The Psychology of Sex and Gender - Jennifer Katherine Bosson, Joseph Alan Vandello, Camille E. Buckner 2022
Cognitive Abilities and Aptitudes
Cognition, Emotion, and Communication
Gender researchers study why women are underrepresented in STEM fields relative to men.
Source: © iStockphoto.com/poba
Test Your Knowledge: True or False?
· 7.1 Most sex differences in cognitive abilities map directly onto sex differences in specific brain structures.
· 7.2 Girls and boys do not differ significantly in average levels of general mental ability (intelligence).
· 7.3 Countries with greater gender equality show smaller sex differences in verbal abilities.
· 7.4 Reminding women about negative stereotypes about women’s math abilities can lower their math test performance.
· 7.5 Women’s underrepresentation in careers in science, technology, engineering, and math (STEM) is due, at least to some degree, to lifestyle choices and demands placed on them to rear children and manage homes.
What Is Cognitive Ability?
· Journey of Research: Measuring the Brain From Phrenology to fMRI
· Sex Differences in General Mental Ability
What Are the Sex Differences and Similarities in Cognitive Abilities?
· Verbal Performance
o Vocabulary and Verbal Fluency
o Reading and Writing
o Verbal Reasoning
· Quantitative Performance
· Visual-Spatial Performance
o Mental Rotation
o Spatial Perception and Visualization
o Spatial Location Memory
· Sex Differences in the Variability of Cognitive Abilities
How Do Individual Differences and Context Influence Cognitive Performance?
· Culture, Race, and Educational Access
· Math Anxiety
· Achievement Motivation and Sensitivity to Feedback
How Do Sex and Gender Relate to Outcomes in School and STEM Fields?
· Education and School Performance
o Cultural Influences
o Home and Classroom Dynamics
· Debate: Do Children Fare Better in Single-Sex Classrooms?
· Sex, Gender, and STEM Fields
o Interests, Values, and Expectations
o Gendered Family Responsibilities
Students who read this chapter should be able to do the following:
· 7.1 Explain the historical origins of research on sex differences in cognitive abilities.
· 7.2 Analyze the specific domains of cognitive performance that show sex similarities and differences.
· 7.3 Evaluate contextual and individual difference factors that can influence cognitive performance.
· 7.4 Apply research on gender and cognitive performance to real-world issues, such as gender disparities in educational systems, school performance, and STEM disciplines.
COGNITIVE ABILITIES AND APTITUDES
Of all the topics studied by psychologists interested in gender, sex differences in cognitive abilities are among the most controversial. For evidence of the strong passions that this topic ignites, consider the following cases.
In 2005, Harvard University hosted a conference, attended by some of the world’s most eminent scientists, to discuss the underrepresentation of women in the sciences and engineering. The conference organizers invited Lawrence Summers, then president of Harvard, to make some remarks. They encouraged him to be provocative, and he was. Summers’s remarks offended many of the conference attendees and drew substantial public criticism (S. Dillon, 2005). What did he say to generate such controversy? Summers (2005) suggested three potential explanations to account for women’s relatively low numbers in science and engineering: the intense work demands of these fields, the greater natural aptitude of men at the highest levels of math and science, and sex-based patterns of socialization and discrimination. After emphasizing his first two explanations, Summers then discounted the role of discrimination.
The reaction was intense, with some supporting Summers and others denouncing him. Nancy Hopkins, a prominent MIT biologist, walked out of the talk, later stating, “When he started talking about innate differences in aptitude between men and women, I just couldn’t breathe because this kind of bias makes me physically ill. Let’s not forget that people used to say that women couldn’t drive an automobile” (S. Dillon, 2005). Denice Denton, former chancellor of the University of California, Santa Cruz, criticized Summers for making assertions that could easily be refuted by examining the latest scholarship in the field—scholarship that was, in fact, the very focus of the conference where Summers made his remarks (Bartindale, 2005). Summers later issued a written apology to the Harvard faculty and community, stating that he wrongly oversimplified a very complex matter and should have put more emphasis on the roles of socialization and discrimination. Nonetheless, the damage was done. Summers resigned from Harvard the next year.
In 2006, Aarhus University in Denmark temporarily suspended psychologist Helmuth Nyborg after he published a paper in a reputable journal that showed a significant sex difference in general intelligence favoring men (Nyborg, 2005). Despite the paper passing through the peer review process for publication, it attracted enough controversy to prompt a university investigation. The investigation committee found Nyborg innocent of fraud but guilty of “grossly negligent behavior.” The university later revoked Nyborg’s suspension but gave him a severe reprimand.
In 2017, the Mathematical Intelligencer accepted for publication, then quickly retracted, a paper describing a mathematical model that explains how men might have evolved greater variability than women in intelligence (and other traits). The editor of the journal pulled the paper after several researchers sent her emails expressing outrage, and she worried that publishing it would be interpreted as backing a sexist agenda (Azvolinsky, 2018).
Cognitive abilities Mental skills, such as paying attention, reasoning, remembering, solving problems, speaking, and interpreting speech.
In this chapter, we critically examine the controversial, politically charged topic of sex differences in cognitive abilities, which are mental skills such as paying attention, reasoning, remembering, solving problems, speaking, and interpreting speech. The stakes are high, in part because of how some kinds of cognitive abilities link to occupations with high societal value, such as those in fields of science, technology, engineering, and math (STEM). What does research on sex differences in cognitive abilities show? A fair degree of inconsistency. While some specific types of cognitive abilities show moderate sex differences, sometimes favoring women and other times favoring men, other cognitive abilities show small or nonexistent sex differences. Further, while the average performance of adolescent boys on standardized math tests in the United States slightly exceeds that of adolescent girls, sex differences on these tests do not emerge until high school (Lindberg, Hyde, Petersen, & Linn, 2010), and girls consistently outperform boys when it comes to school math grades (Kenney-Benson, Pomerantz, Ryan, & Patrick, 2006). This chapter attempts to shed light on these complicated issues by examining three related questions. First, what are the sex differences and similarities in cognitive abilities, and why do these patterns emerge? Second, what accounts for sex disparities in educational systems and careers, particularly those involving STEM? Third, what are the limitations of making binary, male—female comparisons of cognitive abilities?
In addressing sex differences in cognitive domains, we consider the roles of three factors: (1) biology, (2) discrimination, and (3) variables other than cognitive ability, such as preferences, expectations, confidence, or competitiveness. Note that these factors map roughly onto the three explanations for the underrepresentation of women in STEM fields that Lawrence Summers offered in his controversial talk. They also run the gamut from nature to nurture: Whereas assumptions about sex differences as biological or innate imply nature as the primary cause, explanations involving discrimination or other social factors imply nurture as the primary cause. In addition, viewing cognitive sex differences as rooted in varying preferences, expectations, confidence, and competitiveness implies a combination of nature and nurture. This may remind you of the theme of Chapter 3 (“The Nature and Nurture of Sex and Gender”), the notion that nature and nurture often cannot easily be separated. Before we discuss explanations for sex differences in cognitive abilities, we begin with some definitions and background.
Despite facing discrimination within the field, women have excelled in math throughout history. Maryam Mirzakhani broke significant barriers in 2014 by becoming the first woman to win the Fields Medal, considered to be the Nobel Prize of mathematics. Tragically, she died of breast cancer in 2017 at the age of 40.
Source: Associated Press
Intelligence (or general mental ability) The general capacity to understand ideas, think abstractly, reason, solve problems, and learn.
WHAT IS COGNITIVE ABILITY?
