This study examines the disparities in, changes in, and longitudinal interrelationships among mathematics achievement and motivational factors for Hispanics and their White, Black, and Asian peers throughout high school. Analyzing the nationally representative High School Longitudinal Study of 2009, regression results indicate that Hispanics trail other racial/ethnic groups in math cognitive and psychosocial factors, except that they outperform their Black counterparts in math standardized assessments. Cross-lagged path analyses further reveal that while math intellectual competencies and expectancy-value beliefs are generally reciprocal over time for all racial/ethnic groups, the estimated effect of initial math expectancy on subsequent math test scores for Hispanics is 2.4 to 2.6 times larger than for non-Hispanics. This finding highlights the distinctive developmental patterns of math achievement and motivational factors for young Hispanic students, which have important implications for research and practice on increasing participation of Hispanics in science, technology, engineering, and mathematics (STEM) education and careers.
AlfaroE. C.Umaña-TaylorA. J. (2015). The longitudinal relation between academic support and Latino adolescents’ academic motivation. Hispanic Journal of Behavioral Sciences, 37, 319-341.
2.
BaileyS.BurbidgeL.CampbellP. B.JacksonB.MarxF.McIntoshP. (1992). The American Association of University Women (AAUW) report: How schools shortchange girls. Washington, DC: AAUW Education Foundation.
3.
BøeM. V.HenriksenE. K.LyonsT.SchreinerC. (2011). Participation in science and technology: Young people’s achievement-related choices in late-modern societies. Studies in Science Education, 47, 37-72.
4.
BoutakidisI.RodríguezJ. L.MillerK. K.BarnettM. (2014). Academic engagement and achievement among Latina/o and non-Latina/o adolescents. Journal of Latinos and Education, 13, 4-13.
5.
CohenA. (1983). Comparing regression coefficients across subsamples: A study of the statistical test. Sociological Methods & Research, 12, 77-94.
6.
EcclesJ. S. (1983). Female achievement patterns: Attributions, expectancies, values, and choice. Journal of Social Issues, 1, 1-26.
7.
EcclesJ. S. (2009). Who am I and what am I going to do with my life? Personal and collective identities as motivators of action. Educational Psychologist, 44(2), 78-89.
8.
FerrimanK.LubinskiD.BenbowC. P. (2009). Work preferences, life values, and personal views of top math/science graduate students and the profoundly gifted: Developmental changes and sex differences during emerging adulthood and parenthood. Journal of Personality and Social Psychology, 97, 517-532.
9.
FrankK. A.MullerC.SchillerK. S.Riegle-CrumbC.MuellerA. S.CrosnoeR.PearsonJ. (2008). The social dynamics of mathematics course taking in high school. American Journal of Sociology, 113, 1645-1696.
10.
FryerR. G.LevittS. D. (2004). Understanding the Black-White test score gap in the first two years of school. Review of Economics and Statistics, 86, 447-464.
11.
GriffithA. L. (2010). Persistence of women and minorities in STEM field majors: Is it the school that matters?Economics of Education Review, 29, 911-922.
12.
HairJ. F.BlackW. C.BabinB. J.AndersonR. E. (2010). Multivariate data analysis (7th ed.). Upper Saddle River, NJ: Pearson.
13.
HollandJ. L. (1997). Making vocational choices: A theory of vocational personalities and work environments (3rd ed.). Odessa, FL: Psychological Assessment Resources.
14.
IngelsS. J.PrattD. J.HergetD. R.BurnsL. J.DeverJ. A.OttemR.. . . LeinwandS. (2011). High School Longitudinal Study of 2009 (HSLS:09). Base-year data file documentation. Washington, DC: U.S. Department of Education.
15.
IngelsS. J.PrattD. J.HergetD. R.DeverJ. A.FritchL. B.OttemR.. . . LeinwandS. (2013). High School Longitudinal Study of 2009 (HSLS:09) Base year to first follow-up data file documentation. Washington, DC: U.S. Department of Education.
