in math teachers' perceptions of students' ability by gender and race/ethnicity. Gender Soc. 26:290–322 [Google Scholar]
  225. Riegle-Crumb C, King B. 2010. Questioning a white male advantage in STEM: examining disparities in college major by gender and race/ethnicity. Educ. Res. 39:656–64 [Google Scholar]
  226. 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. Am. Educ. Res. J. 49:1048–73 [Google Scholar]
  227. Riegle-Crumb C, Moore C, Ramos-Wada A. 2011. Who wants to have a career in science or math? Exploring adolescents' future aspirations by gender and race/ethnicity. Sci. Educ. 95:458–76 [Google Scholar]
  228. Robertson KF, Smeets S, Lubinski D, Benbow CP. 2010. Beyond the threshold hypothesis: Even among the gifted and top math/science graduate students, cognitive abilities, vocational interests, and lifestyle preferences matter for career choice, performance, and persistence. Curr. Dir. Psychol. Sci. 19:346–51 [Google Scholar]
  229. Rockoff JE. 2004. The impact of individual teachers on student achievement: evidence from panel data. Am. Econ. Rev. 94:247–52 [Google Scholar]
  230. Rohde TE, Thompson LA. 2007. Predicting academic achievement with cognitive ability. Intelligence 35:83–92 [Google Scholar]
  231. Roksa J, Potter D. 2011. Parenting and academic achievement: intergenerational transmission of educational advantage. Sociol. Educ. 84:299–321 [Google Scholar]
  232. Ross T, Kena G, Rathbun A, KewalRamani A, Zhang J. et al. 2012. Higher education: gaps in access and persistence study Natl. Cent. Educ. Stat., Washington, DC. https://nces.ed.gov/pubs2012/2012046.pdf [Google Scholar]
  233. Roth KJ, Druker SL, Garnier HE, Lemmens M, Chen C. 2006. Teaching science in five countries: results from the TIMSS 1999 Video Study Natl. Cent. Educ. Stat., Washington, DC. http://nces.ed.gov/pubs2006/2006011.pdf [Google Scholar]
  234. Rothwell J. 2013. The hidden STEM economy Brookings Inst. http://www.brookings.edu/research/reports/2013/06/10-stem-economy-rothwell [Google Scholar]
  235. Sadler PM, Sonnert G, Coyle HP, Cook-Smith N, Miller JL. 2013. The influence of teachers' knowledge on student learning in middle school physical science classrooms. Am. Educ. Res. J. 50:51020–49 [Google Scholar]
  236. Sadler PM, Sonnert G, Hazari Z, Tai R. 2012. Stability and volatility of STEM career interest in high school: a gender study. Sci. Educ. 96:411–27 [Google Scholar]
  237. Sampson RJ, Sharkey P, Raudenbush SW. 2008. Durable effects of concentrated disadvantage on verbal ability among African-American children. PNAS 105:845–52 [Google Scholar]
  238. Sastry N, Pebley AR. 2010. Family and neighborhood sources of socioeconomic inequality in children's achievement. Demography 47:777–800 [Google Scholar]
  239. Schmidt FL. 2011. A theory of sex differences in technical aptitude and some supporting evidence. Perspect. Psychol. Sci. 6:560–73 [Google Scholar]
  240. Schmidt WH. 2012. At the precipice: the story of mathematics education in the United States. Peabody J. Educ. 87:133–56 [Google Scholar]
  241. Schneider B, Swanson CB, Riegle-Crumb C. 1998. Opportunities for learning: course sequences and positional advantages. Soc. Psychol. Educ. 2:25–53 [Google Scholar]
  242. Sewell WH, Haller AO, Portes A. 1969. The educational and early occupational attainment process. Am. Sociol. Rev. 34:82–92 [Google Scholar]
  243. Seymour E, Hewitt NM. 1997. Talking about Leaving: Why Undergraduates Leave the Sciences Boulder, CO: Westview [Google Scholar]
  244. Sharkey P, Elwert F. 2011. The legacy of disadvantage: multigenerational neighborhood effects on cognitive ability. Am. J. Sociol. 116:1934–81 [Google Scholar]
  245. Sheltzer JM, Smith JC. 2014. Elite male faculty in the life sciences employ fewer women. PNAS 111:2810107–12 [Google Scholar]
  246. Shepherd H. 2011. The cultural context of cognition: what the implicit association test tells us about how culture works. Sociol. Forum 26:121–43 [Google Scholar]
  247. Shettle C, Roey S, Mordica J, Perkins R, Nord C. et al. 2007. America's high school graduates: results from the 2005 NAEP high school transcript study Natl. Cent. Educ. Stat., Washington, DC [Google Scholar]
  248. Simpkins SD, Davis-Kean PE, Eccles JS. 2006. Math and science motivation: a longitudinal examination of the links between choices and beliefs. Dev. Psychol. 42:170–83 [Google Scholar]
  249. Sjaastad J. 2012. Sources of inspiration: the role of significant persons in young people's choice of science in higher education. Int. J. Sci. Educ. 34:101615–36 [Google Scholar]
  250. Sousa S, Park EJ, Armor DJ. 2012. Comparing effects of family and school factors on cross-national academic achievement using the 2009 and 2006 PISA Surveys. J. Comp. Policy Anal. Res. Pract. 14:449–68 [Google Scholar]
  251. Spelke E. 2005. Sex differences in intrinsic aptitude for mathematics and science? A critical review. Am. Psychol. 60:950–58 [Google Scholar]
  252. Steele J, James JB, Barnett RC. 2002. Learning in a man's world: examining the perceptions of undergraduate women in male-dominated academic areas. Psychol. Women Q. 26:46–50 [Google Scholar]