Research on the origins of sex differences in cognitive ability has roots in the scientific study of intelligence (or general mental ability), which is the general capacity to understand ideas, think abstractly, reason, solve problems, and learn. Note that intelligence is a broad capacity to process information and engage in mental activities rather than a specific type of knowledge or expertise, as reflected in this description:
Intelligence is a very general mental capability that … involves the ability to reason, plan, solve problems, think abstractly, comprehend complex ideas, learn quickly, and learn from experience. It is not merely book learning, a narrow academic skill, or test-taking smarts. Rather it reflects a broader and deeper capability for comprehending our surroundings. (Gottfredson, 1997, p. 13)
The study of sex differences in intelligence dates back to at least the early 19th century. As you read in Chapter 2 (“Studying Sex and Gender”), women in the 19th century were commonly believed to be less intelligent than men. For instance, Charles Darwin (1871), otherwise revolutionary in his thinking, argued that “the chief distinction in the intellectual powers of the two sexes is shown by man attaining to a higher eminence, in whatever he takes up, than woman can attain—whether requiring deep thought, reason, or imagination, or merely the use of the senses and hands” (p. 327).
With the birth of scientific psychology toward the end of the 19th century, some scholars attempted to build a scientific case to justify women’s inferior position in society (Shields, 1975). For instance, researchers measured the shape, mass, and volume of male and female brains, arguing that women’s smaller brains explained their intellectual inferiority (Fine, 2010). These methods, however, were rooted in questionable assumptions, as you will read about in “Journey of Research: Measuring the Brain From Phrenology to fMRI.” Also, as you may recall from Chapter 3, sex differences in brain structure (such as shape and mass) do not map cleanly onto sex differences in brain function (such as processes and capacities). Gender biases in brain measurement studies led psychologist Helen Thompson Woolley, a pioneer of gender research, to conclude in 1910 that “there is perhaps no field aspiring to be scientific where flagrant personal bias, logic martyred in the cause of supporting a prejudice, unfounded assertions, and even sentimental rot and drivel, have run riot to such an extent as [the psychology of sex differences]” (p. 340). Not surprisingly, the study of sex differences in cognitive abilities still arouses strong opinions and skepticism today, given this long history of bias in the field.
STOP AND THINK
Science, as a systematic method for asking and answering questions about the world, requires that researchers strive for objectivity and transparency. Given this, what do you make of the gender bias displayed by prominent scientists such as Charles Darwin? What implications does this bias have for the credibility of science in general and scientific findings in the long run? Does science have ways of correcting itself when human bias creates distortions? If so, how?
JOURNEY OF RESEARCH: MEASURING THE BRAIN FROM PHRENOLOGY TO FMRI
Do the size and shape of brains tell us anything about intelligence? As we discussed in Chapter 3 (“The Nature and Nurture of Sex and Gender”), researchers in the field of neuroscience study the structure and function of the brain in order to understand mental processes and behaviors. Although our understanding of the links between brain structure and brain function is still incomplete, the study of the brain spans over 200 years, and the tools and theories used to understand the brain have become increasingly sophisticated.
Early methods of studying the brain were crude. In 1796, Franz Joseph Gall introduced a technique he called cranioscopy, or phrenology, which measured the size and shape of bumps on the surface of the skull to infer various intellectual and psychological attributes. Phrenology was often used to reinforce gender roles and stereotypes—as well as race- and class-based biases—because certain aspects of women’s head shape and size (such as their smaller and lower foreheads than men’s) were interpreted to reflect women’s capacity for “love of children, perception, friendship, benevolence, circumspection, idealism, and secretiveness” (Staum, 2003, p. 64). At the same time, women’s lower foreheads indicated a lack of skill in science and arts and an unsuitability for public speaking and teaching at universities. Although popular in the first half of the 19th century, phrenology was eventually dismissed as pseudoscience, or beliefs and practices that are presented as scientific despite lacking a factual basis and proper scientific scrutiny (Ioannidis, 2012).
In another early method, researchers estimated the size of male and female brains by measuring head circumference or brain weight and volume postmortem. These methods proved unreliable, with questionable connections to intelligence (Fine, 2010), but scientists used them to claim women’s intellectual inferiority. When theories at the time identified the frontal cortex as the brain region responsible for intelligence (it isn’t), scientists began reporting that men had larger frontal lobes than women. When the evidence revealed no actual sex differences in frontal cortex size, the focus shifted to the parietal lobes as the seat of intelligence, and scientists began reporting evidence for men’s superior parietal lobes (Shields, 1975). In short, the strong belief in men’s intellectual superiority seemed to drive the research methods and findings. This stands in contrast to how science should proceed, and it underscores the importance of critically evaluating research, as discussed in Chapter 2 (“Studying Sex and Gender”).
A phrenology diagram illustrating the regions on the skull that presumably mapped onto specific locations in the brain where distinct skills and traits were regulated.
Source: © iStockphoto.com/cjp
Researchers’ fascination with measuring brain structures and activity continues today. Imaging techniques now allow accurate estimates not just of total brain size but of the size of specific brain regions (Cahill, 2014), percentages of gray and white matter (Taki et al., 2011), length of dendrites (neuronal structures that receive information from other neurons; Griffin & Flanagan-Cato, 2012), and degree of interconnectivity between the cerebral hemispheres (Ingalhalikar et al., 2014). One common brain imaging technique, functional magnetic resonance imaging (fMRI), allows researchers to measure brain activity and depict where such activity occurs within specific brain regions. Using such methods, some researchers find sex differences in size and density in certain brain regions (Ruigrok et al., 2014), while others note a great deal of overlap in female and male brains (Joel et al., 2015). And even when sex differences are found, it is difficult to link them directly to sex differences in cognitive abilities. Thus, despite their sophistication, neuroscience methods remain fraught with the problem of dualism that has plagued thinkers since the 18th century: How do the physical structures of the brain relate to the cognitive processes of the mind?
As the tools of neuroscience advance, the discovery of new sex differences in the brain should invite critical reflection about the meaning and importance of these differences. Recall that, due to neuroplasticity, sex differences in the brain likely emerge through a complex interaction of nature and nurture, as gendered environments shape newborn brains. As past research shows, neuroscience findings can be used to sustain false notions of essentialism, the belief that human differences arise from stable and integral (usually biological) qualities within individuals (Bluhm, 2013a). We encourage you to keep these ideas in mind when you encounter research that shows sex differences in the brain.
By the turn of the 20th century, scientists moved beyond simply measuring the brain to developing tests that measure intelligence. Alfred Binet and Theodore Simon developed the first modern intelligence test in France in the early 1900s (Binet & Simon, 1908), which led other researchers to popularize the concept of the intelligence quotient (IQ). The IQ is a standardized score that represents an individual’s level of intelligence relative to his or her same-age peers. Tests of IQ shifted consensus on the supposed superiority of male intelligence when Lewis Terman, a pioneer of early IQ testing in the United States, demonstrated that boys and girls did not differ in intelligence scores (Terman, 1916).
Phrenology The discredited study of how the size and shape of the cranium (skull) relates to mental abilities and personal attributes.
The history of intelligence testing in the United States has an ugly side, especially in its links to the eugenics movement in the late 19th and early 20th centuries. In their aim to control the quality of the human gene pool, eugenicists encouraged the reproduction of those deemed genetically “superior” and actively prevented the reproduction of those deemed genetically “inferior,” including immigrants, people of color, low-income individuals, or people with physical or intellectual disabilities (Ko, 2016). In the early 20th century, American psychologist and eugenicist Henry Goddard misused IQ tests to identify individuals he considered to be mentally deficient and advocated for their forced sterilization (permanent medical procedures that prevent reproduction; Kline, 2014). Although these human rights abuses have decreased steadily over time, evidence of the forced sterilizations of female inmates in California emerged as recently as 2010 (Stern, 2016). Such abuses continue to fuel the reproductive justice movement, which advocates for the rights of all women, particularly those in marginalized communities, to make their own reproductive health decisions (as you read about in Chapter 1).
Pseudoscience Beliefs and practices that are presented as scientific despite lacking a factual basis and proper scientific scrutiny.
Dendrites Branch-like structures of neurons that receive neural messages from other neurons.
