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National Academy of Sciences (US), National Academy of Engineering (US), and Institute of Medicine (US) Committee on Maximizing the Potential of Women in Academic Science and Engineering. Biological, Social, and Organizational Components of Success for Women in Academic Science and Engineering. Washington (DC): National Academies Press (US); 2006.

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Biological, Social, and Organizational Components of Success for Women in Academic Science and Engineering.

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Biopsychosocial Contributions to Cognitive Performance*

Diane F. Halpern

Berger Institute for Work, Family, and Children

Claremont McKenna College

Abstract

Females and males are both similar and different in their cognitive performance. There is no evidence to support claims for a smarter sex. Males and females have different average scores on different cognitive measures; some show an advantage for females and others show an advantage for males. Females are achieving at higher rates in school at all levels and in all subjects, including subjects in which they obtain lower scores on aptitude/ability tests (e.g., advanced mathematics). Although there is much overlap in the female and male distributions, on average, females excel on many memory tasks including memory for objects and location, episodic memory, reading literacy, speech fluency, and writing. Males excel at visuospatial transformations, especially mental rotation, science achievement, mathematics tests that are not tied to a specified curriculum (possibly due to use of novel visuospatial representations and transformations), and males are more variable on many cognitive tests. A biopsychosocial model that recognizes the reciprocal relationships among many types of variables is used as an explanatory framework.

There have been remarkable changes in the lives of women and men in the blink of history that was the 20th century. College enrollments went from consisting largely of men from the privileged classes near the start of the 20th century to men from all socioeconomic classes and literally, all stripes, as they returned from World War II near mid-century. College enrollments for women at the same time consisted mostly of women of privilege, or exceptional talent, or high moti vation, or some combination of all three. But, by the time the post-war baby boom reached college age, women were attending college at an increasingly higher rate than earlier generations, in part because the baby boomers faced more competition as they entered an overcrowded work force. By 1982, the number of women enrolled in and graduating from college exceeded that of men, and the gap in favor of women has continued to widen ever since.

Among women between 25 and 34 years old, 33% have completed college, compared to 29% of men. Women also get higher grades in school, on average, in every subject area (Dwyer and Johnson, 1997; Kimball, 1989). These changes have occurred faster than any gene can mutate or any theory of evolution can explain, so it is not surprising that most people look to societal explanations for the changing roles of men and women. Although women still dominate enrollments in the “helping professions,” such as teaching, social work, and nursing, they have been increasing their enrollments in traditional male disciplines. Males have been much slower to enter the traditional female disciplines. There have been many initiatives to accelerate the increase in the numbers of women in academic areas commonly known as STEM—Science, Technology, Engineering, and Mathematics—however the underrepresentation of women, particularly at the full professor level in university faculties, was brought into a near frenzy of public debate when Lawrence Summers (January 14, 2005), president of Harvard University, offered his personal beliefs about this topic. Summers identified these three broad hypotheses as possible reasons for the large disparities in the percentage of women in academic positions in universities: (1) high-powered job hypothesis; (2) differential availability of aptitude at the high end, and (3) different socialization and patterns of discrimination in the faculty search process. Summers eliminated the third hypothesis quite simply by concluding that there could not be discrimination against women in the process of searching and hiring professors because discrimination would have to occur on every campus in the United States. If there were one or even a few campuses that did not discriminate against women scientists, then these campuses would have many outstanding women at the level of full professor who had been discriminated against at the other campuses; since there are no such campuses, there could not have been discrimination in the hiring or promotion process. Summers’ hasty dismissal of all that is known about implicit stereotyping (Banaji and Hardin, 1996), social expectations, in-group and out-group behaviors (Shelton and Richeson, 2005), and social psychology created a firestorm of controversy. He later retracted his statements and pledged $50 million to enhance faculty diversity and support women’s programs at Harvard. The other two hypotheses proposed by Summers are addressed in greater detail below.

Summers’ statements raised a serious question that is often not asked at the many symposia and talk shows that have followed as a result of his remarks: Are there too few women with the cognitive abilities to become our highest level scientists and mathematicians?