16.
LentR. W.BrownS. D.HackettG. (1994). Toward a unifying social cognitive theory of career and academic interest, choice, and performance. Journal of Vocational Behavior, 45(1), 79-122.
17.
MazzaG. L.EndersC. K.RuehlmanL. S. (2015). Addressing item-level missing data: A comparison of proration and full information maximum likelihood estimation. Multivariate Behavioral Research, 50, 504-519.
18.
MorganS.GelbgiserD.WeedenK. (2013). Feeding the pipeline: Gender, occupational plans, and college major selection. Social Science Research, 42, 989-1005.
19.
MullerP. A.StageF. K.KinzieJ. (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.
20.
National Academy of Sciences, National Academy of Engineering, and Institute of Medicine. (2007). Rising above the gathering storm: Energizing and employing Americans for a brighter economic future. Washington, DC: The National Academies Press.
21.
National Academy of Sciences, National Academy of Engineering, and Institute of Medicine. (2011). Expanding underrepresented minority participation: America’s science and technology talent at the crossroads. Washington, DC: The National Academies Press.
22.
National Academy of Sciences, National Academy of Engineering, and Institute of Medicine. (2016). Developing a national STEM workforce strategy: A workshop summary. Washington, DC: The National Academies Press.
23.
National Science Board. (2016). Science and engineering indicators 2016. Arlington, VA: National Science Foundation.
24.
PatelS.BarreraA. Z.StramblerM.MuñozR. F.MacciomeiE. (2016). The achievement gap among newcomer immigrant adolescents: Life stressors hinder Latina/o academic success. Journal of Latinos and Education, 15, 121-133.
25.
RaykovT. (1997). Estimation of composite reliability for congeneric measures. Applied Psychological Measurement, 21, 173-184.
26.
Riegle-CrumbC.MooreC.Ramos-WadaA. (2011). Who wants to have a career in science or math? Exploring adolescents’ future aspirations by gender and race/ethnicity. Science Education, 95, 458-476.
27.
RosenbaumP. R. (2010). Design of observational studies. New York, NY: Springer-Verlag.
28.
SafavianN.ConleyA. (2016). Expectancy-value beliefs of early-adolescent. Hispanic and non-Hispanic youth: Predictors of mathematics achievement and enrollment. AERA Open, 2, doi:2332858416673357
29.
SchneiderB. L.SawG. K. (2016). Racial and ethnic gaps in postsecondary aspirations and enrollment. Russell Sage Foundation Journal of the Social Sciences, 2, 58-82.
30.
StevensT.OlivárezA.Jr.HammanD. (2006). The role of cognition, motivation, and emotion in explaining the mathematics achievement gap between Hispanic and White students. Hispanic Journal of Behavioral Sciences, 28, 161-186.
31.
WangM. T.DegolJ. (2013). Motivational pathways to STEM career choices: Using expectancy–value perspective to understand individual and gender differences in STEM fields. Developmental Review, 33, 304-340.
32.
WangM. T.DegolJ.YeF. (2015). Math achievement is important, but task values are critical, too: Examining the intellectual and motivational factors leading to gender disparities in STEM careers. Frontiers in Psychology, 6, Article 36.
33.
WangX. (2013). Why students choose STEM majors: Motivation, high school learning, and postsecondary context of support. American Educational Research Journal, 50, 1081-1121.
34.
White House. (2011). A strategy for American innovation: Securing our economic growth and prosperity. Washington, DC: Executive Office of the President.
35.
WigfieldA.TonksS.KlaudaS. L. (2009). Expectancy-value theory. In WentzelK. R.WigfieldA. (Eds.), Handbook of motivation at school (pp. 55-75). New York, NY: Routledge.
36.
XieY.KillewaldA. A. (2012). Is American science in decline?Cambridge, MA: Harvard University Press.