Essentialism The belief that human differences arise from stable and integral (usually biological) qualities within individuals.
Intelligence quotient (IQ) A score representing an individual’s level of intelligence, as measured by a standardized intelligence test. IQ is calculated such that the average for an individual’s same-age peers is always set to 100.
Eugenics A movement whose members seek to control the genetic quality of the human population by preventing the reproduction of those deemed genetically inferior.
Sex Differences in General Mental Ability
Psychologists have long debated whether intelligence consists of a single, general factor or a number of different factors. Many now agree that intelligence consists of several separate yet correlated components that all feed into a superordinate general intelligence (N. Brody, 1992). Given this distinction, we will first examine sex differences in general intelligence and then turn to sex differences in distinct cognitive abilities. While this has been a robust area of research in psychology, we offer a word of caution about the limitations of making binary comparisons of women and men on any dimension. As you read in Chapter 2 (“Studying Sex and Gender”), binary female—male comparisons overlook individuals who identify as neither female nor male (or both female and male), and they also overlook within-sex differences in race, class, age, physical ability, sexual orientation, religion, and culture. We encourage you to keep this in mind and read this section and the next one with a critical eye.
Researchers have been measuring general mental ability for over 100 years, dating back to Charles Spearman. Spearman (1904) argued that individuals possessed a general mental ability that related to their performance on all cognitive tasks and was identifiable through a statistical procedure called factor analysis, which identifies clusters of related scores. Due to the generality of mental ability, a person with good reading comprehension skills, for example, should also demonstrate good working memory and strong pattern recognition. Consistent with this assumption, general mental ability does predict important outcomes, such as academic performance (Deary, Strand, Smith, & Fernandes, 2007), job performance (Schmidt & Hunter, 2004), and even health and longevity (Calvin et al., 2017). It also appears to be stable over the lifespan (Deary, Pattie, & Starr, 2013) and genetically heritable (Plomin & von Stumm, 2018), though environmental factors also play an important role. For instance, children adopted from working-class into middle-class homes exhibit IQs that are 12—18 points higher than their siblings raised by their working-class birth parents (Nisbett et al., 2012).
Are there sex differences in general mental ability? Recall the controversial paper by Helmuth Nyborg mentioned in the chapter opening. Although Nyborg (2005) found a significant IQ advantage favoring men, and a few other researchers find a modest male advantage (Lynn & Kanazawa, 2011), most studies find negligible sex differences in scores on measures of general mental ability (Buczyłowska, Ronninger, Melzer, & Petermann, 2019; Colom, Juan-Espinosa, Abad, & García, 2000; Savage-McGlynn, 2012). Overall, the bulk of research suggests that sex differences in general mental ability are small and do not consistently favor one particular sex. However, looking for sex differences in general mental ability presents a challenge because most standardized intelligence tests are intentionally constructed to show no sex difference in overall scores (Neisser et al., 1996). That is, items that consistently produce sex differences are replaced with items that do not produce differences. Gender neutral by design, these tests thus cannot be used reliably to examine sex differences in mental ability.
Factor analysis A statistical procedure used to identify clusters of related scores or items.
STOP AND THINK
Just as researchers design standardized intelligence tests to be gender neutral, they take a similar approach with items that produce ethnic, racial, or cultural differences in responses. Why do you think test designers intentionally strive to create gender-, race-, and culture-neutral tests? What is the logic behind this strategy? How does it relate to the validity of intelligence tests?
SIDEBAR 7.1 IN HOLLYWOOD, BRILLIANCE = (WHITE?) MEN
Picture a movie featuring a brilliant scientist. What image comes to mind? When Gálvez, Tiffenberg, and Altszyler (2019) analyzed over 11,000 top-grossing films from the past 50 years, they found evidence that movies perpetuate a stereotype that brilliance is associated with men. Gálvez and colleagues searched a huge database of movie transcripts, and they found an association between male pronouns and high-level cognitive ability words such as “brilliant,” “genius,” “clever,” and “intelligent.” In addition, movies from the past decade were just as likely to portray men as brilliant as films from 50 years ago. Although the researchers did not directly examine race, given that characters in top-earning films have historically been overwhelmingly White (Smith, Choueiti, Pieper, Case, & Choi, 2018), we can infer that these depictions of brilliance largely involve White men.
Portrayals of brilliance in Hollywood films from the past 50 years largely feature White male characters.
Source: Photo 12 / Alamy Stock Photo United Archives GmbH / Alamy Stock Photo
WHAT ARE THE SEX DIFFERENCES AND SIMILARITIES IN COGNITIVE ABILITIES?
While the evidence does not consistently support sex differences in overall intelligence, some sex differences do emerge in research on more specialized cognitive abilities. These specific cognitive abilities are often grouped into verbal, quantitative, and spatial abilities, dating back to the groundbreaking work of Thurstone and Thurstone (1941). The Thurstones gave 60 different intelligence tests to a sample of eighth graders; using factor analysis, the researchers found that the test items grouped into these three clusters of abilities. In the mid-1970s, Maccoby and Jacklin (1974) published a comprehensive and influential review of research on sex differences and concluded that the sexes indeed differed in verbal, quantitative, and spatial abilities. This conclusion quickly became the dominant understanding in the field.
By the 1980s and 1990s, however, a more complex picture began to emerge. Janet Hyde and her colleagues conducted several large meta-analyses to update Maccoby and Jacklin’s (1974) findings (Hyde, Fennema, & Lamon, 1990; Hyde & Linn, 1988). These and more recent studies reveal a fairly consistent pattern of sex differences, though the size of the difference often depends on factors such as the type of cognitive skill measured, the publication date of the study, and the age or cultural background of the participants (Else-Quest, Hyde, & Linn, 2010). One large study of adults even revealed that health and health habits play a role in some cognitive sex differences. Specifically, Anthony Jorm and colleagues assessed various verbal, perceptual, and memory tasks and found that, after controlling for physical and mental health variables, sex differences disappeared in cases of male advantage and increased in cases of female advantage (Jorm, Anstey, Christensen, & Rodgers, 2004). That is, men’s better health on some dimensions, such as pulmonary functioning, depression, and exercise frequency, accounts for their advantages on some cognitive tests, but health variables do not account for female advantages on other cognitive tests.
Recall from Chapter 2 that researchers measure the size of sex differences by calculating d value effect sizes, or estimates of the average difference between groups expressed in standardized units. As a reminder, d values are interpreted as reflecting differences that range from close to zero to very large in magnitude, and Table 7.1 provides you with a refresher of how to interpret d values. Also recall that positive d values indicate effect sizes favoring boys and men, and negative d values indicate effect sizes favoring girls and women. Finally, note that we will first focus on sex differences in average cognitive performance, and then we will consider variability in performance.
The right column provides descriptive labels to accompany various ranges of d values (left column). This helps researchers interpret the magnitude of effects.
Source: Hyde (2005).
In their 1974 book, Maccoby and Jacklin concluded that research consistently showed a female advantage on verbal tasks. Does research conducted since then support this conclusion? In an early meta-analysis, Hyde and Linn (1988) found a very small female advantage in verbal skills (d = −0.11), and a later meta-analysis revealed only close-to-zero effect sizes in verbal abilities (Hedges & Nowell, 1995). However, verbal abilities are not a unitary construct, and the size of the difference depends on the area being measured (e.g., vocabulary, verbal fluency, reading, writing, and verbal reasoning). As shown in Table 7.2, when sex differences do emerge, they are small to moderate and tend to favor girls and women. Let’s take a closer look at sex differences in these specialized verbal domains.
