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, 8 (1), 1-51

The Science of Sex Differences in Science and Mathematics

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The Science of Sex Differences in Science and Mathematics

Diane F Halpern et al. Psychol Sci Public Interest.

Abstract

Amid ongoing public speculation about the reasons for sex differences in careers in science and mathematics, we present a consensus statement that is based on the best available scientific evidence. Sex differences in science and math achievement and ability are smaller for the mid-range of the abilities distribution than they are for those with the highest levels of achievement and ability. Males are more variable on most measures of quantitative and visuospatial ability, which necessarily results in more males at both high- and low-ability extremes; the reasons why males are often more variable remain elusive. Successful careers in math and science require many types of cognitive abilities. Females tend to excel in verbal abilities, with large differences between females and males found when assessments include writing samples. High-level achievement in science and math requires the ability to communicate effectively and comprehend abstract ideas, so the female advantage in writing should be helpful in all academic domains. Males outperform females on most measures of visuospatial abilities, which have been implicated as contributing to sex differences on standardized exams in mathematics and science. An evolutionary account of sex differences in mathematics and science supports the conclusion that, although sex differences in math and science performance have not directly evolved, they could be indirectly related to differences in interests and specific brain and cognitive systems. We review the brain basis for sex differences in science and mathematics, describe consistent effects, and identify numerous possible correlates. Experience alters brain structures and functioning, so causal statements about brain differences and success in math and science are circular. A wide range of sociocultural forces contribute to sex differences in mathematics and science achievement and ability-including the effects of family, neighborhood, peer, and school influences; training and experience; and cultural practices. We conclude that early experience, biological factors, educational policy, and cultural context affect the number of women and men who pursue advanced study in science and math and that these effects add and interact in complex ways. There are no single or simple answers to the complex questions about sex differences in science and mathematics.

Figures

Fig. 1
Fig. 1
Difference in average scores between boys and girls for the combined reading literacy scale of fourth graders, by country. (Note that girls scored significantly higher than boys in all 33 countries in which the assessment was conducted.) Findings from the Progress in International Reading Literacy Study of 2001 (Mullis, Martin, Gonzalez, & Kennedy, 2003; Ogle et al., 2003).
Fig. 2
Fig. 2
An example of a mental rotation task. The task is to determine if the two figures labeled A and the two figures labeled B could be made identical by rotating them in space. These are called mental rotation tasks because the rotation must be done in working memory.
Fig. 3
Fig. 3
Average SAT-Mathematics scores of entering college classes, 1967–2004, by sex. Data from The College Entrance Examination Board (2004).
Fig. 4
Fig. 4
Sex differences in eventual career choices for two cohorts of mathematically talented youth. Data are from Benbow, Lubinski, Shea,& Eftekhari-Sanjani (2000).
Fig. 5
Fig. 5
Favorite and least-favorite high-school courses at age 18 (panels A and B), college majors at age 23 (panel C), and occupations at age 3 (panel D) as predicted by SAT-Mathematics (SAT-M; x-axis), SAT-Verbal (SAT-V; y-axis), and visuospatial ability (left vs. right arrows) for sample of precocious males and females. These 4 figures depict the simultaneous effects of math, verbal, and spatial scores, using 3-dimensiona space. Panel A, for example, shows that students who reported that their favorite high-school subjects were in the humanities or social science tended to be above the mean on the SAT-V (above the horizontal axis), below the mean on the SAT-M (to the left of the vertical axis), and below th mean on spatial ability (indicated with an arrow facing left). The other three panels can be read in the same way. Reprinted from “Importanc of Assessing Spatial Ability in Intellectually Talented Young Adolescents: A 20-Year Longitudinal Study,” by D.L. Shea, D. Lubinski, & C.P. Benbow, 2001, Journal of Educational Psychology, 93, pp. 604–614. Copyright 2001, the American Psychological Association. Reprinte with permission.
Fig. 6
Fig. 6
Illustration of the brain using three different magnetic resonance imaging methods: acquired T2-weighted image (left), proton density image (middle), and the segmented image (right), in which gray matter is shown in white, white matter in light gray, and cerebrospinal fluid inblack. Reprinted from “Sex Differences in Brain Gray and White Matter in Healthy Young Adults: Correlations With Cognitive Performance,” by R.C. Gur, B.I. Turetsky, M. Matsui, M. Yan, W. Bilker, P. Hughett, & R.E. Gur, 1999, Journal of Neu-roscience, 19, p. 4066. Copyright 1999 by the Society for Neuroscience. Reprinted with permission.
Fig. 7
Fig. 7
Mean percentages of gray matter (GM) and white matter (WM) tissue and cerebrospinal fluid (CSF)inthe brainsof men versus women (top) and differencesinlaterality between the sexes (bottom). The laterality index shows left-hemisphere minus right hemisphere ofGM, WM, and CSFinmen (dark bars) and women (light bars). Reprinted from “Sex Differences in Brain Gray and White Matter in Healthy Young Adults: Correlations With Cognitive Performance,” by R.C. Gur, B.I. Turetsky, M. Matsui, M. Yan, W. Bilker, P. Hughett, & R.E. Gur, 1999, Journal of Neuroscience, 19, p. 4068. Copyright 1999 by the Society for Neuroscience. Reprinted with permission.
Fig. 8
Fig. 8
Biopsychosocial model showing how genes, hormones, and experiences alter brain development and how individuals select experiences from the environment based on their predilections and past experiences, thus also altering the size and connectivity of their brains. In this model, nature and nurture exert reciprocal effects on each other. From Halpern (2000).
Fig. 9
Fig. 9
Eccles (1994) model showing how cultural milieu, beliefs, aptitudes, and experiences work together to create beliefs and expectations in developing children and ultimately influence those individuals’ achievement in math and science. Adapted from “UnderstandingWomen’s Educational and Occupational Choices: Applying the Eccles et al. Model of Achievement-Related Choices,” by J.S. Eccles, 1994, Psychology of Women Quarterly, 18, p. 588. Copyright 1994, Blackwell Publishing. Adapted with permission.

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