There are many science disciplines and women are dominating some of them. Women now comprise 75% of all graduating veterinarians, a field that is sometimes considered one of the most difficult of the sciences because there are multiple biological systems to be learned; women are obtaining 50% of medical school degrees, and 44% of the PhDs in the biological and life sciences, so women clearly have the innate ability to succeed in science. By contrast, women are getting only 29% of the doctorates in mathematics; 17% in engineering; and 22% in computer/information sciences. These percentages are higher than they used to be, but not equal to the number of males in these areas. On the other hand, should we be just as concerned about the low percentage of men who obtain only 32% of PhDs in psychology, 37% in health sciences, 34% in education (U.S. Department of Education, 2000)? Clearly women have the cognitive ability to learn and succeed in math and science, although there are sex differences in the fields of sciences in which they are selecting. The differences among these fields are sometimes described by a theory that suggests that biological or life sciences are preferred by women and inorganic sciences are preferred by men, but when psychologists look over this list, alternative categorizations emerge. For example, Lippa (1998) found that women, by a large margin, prefer to work with people— a career preference that also fits with women’s success in the field of law, which used to be dominated by men, versus men’s, strong preference for working with “things.” Ackerman et al. (2001) studied how trait complexes, which consist of abilities, interests, and personality variables, combine to influence achievement and career goals.

These data raise interesting philosophical questions about values and opportunities: would we expect or want all fields of study and all careers to become approximately equal in the numbers of men and women, and if so, at what cost are we willing to pursue that goal?

Biopsychosocial Model

When it comes to understanding cognitive performance, males and females are both similar and different, and some of the differences are small and some are large. There are cognitive tasks and tests that show, on average, some differences that favor females and some that favor males. There is also much overlap, so we do not have distinctly different groups, but overlapping distributions. In thinking about the differences, some of them have not changed over the decades for which we have data. Most people prefer environmental explanations, but are willing to settle for an explanation the will give a percentage of the “explanation” to nurture, a percentage to nature, and a percentage to their interaction. But nature and nurture cannot act independently, and they cannot “just interact.” Nature and nurture mutually influence each other in reciprocal ways and cannot be separated. It is not as though there is a number that exists in the real world and if researchers are very clever they will discover the percentage that can be attributed to nature or nurture and their interaction. Nature and nurture have no meaning without each other—nature needs nurture and vice versa.

The distinction between biology and experience is hopelessly blurred, so asking whether nature or nurture plays the greater part in determining a cognitive sex difference is the wrong question. Consider, for example, the brain. It is the quintessential “biological” organ, yet, it is also shaped extensively by experience. There are many sex differences in the architecture of the brain, but it cannot be assumed that differences in female and male brains result solely from genetic or hormonal action. The importance of experience was demonstrated in a study of London cab drivers that found that the cabbies had enlarged portions of their right posterior hippocampus relative to a control group of adults whose employment required less use of spatial navigational skills (Maguire et al., 2000). The cab drivers showed a positive correlation between the size of the region of the hippocampus that is activated during recall of complex routes and the number of years they worked in this occupation. The finding that size of the hippocampus varied as a function of the number of years spent driving taxis makes it likely that it was a lifetime of complex way-finding that caused the brain structure used in certain visual-spatial tasks to increase in size.

The burgeoning field of hormone replacement therapies for men and women is providing evidence that hormones continue to be important in cognition throughout the life span, although the field is complex and rife with controversies. The best evidence for a beneficial effect is the effect of estrogen on verbal memory in old age. Even though there are many studies that have failed to find beneficial effects for hormone replacement in elderly women, a substantial number of studies suggest that exogenous estrogen (pill, patch, cream, or other form) causes positive effects on the cognition of healthy older women and possibly for women in early stages of Alzheimer’s disease. This conclusion is in accord with Sherwin’s (1999) meta-analytic review of 16 prospective, placebo-controlled studies in humans, where she concludes that “Estrogen specifically maintains verbal memory in women and may prevent or forestall the deterioration in short- and long-term memory that occurs with normal aging. There is also evidence that estrogen decreases the incidence of Alzheimer disease or retards its onset or both” (p. 315). The results of these studies and others provide a causal link between levels of adult hormones and sex-typical patterns of cognitive performance.