Vocabulary and Verbal Fluency
On average, girls learn to talk younger and their vocabularies bloom earlier than boys’ do, although the effect sizes are small (Bornstein, Hahn, & Haynes, 2004). By later childhood, however, sex differences in vocabulary generally disappear (Wallentin, 2009). In contrast, tests of verbal fluency tend to show consistent sex differences. Verbal fluency is the ability to generate words, and tests of this ability require people to generate as many words as possible that belong to a certain category (e.g., “birds”) or that begin with a specific letter (e.g., M) in a short period of time (usually 1 minute). In one meta-analysis, Hyde and Linn (1988) reported a small sex difference favoring girls and women for verbal fluency (d = −0.33). More recent studies reveal similar effect sizes (d = −0.24 and −0.45; Weiss, Kemmler, Deisenhammer, Fleischhacker, & Delazer, 2003) and show that the female advantage in verbal fluency is present across ages and sexual orientations (Maylor et al., 2007).
Verbal fluency The ability to generate words.
Reading and Writing
Although the earliest meta-analysis of reading comprehension found no overall sex difference (d = −0.03; Hyde & Linn, 1988), more recent studies consistently find a reading advantage for girls. For example, Hedges and Nowell (1995) found an average effect size (d) of −0.18 across tests of reading comprehension in the United States, and a large-scale study of 15-year-olds from 40 countries around the globe found that girls scored higher on reading comprehension than boys in every country (Guiso, Monte, Sapienza, & Zingales, 2008). There are a couple of interesting points to note about this sex difference. First, in the United States, one study found that the size of the sex difference (d) grew from −0.19 in Grade 4 to −0.32 by Grade 12 (Reilly, Neumann, & Andrews, 2019). Second, around the world, the size of the sex difference in reading correlates with national indices of gender equality: In countries where girls have more educational and economic opportunities, they tend to outperform boys by the widest margins. For example, the largest sex differences in reading ability emerged in Finland (d = −0.64), which has the third-highest level of gender equality in the world (World Economic Forum, 2019). By way of comparison, the United States ranks 53rd in gender equality and shows a smaller female advantage in reading (d = −0.26). How do you interpret these patterns indicating that the size of sex differences in reading comprehension changes with children’s age and with national gender equality?
Sex differences in quantitative abilities tend to be close to zero or small; sex differences in verbal abilities tend to be small to moderate; and sex differences in visual-spatial abilities tend to be moderate. The size and direction of sex differences, however, differ across the specific cognitive ability of interest.
Source: a. Hyde and Linn (1988); b. Hedges and Nowell (1995); c. Weiss, Kemmler, Deisenhammer, Fleischhacker, and Delazer (2003); d. Reilly (2012); e. Reilly, Neumann, and Andrews (2019); f. Strand, Deary, and Smith (2006); g. Hyde, Lindberg, Linn, Ellis, and Williams (2008); h. Hyde, Fennema, and Lamon (1990); i. Voyer, Voyer, and Bryden (1995); j. Lippa, Collaer, and Peters (2010); k. Linn and Petersen (1985); l. Lauer, Yang, and Lourenco (2019)*; m. Voyer, Voyer, and Saint-Aubin (2017).
*Effect size reported as Hedges’ g, similar to d with large sample sizes.
In terms of writing ability, most research shows that girls again have an advantage, with Hedges and Nowell (1995) reporting a moderate effect size (d = −0.57). More recent evidence indicates that American girls outperform boys, on average, in tests of writing proficiency at Grades 4, 8, and 12 (Reilly et al., 2019; U.S. Department of Education, 2011). Thus, girls tend to outperform boys in reading and writing, but the sex difference is somewhat larger in writing.
Verbal reasoning, or the ability to understand and analyze concepts, offers an exception to the general trend toward a female advantage in verbal abilities. As an example of a test item that measures verbal reasoning, try to solve this analogy: Remembering is to forgetting as happy is to _____. Now consider this word problem: “If Bob is taller than Juan, and Juan is taller than Marcus, which person is the shortest of the three?” Instead of a female advantage on verbal reasoning tasks, studies find either a small male advantage (d = 0.15; Strand, Deary, & Smith, 2006) or no sex difference (Feingold, 1988). Some researchers argue that the typical female verbal advantage does not emerge on this type of task because verbal reasoning tasks often require people to transform verbal information mentally (Colom, Contreras, Arend, Leal, & Santacreu, 2004). In other words, to solve the problem about Bob, Juan, and Marcus, you have to visualize the separate elements and then compare across them—perhaps by imagining Bob towering over Juan and Juan towering over Marcus. In general, men tend to outperform women in tasks that use this sort of visual-spatial processing. (We will return to this in the section on “Visual-Spatial Performance.”)
Verbal reasoning The ability to understand and analyze concepts, often tested with analogies or word problems.
Summarizing across what we have just discussed, the female advantage in verbal abilities that was claimed by Maccoby and Jacklin (1974) must be qualified. As shown in Table 7.2, girls outperform boys on some tests of verbal ability but not others, and the sex differences are small to moderate in magnitude. The size of the sex difference also depends on the age of the sample and the culture from which the sample is drawn (Reilly, 2012; Wallentin, 2009).
Countries high in gender equality, such as Finland, show the largest reading advantages for girls compared with boys.
Source: © iStockphoto.com/gpointstudio
Janet Hyde and her colleagues conducted large meta-analyses of sex differences in math performance in 1990 and more recently in 2008 (see Table 7.2). Both meta-analyses summarized data from millions of respondents in the general population and yielded no sex differences in overall math performance or math skills (Hyde et al., 1990; Hyde, Lindberg, Linn, Ellis, & Williams, 2008). Another recent meta-analysis that included data from over 2 million people also showed little evidence of an overall sex difference (d = 0.05), and 64% of the effect sizes fell in the close-to-zero range (Lindberg et al., 2010). However, even though no sex differences emerge in overall math performance, small sex differences do appear in some specific math domains, just as we saw with verbal abilities. In their 1990 meta-analysis, Hyde and colleagues found a small female advantage for computational ability (d = −0.20) at ages 5—10 that disappeared by ages 15—18, and they also found a modest male advantage for complex math problem solving (d = 0.29 and 0.32) at ages 15—18 and 19—25 that was absent in the younger age groups. However, the male advantage in complex math problem solving was less evident in more recent meta-analyses, which found only close-to-zero (d = 0.07) and small (d = 0.16) effect sizes (Hyde et al., 2008; Lindberg et al., 2010).
Cross-culturally, there is evidence of variability across nations in the magnitude of sex differences in math performance. For example, a meta-analysis of the math test scores of nearly 500,000 adolescents from 69 countries found sex differences that ranged from a moderate advantage favoring girls (d = −0.42) in Bahrain to a moderate advantage favoring boys (d = 0.40) in Tunisia (Else-Quest et al., 2010). Halpern (2004) also found variability in the standardized math test performance of eighth graders across 30 countries. Though the overall pattern showed boys outperforming girls in most of the countries, statistically significant differences emerged in only seven countries.
Why do you think the relative math performance of girls and boys differs across cultures? Just as we saw with reading abilities, the gender equality of a culture may play a role. Specifically, as the gender equality of a given nation increases, the size of men’s advantage in math ability decreases (Guiso et al., 2008; Reilly, 2012), suggesting that sociocultural factors influence sex differences in quantitative performance. In countries in which girls’ educational opportunities equal those of boys and more women have careers in science, sex differences in math performance tend to disappear.
Overall, as summarized in Table 7.2, the research shows little difference in the average math performance of girls and boys. Small differences favoring boys sometimes emerge in complex math problem solving among older children (Lindberg et al., 2010), but these gaps in math performance tend to disappear in countries with greater gender equality (Reilly, 2012).
Visual-spatial abilities allow people to understand relationships between objects and navigate three-dimensional space. They include the abilities to rotate figures mentally, predict the trajectories that moving objects will follow, and remember the locations of objects. People use these skills any time they navigate through a three-dimensional world in a video game, building structures or fighting off enemies. As with verbal and math performance, visual-spatial performance is measured in a number of ways. In general, sex differences favoring boys and men are larger and more consistent in visual-spatial performance, particularly in mental rotation tasks (see Figure 7.1a), than in other cognitive domains. However, the size of the sex difference depends on the specific task used and the age of the target population (Voyer, Voyer, & Saint-Aubin, 2017). Sex differences in visual-spatial tasks may also be influenced by factors such as time pressure and prior experience with the task.