  253. Steelman LC, Powell B, Werum R, Carter S. 2002. Reconsidering the effects of sibling configuration: recent advances and challenges. Annu. Rev. Sociol. 28:243–69 [Google Scholar]
  254. Stevenson HW, Stigler JW. 1992. The Learning Gap: Why Our Schools Are Failing and What We Can Learn from Japanese and Chinese Education New York: Simon & Schuster [Google Scholar]
  255. Stout JG, Dasgupta N, Hunsinger M, McManus MA. 2011. STEMing the tide: using ingroup experts to inoculate women's self-concept in science, technology, engineering, and mathematics (STEM). J. Personal. Soc. Psychol. 100:255–70 [Google Scholar]
  256. Su R, Rounds J, Armstrong PI. 2009. Men and things, women and people: a meta-analysis of sex differences in interests. Psychol. Bull. 135:859–84 [Google Scholar]
  257. Tai RH, Liu CQ, Maltese AV, Fan X. 2006. Planning early for careers in science. Science 312:1143–44 [Google Scholar]
  258. Tay L, Su R, Rounds J. 2011. People-things and data-ideas: bipolar dimensions?. J. Couns. Psychol. 5:424–40 [Google Scholar]
  259. Tenenbaum HR, Ruck MD. 2007. Are teachers' expectations different for racial minority than for European American students? A meta-analysis. J. Educ. Psychol. 99:253–73 [Google Scholar]
  260. Tinto V. 1987. Leaving College: Rethinking the Causes and Cures of Student Attrition Chicago: Univ. Chicago Press [Google Scholar]
  261. Tsui M. 2005. Family income, home environment, parenting, and mathematics achievement of children in China and the United States. Educ. Urban Soc. 37:3336–55 [Google Scholar]
  262. Turner SL, Steward JC, Lapan RT. 2004. Family factors associated with sixth-grade adolescents' math and science career interests. Career Dev. Q. 53:41–52 [Google Scholar]
  263. Turney K, McLanahan S. 2012. The academic consequences of early childhood problem behaviors Work. Pap. No. 1427, Princeton Univ. http://crcw.princeton.edu/workingpapers/WP12-17-FF.pdf [Google Scholar]
  264. Tytler R, Osborne J. 2012. Student attitudes and aspirations towards science. Second International Handbook of Science Education BJ Fraser, K Tobin, CJ McRobbie 597–625 Dordrecht, Neth.: Springer [Google Scholar]
  265. US Census Bureau 2014. Annual estimates of the resident population by sex, age, race, and Hispanic origin for the United States and States. April 1, 2010 to July 1, 2013. http://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=bkmk
  266. Valian V. 2014. Interests, gender, and science. Perspect. Psychol. Sci. 9:225–30 [Google Scholar]
  267. Valla JM, Ceci SJ. 2011. Can sex differences in science be tied to the long reach of prenatal hormones? Brain organization theory, digit ratio (2D/4D), and sex differences in preferences and cognition. Perspect. Psychol. Sci. 6:134–46 [Google Scholar]
  268. Wai J, Cacchio M, Putallaz M, Makel MC. 2010. Sex differences in the right tail of cognitive abilities: a 30-year examination. Intelligence 38:412–23 [Google Scholar]
  269. Wang M-T, Eccles JS, Kenny S. 2013. Not lack of ability but more choice: individual and gender differences in choice of careers in science, technology, engineering, and mathematics. Psychol. Sci. 24:770–75 [Google Scholar]
  270. Wang X. 2013. Why students choose STEM majors: motivation, high school learning, and postsecondary context of support. Am. Educ. Res. J. 50:51081–121 [Google Scholar]
  271. Ware NC, Lee VE. 1988. Sex differences in choice of college science majors. Am. Educ. Res. J. 25:4593–614 [Google Scholar]
  272. Watt HMG. 2004. Development of adolescents' self-perceptions, values, and task perceptions according to gender and domain in 7th- through 11th-grade Australian students. Child Dev. 75:1556–74 [Google Scholar]
  273. Watt HMG. 2006. The role of motivation in gendered educational and occupational trajectories related to maths. Educ. Res. Eval. 12:305–22 [Google Scholar]
  274. Wayne AJ, Youngs P. 2003. Teacher characteristics and student achievement gains: a review. Rev. Educ. Res. 73:89–122 [Google Scholar]
  275. Weinberger CJ. 2005. Is the science and engineering workforce drawn from the far upper tail of the math ability distribution?. Work. Pap., Inst. Soc., Behav., Econ. Res., Dep. Econ., Univ. Calif., Santa Barbara. http://www.econ.ucsb.edu/∼weinberg/uppertail.pdf
  276. Willis RJ, Rosen S. 1979. Education and self-selection. J. Polit. Econ. 87:S7–36 [Google Scholar]
  277. Woolnough BE. 1994. Effective Science Teaching. Developing Science and Technology Education. London: Open Univ. [Google Scholar]
  278. Xie Y, Goyette K. 2003. Social mobility and the educational choices of Asian Americans. Soc. Sci. Res. 32:467–98 [Google Scholar]
  279. Xie Y, Killewald AA. 2012. Is American Science in Decline? Cambridge, MA: Harvard Univ. Press [Google Scholar]
  280. Xie Y, Shauman KA. 2003. Women in Science: Career Processes and Outcomes Cambridge, MA: Harvard Univ. Press [Google Scholar]
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