A graphic depiction of the biopsychosocial model is shown in Figure 2-6 as a continuous, dynamic loop, essentially blurring the distinction between biology and environment. Learning, for example, is both a biological and environmental variable, with the brain differentially responsive to new learning based on prior learning, genetic factors, nutrition, and much more. Even hormones, which are usually considered “biological” variables, do not act in fixed or preprogrammed ways, but act within a context. We now know, for example, that testosterone can increase or decrease depending on whether an individual wins or loses a game (Schultheiss et al. 2005) and that some cognitive measures vary slightly over the menstrual cycle for cycling women and over the diurnal cycle for men, but the size of the fluctuations in cognitive performance are too small to be meaningful in everyday life (Halpern and Tan, 2001; Moffat and Hampson, 1996). The biopsychosocial model also makes it easier to understand that although sex differences are often (not always) found on some cognitive tasks, these differences are not immutable or inevitable and “biological” variables are developed in environments that are more or less favorable to their development and maintenance.

FIGURE 2-6. Biopsychosocial model in which the nature-nurture dichotomy is replaced with a continuous feedback loop.

FIGURE 2-6

Biopsychosocial model in which the nature-nurture dichotomy is replaced with a continuous feedback loop.

Sex Differences in Cognitive Performance

In understanding sex differences in cognitive performance, Hyde’s (2005) recent meta-analyses remind us that the sexes are similar in more ways than they are different. The standardized intelligence tests were written and normed to show no overall sex differences, but even a comparison of cognitive tests that were not deliberately normed to eliminate sex differences provide no evidence of overall sex differences in intelligence (Jensen, 1998). These tests do, however, show predictable sex differences on their subscores.

Some researchers object to the study of sex differences because they fear that it promotes false stereotypes and prejudice, but, there is nothing inherently sexist in a list of cognitive sex differences; prejudice is not intrinsic in data, but can be seen in the way people misuse data to promote a particular viewpoint or agenda. Prejudice also exists in the absence of data. Research is the only way to separate myth from empirically supported findings. A necessarily very brief overview of the largest differences is presented here. For a more complete review, see Halpern (2000).

Female:

  • Writing and comprehending complex prose. In a report published by the U. S. Department of Education, entitled, “Trends in Educational Equity of Girls and Women,” the data on reading and writing achievement are described this way, “Females have consistently outperformed males in writing achievement at the 4th, 8th, and 11th grade levels between 1988 and 1996. Differences in male and female writing achievement were relatively large. The writing scores of female 8th graders were comparable to those of 11th grade males” (U.S. Department of Education, 2000, p. 18). A meta-analysis by Hedges and Nowell (1995) called the sex difference in writing that favored girls to be so large as to be “alarming”. The female advantage in writing may be one reason why females get higher grades in school, on average. Any assessment that relies on writing provides an advantage to females.
  • Rapid access to and use of phonological, semantic, and episodic information in long term memory. Many laboratory tests show females are better at generating synonyms, recalling information about events, and numerous standard memory tasks such as object location and identity (Herlitz, Nilsson, & Baeckman, 1997, Levy, Astur, & Frick, 2005).
  • Speech articulation and fine motor tasks. Females are much less likely to stutter and have better fine motor skills (e.g., O’Boyle, Hoff, & Gill, 1995). These results could be interpreted as females are “naturally” better at typing, or small motor repair, or brain surgery.