Visual-spatial abilities Cognitive skills that help individuals understand relationships between objects and navigate three-dimensional space.
Figure 7.1 Visual-Spatial Tasks
Source: (a) Shepard and Metzler (1971); (b) Linn and Petersen (1985); and (c) Chu and Kita (2011).
STOP AND THINK
Consider the finding that sex differences in cognitive performance correlate with national indices of gender equality. Why might greater gender equality predict smaller male advantages in math and larger female advantages in reading? What do these trends suggest about the extent to which nature and nurture contribute to gender differences in cognitive abilities?
Gender researchers show great interest in sex differences in mental rotation ability—the ability to rotate an object in one’s mind—in part because this skill is essential for success in prestigious, male-dominated occupations such as engineering and architecture. As summarized in Table 7.2, an early review of mental rotation tasks revealed a large sex difference favoring boys and men (d = 0.73; Linn & Petersen, 1985), although this difference has decreased somewhat over time (d = 0.39 and 0.47; Lauer, Yang, & Lourenco, 2019; Lippa, Collaer, & Peters, 2010). The male advantage in mental rotation appears in infancy (P. C. Quinn & Liben, 2014) and emerges consistently across cultures. For instance, the moderate effect size in mental rotation that we just mentioned (d = 0.47) derived from a study of over 200,000 participants in 53 countries (Lippa et al., 2010). Men exceeded women in mental rotation in every country, with larger sex differences in countries with greater gender equality and more economic development. Notably, though, simple experimental manipulations can reduce this sex difference. For example, activating feelings of power by having students sit at a professor’s desk improved Israeli women’s performance on a mental rotation task (Nissan, Shapira, & Liberman, 2015). Similarly, activating stereotypes about men prior to the mental rotation task erased the sex difference in mental rotation (d = 0.01) among Austrian college students, compared with activating stereotypes about women (d = 0.59; Ortner & Sieverding, 2008).
Mental rotation ability The ability to rotate an object in one’s mind.
Spatial Perception and Visualization
How well can you identify a true horizontal water level line in a tilted glass? This ability is an example of spatial perception, which is the ability to perceive, understand, and remember spatial relations between objects. In spatial perception tasks (see Figure 7.1b), there tends to be a small male advantage in childhood (d = 0.33) that increases to moderate (d = 0.48) in adulthood (Voyer et al., 1995). Boys and men also tend to perform better on tasks of movement perception, such as judging velocity (Law, Pellegrino, & Hunt, 1993) or estimating when a moving target will reach a certain point (Schiff & Oldak, 1990). Similarly, boys and men tend to outperform girls and women on spatial navigation tasks (e.g., finding one’s way through a maze; Sneider et al., 2015). Spatial visualization, another type of spatial skill, is the ability to mentally manipulate spatial information sequentially, such as imagining what a folded shape will look like when unfolded (see Figure 7.1c). Spatial visualization tasks show small average male advantages (d < 0.20), and these do not emerge until the teenage years (Linn & Petersen, 1985; Voyer et al., 1995).
Spatial relations The ability to perceive, understand, and remember relations between objects in three-dimensional space.
Spatial visualization The ability to represent and manipulate two- and three-dimensional objects mentally.
Spatial location memory The ability to remember the location of objects in physical space.
Spatial Location Memory
At least one type of spatial task shows a reverse of the typical male advantage: Women tend to have better spatial location memory than men do, which means that women are better than men at remembering where objects are, although the sex difference is small (d = −0.34; Voyer et al., 2017) and inconsistent across studies (C. M. Jones & Healy, 2006). An evolutionary explanation for the female advantage in spatial location links it to sex-based labor divisions in which ancestral women more often foraged for fruits, vegetables, and roots over large geographic regions (New, Krasnow, Truxaw, & Gaulin, 2007).
What might account for the overall tendency for boys and men to display advantages in visual-spatial skills? Some suggest that play preferences in childhood contribute to boys’ enhanced abilities. Boys, as compared with girls, tend to play more games that involve hand—eye coordination, such as throwing and catching balls (Cherney & London, 2006), and they immerse themselves more often in video games that require them to manipulate and navigate three-dimensional worlds (Terlecki & Newcombe, 2005). Demonstrating that life experience shapes abilities in these sorts of tasks, a meta-analysis including over 200 research studies revealed that spatial skills training improved spatial performance for both male and female participants (Uttal et al., 2013). It makes sense that people who get more training with video games on a regular basis will score higher on measures of visual and spatial abilities, regardless of sex. Recall, however, that the male advantage in mental rotation appears in infancy, before children gain experience with video games. Thus, play preferences may contribute to and enlarge sex differences in visual-spatial skills, but they likely cannot fully explain these sex differences.
The aforementioned studies suggest that nurture (the different life experiences of girls and boys) contributes to sex differences in spatial abilities, but what about nature? Some preliminary evidence suggests that prenatal exposure to certain hormones and hormone levels can influence the brain in ways that can later shape cognitive abilities. For instance, one study found that female fetuses exposed to unusually high levels of androgens (masculinizing hormones) later exhibited better spatial performance and mental rotation ability than those exposed to normal levels of androgens (Berenbaum, Bryk, & Beltz, 2012). This finding should be interpreted with caution, however, because not all studies find effects of prenatal hormone exposure on later cognitive performance (Valla & Ceci, 2011).
In summary, some spatial abilities, such as mental rotation, show moderate to large sex differences favoring boys and men, while others, such as spatial location memory, show small to moderate sex differences favoring girls and women (see Table 7.2). It is likely that biological factors contribute to these sex differences, given the cross-cultural consistency and early appearance of these differences in development. However, some evidence suggests that environmental experiences, such as prior experience and learning, can also influence spatial abilities.
Playing action video games can improve spatial attention and mental rotation ability for both girls and boys.
Source: © iStockphoto.com/Chris Ryan
STOP AND THINK
You have just read about the various outcomes of research on sex differences in verbal, quantitative, and visual-spatial abilities. How could understanding the outcomes of this research be useful? What are some of the limitations of this research? Specifically, how does the almost exclusive focus on binary sex (male—female) comparisons limit our understanding of individual differences in cognitive abilities? Why might factors other than sex, such as race, ethnicity, class, educational opportunities, and culture, be important to consider as well?
Sex Differences in the Variability of Cognitive Abilities
Thus far, we have focused on average sex differences in cognitive performance. By shifting the focus to variability, we can examine outliers—that is, people at the extreme ends of distributions. Two groups that show no average difference in ability can still differ in the concentrations of people who score very high (or low) on that ability. Consider Figure 7.2, which represents the hypothetical distributions of scores of women (the solid line) and men (the dashed line) on some cognitive test. Note that for both groups, the average score is 100. Thus, if we only examined mean differences, we would conclude that the groups have the same level of ability. Yet when we look at Figure 7.2, it is clear that the distributions for women and men are not identical. Men’s scores have much more within-group variance than women’s scores do. Recall from Chapter 2 that within-group variance is an index of how spread out the values are among people within a group. In other words, the men’s distribution has more very low and very high scores, while the women’s scores more tightly cluster around the mean.
Outliers Values at the extreme ends of a statistical distribution.
Greater male variability hypothesis The prediction that men show more variability than women in their distributions of scores on cognitive performance measures, leading them to be overrepresented in the very bottom and very top of score distributions.
Figure 7.2 Differences in Within-Group Variance
Source: Chu and Kita (2011).
Note: Although these two distributions have the same mean, the men’s distribution has much more within-group variance than the women’s distribution.
The possibility that men show greater variability in cognitive performance is aptly named the greater male variability hypothesis. Though first formally proposed by Havelock Ellis in 1894, this hypothesis dates back to Charles Darwin (Shields, 1982). If this hypothesis has merit, sex differences will be more pronounced in the high and low tails of distributions. In other words, where in a distribution a researcher draws a sample will impact the size and direction of the sex difference found.
STOP AND THINK
With the greater male variability hypothesis in mind, imagine that you took samples of female and male high achievers in math (from the right tails of each distribution). What would you likely find, in terms of the size and direction of the sex difference? How would this differ if you took samples from the lower, left tails of each distribution or if you took samples from the middle of each distribution? If a sex difference researcher is unaware of this phenomenon, how could it bias the conclusions drawn?
In support of the greater male variability hypothesis, men are disproportionately represented at both ends of cognitive ability distributions (Bergold, Wendt, Kasper, & Steinmayr, 2017; Hyde et al., 2008). This means that the top scorers on many cognitive tests are more likely to be men than women, but so are the lowest scorers. As early as the 19th century, researchers noted the disproportionate numbers of men in homes for the intellectually challenged (H. Ellis, 1894). Similarly, boys are more likely than girls to receive diagnoses of learning disabilities and developmental disorders, such as dyslexia (J. M. Quinn & Wagner, 2015), autism spectrum disorder (Loomes, Hull, & Mandy, 2017), and Down syndrome (Shin et al., 2009).
Dyslexia A learning disability characterized by impairments in reading, including difficulties with word recognition and spelling.
Autism spectrum disorder A developmental disorder typically characterized by sensory sensitivities, repetitive behaviors, and difficulties with speech, nonverbal communication, and social interaction.
How does the greater male variability hypothesis fare across cultures, race, and time? In a cross-cultural study of over 40 countries, Machin and Pekkarinen (2008) found greater male variance in math performance in most (88%) of the countries examined. Within the United States, most top-scoring math test takers are boys, but the ratio of top-scoring boys to girls declined from the early 1980s through 2015. Among students earning perfect math SAT scores, 93% were boys in the early 1980s, whereas only 75% were boys by 2015 (Makel, Wai, Peairs, & Putallaz, 2016). Some evidence, however, counters the greater male variability hypothesis. When looking at verbal performance, for instance, girls are about twice as likely as boys to earn perfect 800 scores on the SAT verbal test (Makel et al., 2016). Additional research suggests that the greater male variability hypothesis does not hold consistently for people in every racial and ethnic group. For example, Hyde et al. (2008) found greater male than female variability in math performance for White students but not for Asian students.
When it does occur, what might account for the greater variability in men’s math performance? Note that simply finding evidence of greater male variability does not indicate which factors—biological or sociocultural—cause the greater variability. As with most outcomes, both nature and nurture likely play a role. On the nurture side, greater male variability in math ability does not emerge in some countries (e.g., Denmark and The Netherlands), and it tends to decrease as countries show greater evidence of gender equality (Hyde & Mertz, 2009). On the nature side, research suggests that alleles, or variant forms of genes, on the X chromosome can explain sex differences in the lower tails of ability distributions, such as the disproportionate numbers of boys and men with intellectual disabilities (Turkheimer & Halpern, 2009). To date, however, there are no known genetic variations that can account for differences in variability at the high end of the distribution. This brings us back to the chapter opener and the controversial comments that Lawrence Summers made in which he attributed men’s disproportionate representation in STEM fields to their greater “natural aptitude” at the highest levels of achievement. Contrary to Summers’s position, we lack evidence that nature alone can explain women’s underrepresentation in STEM majors and fields. We will return to this issue shortly.
Boys are more likely than girls to receive diagnoses of developmental disorders such as Down syndrome.
Source: © iStockphoto.com/SDI Productions
Down syndrome A genetic disorder characterized by physical growth delays, mild to moderate intellectual impairment, and distinct physical features.
Allele A variant form of a gene.
HOW DO INDIVIDUAL DIFFERENCES AND CONTEXT INFLUENCE COGNITIVE PERFORMANCE?
Throughout this book, we have noted that nature and nurture interact in complex ways to produce sex differences in cognitive performance. This assumption lies at the heart of Diane Halpern’s (2004) biopsychosocial model, which addresses a range of biological and environmental factors that contribute to cognitive performance (see Figure 7.3). Halpern argues that biology (e.g., genetic predispositions and prenatal hormones) and environment (e.g., culture and learning experiences) are inextricably linked and mutually shape each other to produce cognitive abilities. In other words, cause and effect are circular, and each factor both influences and is influenced by the other. This model fits the evidence well and echoes the point we made in Chapter 3 about the inseparability of nature and nurture. People’s learning experiences influence the structure and growth of their neurons. The structure of the brain, in turn, leads people to develop certain skills and aptitudes and to select experiences that reinforce and strengthen the brain’s architecture. Learning is both biological and environmental, “as inseparable as conjoined twins who share a common heart” (Halpern, 2004, p. 138).
To get a fuller picture of how sex and gender relate to cognitive performance, it is important to examine various contextual factors and individual difference variables that can impact performance. Not everyone has access to high-quality education. Some people approach high-stakes testing situations as an exciting challenge and prepare accordingly, while others freeze up. Still others may feel perfectly at ease taking a standardized test but have little motivation to work consistently each day in school, resulting in high test scores but poor grades. In what follows, we review several variables that may contribute to and interact with sex differences in cognitive test performance. While reading this material, consider where each factor might enter the circle of Halpern’s (2004) biopsychosocial model of cognitive ability (see Figure 7.3). If you find this task difficult, you are not alone—disentangling whether factors are biological, sociocultural, or psychological poses a challenge for researchers as well.
Figure 7.3 A Biopsychosocial Model of Cognitive Ability
Source: Halpern (2004).
Culture, Race, and Educational Access
What can cross-cultural evidence tell us about gender and cognitive ability? If boys have an innate cognitive advantage in quantitative skills, or girls have an innate advantage in verbal skills, it should hold across cultures. The evidence here is mixed and depends on the cognitive ability domain. On the nurture side, several large cross-cultural studies, representing hundreds of thousands of people, find a fair amount of cross-cultural variation in the size of some cognitive sex differences. As noted earlier, the size of these differences often correlates with measures of gender equality, suggesting that some sex differences stem less from biology and more from cultural, structural, and economic features of societies (Hyde & Mertz, 2009; D. I. Miller & Halpern, 2014). Of all the cognitive domains, math performance shows the greatest variability in sex differences from culture to culture (Else-Quest et al., 2010). Moving more toward the nature side, verbal and visual-spatial sex differences generally show less (though still some) cross-cultural variation (Halpern, 2004; Lippa et al., 2010; Reilly, 2012). Figure 7.4 illustrates these trends in a study of reading and math performance in schoolchildren from 33 countries (Halpern, 2004). Note that girls significantly outperformed boys in reading literacy in every country. Boys, on the other hand, significantly outperformed girls in math performance in only about 20% of the countries. These cross-cultural findings suggest that nature may play a stronger role in shaping verbal ability than in shaping math ability.
While interesting, these global, binary comparisons of boys and girls oversimplify a complex picture. Since not every girl and not every boy around the globe has the same access to resources and education, comparing the average girl to the average boy in cognitive abilities glosses over some important distinctions. In the United States, schools are becoming increasingly segregated based on income and race, with substantially more resources allocated to schools with majority White—compared to majority Black and Latinx—student populations (Ladson-Billings, 2006). This creates what Ladson-Billings calls the education debt, or the ongoing, cumulative lack of investment in the education of low-income and racial minority students. Structural inequalities in school systems, coupled with racism, lead to substantially different educational experiences and outcomes for students based on race and income. For example, schools serving primarily low-income and minority students are less equipped to offer high-level math and science courses, and schools offering such classes regularly track minority students out of them (Diversity in Mathematics Education, 2007).