Male:

  • Visuospatial transformations, especially mental rotation. This is a well-replicated and large effect that has not declined in over 30 years (between 0.9 to 1.0 standard deviations; Halpern & Collaer, 2005; Masters & Sanders, 1993; Nordvik & Amponsah, 1998). In addition, performance on mental rotations tasks improve with practice and the improved performance transfers to novel mental rotation stimuli, but performance improves equally for women and men (Peters et al. 1995). Numerous replications with training do not find a sex by training interaction. Females do not especially benefit from training. An example of a mental rotation task is shown in Figure 2-7. The task is to determine if the pairs of figures can be rotated to be identical. When this test is administered on a com puter, both the number (and percentage) correct is recorded with the reaction time for each item. Men not only get more items correct, but they also rotate the items more quickly than most women.
  • Fluid (novel) reasoning tasks in math and science. The advantage for males in mathematics is seen on some math tests. As already noted, females get higher grades in school, even in advanced math and science courses, although there are usually many fewer females enrolled in these courses. The advantage for males in math and science is found on high stakes tests that are not tied to a specific curriculum, which means that the problems require novel approaches, most frequently visuospatial problem representation or transforming visuospatial information in working memory (Gallagher, Levin & Cahalan, 2002). The size of the male advantage gets larger as the population sampled becomes more selective. In other words, the difference between males and females grows larger as the sample moves from high school to college-going students, from college-going students to graduate schools students, and from graduate students to those who are most gifted in math and science among graduate students. As this sample becomes more selective, so does the demand for visuospatial mental representation and transformation, which may be the underlying factor in this cognitive performance differential between males and females.
  • More variable in cognitive performance. There are more males at both the high and low ends of many cognitive performance distributions. The greater variability for males means that there are more males with mental deficiencies, and there are more males that score at the very high end on many tests of intelligence and achievement. The SAT-M, the mathematics test administered by the Educational Testing Service that is used by many universities for college admissions is one of the tests that shows an excess of males on the extreme high end. The quantitative test of the Graduate Record Examination (GRE-Q), which is used for admissions for graduate school also has a greater proportion of males scoring at its highest end (Webb, Lubinski, & Benbow, 2002).
FIGURE 2-7. An example of a mental rotation task.

FIGURE 2-7

An example of a mental rotation task. Can the pairs of figures in A and B be rotated so that they are identical? Reaction times and correct answers are recorded.

Distribution of Aptitude

Several researchers have argued that the excess of males at the very high end of the abilities distributions for mathematics can account for the underrepresentation of females in physical sciences and math careers. When Summers referred to the different availability of aptitude at the high end, he was referring the finding that the ratio of males to females in the tails of distributions such as the GRE-Q is very high and gets higher the farther out in the tail that the distribution is cut, so that at the top 1% or 0.5 % there are many more males than females. There are flaws in this line of reasoning as an explanation of the underrepresentation of women in science and math academic careers because there is a lack of females at all ability ranges in science and math, not just at the highest ability range (Halpern, in press). There are many males in science and math who are not in the highest ability ranges because, by definition, only a very small percentage of the population is in this range. In other words, it is not as if we have only mediocre women in sciences and math with a lack at the top—women are underrepresented across the board.

Although the relative scarcity of females in the extreme tails of distributions cannot explain the absence of females in science and math careers overall, a surprising finding showed that for the very highest scoring SAT-M students at age 13, having a “genius” level score made a difference in their own career choices and achievements 20 years later (Wai, Lubinski, and Benbow, 2005). Researchers found that among precocious youth, there were differences in career choices and achievements 20 years later between those youth who scored in the top quartile of the top 1% on the SAT-M and those who scored in the bottom quartile of the top 1% on the SAT-M. Most psychologists would have believed, and probably still believe, that if an individual has achieved a threshold level of ability, additional ability beyond that level has little or no effect on life success because other variables such as motivation, interest, and opportunity would be far more important. These results remind researchers that high level ability is an important determinant of life outcomes, assuming that people have the opportunities to develop their abilities.

In looking over this abbreviated list of areas in which there are cognitive sex differences, one point should be evident—everyone except the profoundly retarded can improve in these cognitive areas with appropriate education, which is why we have schools. We really do not know if we could close, reverse, or increase any or all of the average differences between males and females with learning experiences, “selective breeding” (which was not discussed), hormone manipulations, or with combinations of all of these.

International Comparisons

Some differences between females and males are found consistently in international assessments. International comparisons of males and females are shown in Figure 2-8. The left hand column shows data from 15 year-old students from 25 countries who participated in the Program for International Assessment (PISA). As seen in this figure, all of these countries showed significantly different effects favoring girls in reading literacy. The mathematics achievement and science achievement data are taken from the Third International Math and Science Study (U.S. Department of Education, 1997). The sex differences in math achievement at 8th grade are not as impressive on this assessment as it is on more advanced measures, but as indicated earlier, the size of the sex difference depends on what is assessed and it grows with more select samples. The cross-national consistency of the science achievement data is striking. In looking over these data, it is apparent that the results all show that males performed better than females and that the differences are statistically significant.

FIGURE 2-8. Gender differences in achievement: 15 year old* and 8th grade students.

FIGURE 2-8

Gender differences in achievement: 15 year old* and 8th grade students.

Readers may be wondering whether these effects are large enough to be important or meaningful in “real world” contexts. The question of when an effect is large enough to be meaningful has been the subject of much debate. In Valian’s (1998) analysis of women’s slow advancement in academia and other professions, she showed how small disparities can be compounded over time to create larger disparities, so a seemingly “small” percentage of variance accounted for can be meaningful, depending on the context and variable being assessed. Rosenthal, Rosnow, and Rubin (2000, pp. 15-16), three leading statisticians weigh in on this critical matter: “Mechanically labeling … ds automatically as ‘small,’ ‘medium,’ ‘and ‘large’ can lead to later difficulties. The reason is that even ‘small’ effects can turn out to be practically important.”

In a research paper on the mental rotation test, Peters et al. (1995) report that sex accounted for only 18% of the variance, but when they calculated a Binomial Effect Size Display (BESD), they found that 15% of the females exceed the mean of the males on this test. If the mean value of the male distribution were selected as the cut point for selection for an engineering program or some similar program, 50% of men would be admitted and 15% of women would be admitted, so even a seemingly “small” percentage of variance would have a devastating effect on the number of women admitted to this hypothetical program for further training.

Grades-Tests Disparities

Although females, in general, are doing better in school than their male counterparts (boys are more likely to repeat a grade, be victimized in school, or show up for school unprepared; U.S. Department of Education, 2000), males do better, in general, on standardized tests that are not linked to any specific curriculum, such as the SATs and GREs, which are used for college and graduate school admissions. The grades-tests disparity implies that the SAT-V (verbal) and SAT-M under-predict women’s grades in college, which is empirically supported (Cullen, Hardison, and Sackett, 2004). One explanation of the underprediction of women’s grades by tests that are not linked to the curriculum is that women are better students. Class grades also include classroom behavior and other noncognitive variables that are part of the good student role—a social role that is more compatible with the female sex role than the male sex role.

Average scores on the SAT-M for entering college classes from 1967 to 2004 are shown for men and women are shown in Figure 2-9. Despite the huge changes in number of women enrolled in mathematics courses and their higher grades in mathematics courses, the male advantage on this test has remained fairly constant over the last 36 years.

FIGURE 2-9. Average SAT scores of entering college classes, 1967-2004.

FIGURE 2-9

Average SAT scores of entering college classes, 1967-2004. SOURCE: The College Board (2004). Table 2: Average SAT scores of entering college classes, 1967-2004. Date retrieved June 15, 2005, from http://www.collegeboard.com/prod_downloads/about/ness_info/cbsenior/yr2004/links.html. (more...)

Cognitive Process Taxonomy

How can we understand the grade-tests disparity? One way to consider the underlying cognitive processes used in executing the cognitive tasks being assessed when females or males excel at a cognitive task. Using a basic framework that was derived from the empirical literature on sex differences, Halpern (2000) proposed that females, in general, have faster access to information in episodic memory, to word knowledge and phonetic information; greater language fluency and implicit use of grammatical rules (in writing). Males, in general have faster access to visuospatial information and more accurate transformations of visuospatial information. In a study of the strategies used to solve mathematical problems, Gallagher et al. (2000) used the framework proposed by Halpern to see if boys and girls differed systematically in their use of mathematical strategies for different types of problems. In a series of several studies, they found that overall, the male students were more likely to use a flexible set of general strategies and more likely to solve problems that required a spatial representation, a short cut, or the maintenance of information in spatial working memory. Females were more likely to correctly solve problems with context that was familiar for females, used verbal skills, or required retrieval of a known solution or algebraic or multi-step solution.