Education debt The ongoing, cumulative lack of investment in the education of low-income and racial minority students that leads to different educational experiences and outcomes based on race and income.
It is important to consider how these contextual factors might relate to individual differences in academic performance. Since the 1970s, researchers have documented achievement gaps in the United States that show Black and Latinx students lagging behind White students in reading and math, although the gaps have been decreasing over time, largely due to increases in Black and Latinx students’ test scores (Stanford Center for Education Policy Analysis, 2015). Moreover, racial and ethnic achievement gaps sometimes interact with sex. For example, the Black-White achievement gap in math has been narrowing primarily due to gains made by female Black students (Vanneman, Hamilton, Anderson, & Rahman, 2009).
What accounts for achievement gaps? Although there is no consensus pointing to one definitive explanation, researchers have identified a host of contributing factors, such as school funding, teaching practices and curricula, discrimination, social and physical environments, family background and resources, and culture (Bécares & Priest, 2015; Ladson-Billings, 2006). It is clear that low-income and minority students generally face structural inequalities and barriers in their access to high-quality education. Meaningful improvements (e.g., decreases in achievement gaps) will therefore likely require addressing the race- and class-based structural inequalities in our educational system.
Figure 7.4 Children’s Reading and Math Performance Across Cultures
Source: Halpern (2004).
For many people, math arouses anxiety. A large body of evidence indicates that girls and women have more math anxiety in general than boys and men (Ramirez, Shaw, & Maloney, 2018). Boys across the globe report somewhat more positive attitudes and feelings about math than girls (d values from 0.10 to 0.33), and girls in the United States tend to have lower math self-confidence (d = 0.27) and greater math anxiety (d = −0.23) than boys (Else-Quest et al., 2010). Does the sex difference in math anxiety contribute to the sex difference in math performance at the high end of achievement, and, by extension, to the underrepresentation of girls and women in STEM disciplines?
Math anxiety does relate to lower math achievement (Ma, 1999). This could mean that anxiety produces lower math achievement, but it could also mean that poor math performance produces anxiety. There is evidence for both of these processes, which suggests a self-reinforcing loop: doing poorly in math can cause anxiety, which can then disrupt future performance in math (Carey, Hill, Devine, & Szucs, 2016). How exactly might math anxiety produce poorer math performance? You may recall the concept of stereotype threat from Chapter 5 (“The Contents and Origins of Gender Stereotypes”). Stereotype threat is the anxiety people feel when they risk confirming a negative stereotype about their group (Steele, 1997). For example, consider the stereotype that Lawrence Summers espoused in the chapter opener: that girls and women lack STEM aptitude relative to men. Now imagine that you are a woman who is taking a difficult but important math exam that will determine whether you can get college math credit. Many people experience anxiety in these situations; but if you are a woman who is reminded about gender stereotypes before the exam, you may notice an extra little voice of doubt in your head, adding to your anxieties: “What if the stereotypes are true? Maybe I’m not cut out for this.” Being reminded of a negative stereotype about one’s group can disrupt working memory capacity (Rydell, McConnell, & Beilock, 2009; Schmader & Johns, 2003). This can be especially detrimental when trying to solve complex math problems because such problems often require people to hold several pieces of information in memory simultaneously. Thus, nagging doubts about the poor math abilities associated with one’s sex can cause lapses in concentration that disrupt performance. Consistent with this explanation, stereotype threat effects diminish for women who have a greater working memory capacity (Regner et al., 2010).
Meta-analyses indicate that stereotype threat may have a small overall effect on women’s cognitive test performance (Nguyen & Ryan, 2008; Picho, Rodriguez, & Finnie, 2013; Shewach et al., 2019), but the type of threat makes a difference. Researchers can manipulate stereotype threat using either subtle cues (e.g., merely reminding people of their sex by having them write it down on a form prior to an exam) or blatant cues (e.g., telling people before a math exam that “women tend to perform worse than men on this test”). Subtle cues tend to elicit stronger stereotype threat effects than blatant cues, perhaps because blatant reminders of stereotypes are easier to attribute to discrimination and therefore disregard (recall the material on attributional ambiguity from Chapter 6, “Power, Sexism, and Discrimination”). Moreover, women who identify moderately strongly with math generally experience the most negative effects of stereotype threat on math test performance. For women who identify very strongly with math, confidence in their math abilities likely helps them to overcome the threat of negative stereotypes, and women who do not identify with math at all may not feel threatened by negative stereotypes.
Teachers’ math anxieties may also impact students’ performance. Beilock and colleagues (2010) measured math anxiety among a sample of female elementary school teachers (women make up over 90% of elementary school teachers in the United States). At the beginning of the school year, teachers’ math anxieties were unrelated to students’ performance in math. But by the end of the school year, a disheartening pattern emerged. Math anxiety in teachers positively predicted female students’ (but not male students’) endorsement of the stereotype that “boys are good at math and girls are good at reading.” Worse yet, the teachers’ math anxieties predicted lower math performance for girls by the end of the year. Similarly, having a math-anxious parent who assists with homework can lead kids to develop math anxiety themselves (Maloney, Ramirez, Gunderson, Levine, & Beilock, 2015).
What can girls and women do to overcome the negative effects of math anxiety? Berkowitz and colleagues (2015) tried an intervention aimed at reducing math anxiety in first graders (and their math-anxious parents). They gave children iPads that included either a math story app or a reading app (control condition). The math achievement of children who used the math app increased significantly across the school year, and the improvements were especially large for children with math-anxious parents. This intervention works regardless of sex, but since girls and women show higher levels of math anxiety compared with boys and men, the intervention holds the potential to decrease sex differences in math anxiety.
SIDEBAR 7.2 YOU’VE COME A LONG WAY, BARBIE
In July 1992, Mattel released a talking Barbie that was programmed to utter several canned phrases. One of Barbie’s phrases—“Math class is tough!”—raised the ire of the American Association of University Women for its negative portrayal of girls’ math abilities. By October 1992, Mattel responded to the controversy by removing the offensive phrase from Barbie’s lexicon. Almost a generation later, following a poll of fans, Mattel introduced computer engineer Barbie in 2010 and scientist Barbie in 2016.
Unlike the Barbie of the early 1990s, who struggled to keep up in her math class, today’s Barbie is more likely to pursue a career as a computer engineer or scientist.
Source: © iStockphoto.com/ivanastar
Achievement Motivation and Sensitivity to Feedback
Some question whether differences in achievement motivation—or individuals’ need to meet goals and accomplish tasks—can explain girls’ (sometimes) lower performance in complex math reasoning tests. When it comes to math, perhaps girls feel less motivated to succeed or persist in the face of failure than boys do. However, Jacquelynne Eccles has been studying this issue for many years (Eccles, 1984, 2005), and she finds no evidence that girls are more likely to give up after academic failures. In fact, some studies find that girls show higher intrinsic motivation for school in general, while boys show greater work avoidance (Spinath, Eckert, & Steinmayr, 2014). This may explain why, despite roughly equal cognitive ability and generally small differences on standardized test scores, girls tend to get better school grades than boys.
Achievement motivation An individual’s need to meet goals and accomplish tasks.
If there are no sex differences in achievement motivation, then perhaps there are sex differences in the effects that feedback has on people’s confidence? Imagine struggling as a student to learn some challenging new material. Your teacher gives you mixed feedback on your performance, communicating that you could be doing much better. How do you react? Tomi-Ann Roberts (1991) reviewed evidence indicating that women and men tend to respond differently to evaluative feedback about their performance. In general, women’s self-evaluations tend to be more responsive than men’s to the feedback that they receive, both good and bad. This may be because women, more so than men, tend to approach performance situations as a way of gaining information about their abilities. In contrast, men tend to approach performance situations as opportunities to compete, more often adopting a self-confident approach that makes them relatively impervious to others’ evaluations of them. Thus, men are more inclined than women to respond to feedback by acknowledging positive comments and denying negative ones. While neither approach is necessarily better than the other, they can lead to sex differences in how people develop, act on, and update their beliefs about their own abilities. For example, Ehrlinger and Dunning (2003) showed that women tend to have less confidence in their scientific reasoning abilities than men, despite being equally competent. As a result, women were less likely to enter a science competition, thus reducing their own opportunities for feedback and the improvement it can foster.