Building on the cognitive processing model, Gallagher, Levin and Cahalan (2002) examined cognitive patterns of sex differences on math problems on the Graduate Record Examination (GRE). They found the same results as predicted from the processes involved in solving the specific math problems, with differences favoring males for problems where there was an advantage to using a spatially-based solution strategy (use of a spatial representation), but not when solution strategies were more verbal in nature or similar to the ones presented in popular math textbooks. Similarly, the usual male advantage was found with math problems that had multiple possible solution paths, but not on problems that had multiple steps, so the differences in the performance of males and females on GRE math problems lie in the recognition and/or selection of a solution strategy that may be novel and not in the load on working memory. They found that the usual male advantage on standardized math tests can be minimized, equated, or maximized by altering the way problems are presented and the type of cognitive processes that are optimal for their solution.

These are important findings because they advance our understanding of problem solving in general and math problem solving for all learners. These findings also suggest ways to help everyone improve at what is often the “funnel”— or sieve—in education. Everyone can be taught how to create spatial representations and how to use successful strategies when they are appropriate for a specific type of mathematical problem. This is one example where the study of sex differences can move us toward a better understanding of the cognitive processes people use and new ways to improve strategies for math problem solving.

Noncognitive Variables

There are many context variables that influence cognitive performance. The president of Harvard, Lawrence Summers (2005, January 14) offered a “high-powered job” hypothesis as one possible reason for the low participation rate of women at the full professor level in the sciences and math that considers the larger context of higher education. There are few women full professorships in any discipline at research universities—they are underrepresented in all disciplines. Higher education is one of the few places that has an early “up or out” system. Law and accounting firms that require early partnership are the only other comparable models where young talented employees must prove themselves in the first six or seven years of their careers. For a scientist, who will usually have a postdoc position after receiving a doctorate, tenure decisions will be made around age 36, which means that tenure clocks run in the same time zone as biological clocks. A recent study found that early babies—before tenure—hurt women’s careers in academe, but help men’s (Mason and Goulden, 2004). Women who want a career in academic science will have to make greater sacrifices than men, because in general, women have greater care responsibilities than men do. The inflexibility of the tenure system to accommodate to the reality of women’s lives is the more likely and proximal cause of the underrepresentation of women in academic science, which in addition to the other requirements in the academy, includes long hours in the laboratory.

Thus, although there are sex differences in cognitive performance on many tests, and despite the many unanswered and important questions about the interplay of social, assessment, and biological variables on cognitive performance, the most immediate route to helping talented women gain entry and move through career in science and mathematics is by recognizing the family and other care-taking demands that most usually fall on women. Many talented women resent the choice between children and career that society is not asking of their male peers. Egalitarian households would go a long way to achieving workplace equity, but until we achieve that reality, part-time tenure track appointments without retaliation and other family-compatible options for men and women will be needed so that the nation can take advantage of the talent in the new workforce.

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Footnotes

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Paper presented at the National Academies Convocation on Maximizing the Success of Women in Science and Engineering: Biological, Social, and Organizational Components of Success, held December 9, 2005 in Washington, DC.

Some authors prefer to use the term “gender” when referring to female and male differences that are social in origin and “sex” when referring to differences that are biological in origin. In keeping with the biopsychsocial model that is advocated in this paper and the belief that these two types of influences are interdependent and cannot be separated, only one term is used in this chapter. “Sex” is used without reference to the origin of any observed differences or similarities and is not meant to imply a preference for biological explanations. These terms are often used inconsistently in the literature.

Copyright © 2006, National Academy of Sciences.
Bookshelf ID: NBK23780

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