STOP AND THINK
Consider the pros and cons of these different strategies for dealing with evaluative feedback about cognitive performance. In what ways might it be beneficial to remain responsive to others’ feedback? In what ways might it be costly or detrimental? What about the strategy of ignoring negative performance evaluations? How might this approach be beneficial or detrimental in the long run?
HOW DO SEX AND GENDER RELATE TO OUTCOMES IN SCHOOL AND STEM FIELDS?
Education and School Performance
As mentioned, girls and boys do not have equal access to education around the globe, with some areas of the world such as the Middle East and sub-Saharan Africa showing the largest gender gaps favoring boys (United Nations Educational, Scientific and Cultural Organisation, 2016). Increasing educational access for girls not only has psychological and physical health benefits for individual girls, but it has economic benefits for entire communities as well. As girls’ level of education (and hence earning potential) increases within a community, the degree of poverty in the community decreases (Bourne, 2014).
For girls and boys with access to education, sex and culture may interact to shape educational outcomes. East Asian cultures, such as Taiwan and Japan, tend to stress effort-based learning in which teachers and other adults expect students to put effort into their studies regardless of their personal interest in the subject matter. In contrast, Western cultures, such as the United States, tend to emphasize interest-based learning in which adults encourage students to channel their efforts into particular domains of interest. How do these different educational emphases relate to outcomes? To answer this, E. M. Evans, Schweingruber, and Stevenson (2002) examined academic interest and knowledge levels in representative samples of eleventh-grade girls and boys in Taiwan, Japan, and the United States. Overall, regardless of sex, students in Taiwan and Japan performed better in math than their U.S. counterparts. Moreover, sex differences (favoring boys) in math were larger in Taiwan and Japan than in the United States, but Taiwanese and Japanese girls still outperformed both girls and boys in the United States, despite reporting lower levels of interest in math as compared with U.S. boys. That is, their culture’s emphasis on effort-based learning led girls to achieve better math performance. This suggests that cultural values may play a more powerful role in shaping school performance than gender roles do.
Home and Classroom Dynamics
Children’s academic interests are malleable, making them susceptible to influence by specific home and school environments. For instance, Edward Melhuish and his colleagues followed English children over time and found that children’s math achievement at age 10 was predicted less strongly by their sex than it was by factors such as their home learning environment at ages 3—4, their mother’s education level, and the effectiveness of their primary school (Melhuish et al., 2008). Just as more stimulating home environments can enhance children’s academic performance, parents can also behave in ways that decrease their children’s performance. As noted earlier, parents can unwittingly transfer their own math anxieties to their children. In a longitudinal study of first and second graders’ math achievement, Erin Maloney and her colleagues found that the children of parents higher in math anxiety learned less math and showed increases in math anxiety over the course of the school year, but this occurred only when parents frequently helped with math homework (Maloney et al., 2015). Thus, math-anxious parents may unintentionally pass their math anxiety onto their children while helping them with math. However, when math-anxious parents are trained in how to complete structured, interactional math activities with their children, it can increase the children’s math achievement across the school year (Berkowitz et al., 2015).
Just as with home environments, school environments can shape children’s academic expectations and interests. The domain of math is again relevant here. As discussed, girls often have more negative attitudes toward math than boys do, which has bearing on girls’ math performance and their choices regarding math-related courses and careers (Gunderson, Ramirez, Levine, & Beilock, 2012). To what degree are children’s math attitudes shaped by their teachers’ attitudes? The findings in this area are mixed. Some evidence suggests that teachers’ gender stereotypes about math can spill over and influence their students’ gender stereotypes about math (Keller, 2001). However, teachers’ perceptions of their own students’ math abilities tend to be accurate rather than based merely on gender stereotypes (Jussim & Eccles, 1992). Finally, although teachers’ beliefs about their students’ math potential in kindergarten predict students’ interest in math across the elementary school years, this pattern holds for both female and male students (Upadyaya & Eccles, 2014). Thus, while teachers’ gender stereotypes can influence their students’ gender stereotypes, there is less evidence that teachers’ expectations directly drive sex differences in their students’ math performance.
In some cases, school performance can be impacted by the more troubling issue of overt discrimination. For instance, one study of over 250 primarily Latinx, Black, and White sexual minority youth at urban high schools in the United States found that students who reported more daily experiences with homophobic or transphobic discrimination also had worse school performance, more absenteeism, and more discipline problems at school (Craig & Smith, 2014). Given the role of high school performance in predicting future life outcomes, it will be important for researchers to examine how best to meet the unique needs of sexual minority youth within schools. In a related vein, some advocate that educating girls and boys in single-sex classrooms would be an effective strategy to minimize the negative consequences of gender-biased school environments. The key question here centers on the relative effectiveness of single-sex versus mixed-sex learning environments, which we explore in the debate titled “Do Children Fare Better in Single-Sex Classrooms?”
DEBATE: DO CHILDREN FARE BETTER IN SINGLE-SEX CLASSROOMS?
Given the nature of the topic and the uneven and sometimes contradictory state of the research findings, people often have quite passionate opinions about gender and cognitive abilities. Here, we will consider a relevant real-world social issue: whether single-sex or mixed-sex classrooms produce better learning outcomes. The public push for single-sex education is fairly recent. In 2002, the United States had only about a dozen public schools that offered single-sex classrooms, but by 2011, this number had grown to over 500 (Hartmann, 2011). Advocates of single-sex schooling believe that traditional mixed-sex classrooms can harm children’s learning and socialization, while others argue that the evidence does not support this claim. What are the arguments for and against single-sex classrooms, and what does the evidence suggest?
YES, CHILDREN FARE BETTER IN SINGLE-SEX CLASSROOMS
Single-sex schooling advocates, such as the National Association for Single Sex Public Education, argue that the current environment in schools is sexist and narrows children’s expectations. They further argue that single-sex classrooms can dissolve gender stereotypes and allow children to flourish (Hartmann, 2011). In addition, these groups argue that boys and girls should be separated due to differences either in cognitive learning or in temperaments and social needs.
In a longitudinal study of Canadian high school students, Shapka and Keating (2003) found that girls who received math and science instruction in single-sex classrooms in the ninth or tenth grade later took more math and science classes and made better grades in these classes than girls who received math and science instruction in mixed-sex classrooms. Furthermore, Kessels and Hannover (2008) randomly assigned German eighth graders to single-sex versus mixed-sex physics classes and found that girls in single-sex classes reported having more positive perceptions of their physics ability than girls instructed in mixed-sex classrooms.
Evidence also suggests that because boys and girls tend to behave differently, teachers treat them differently in classrooms. For example, one study (Cornwell, Mustard, & Van Parys, 2013) found that elementary teachers factor children’s behavior into grades, and girls exhibit better classroom behavior than boys, on average. Girls tend to be more attentive, more persistent, more independent, less disruptive, and less fidgety than boys. These sex differences in temperaments lead some to argue that most of today’s classrooms are not designed with boys in mind. Lessons that require sitting quietly for long periods of time are not ideal for boys, who tend to have greater activity levels. Single-sex classrooms would allow teachers to tailor learning to the unique learning styles and behavior patterns of boys and girls.
The effectiveness of single-sex versus mixed-sex classrooms may depend on the cultural setting. For cultural and religious regions, education is often segregated by sex in Muslim cultures; in many Muslim countries that score relatively low on measures of gender equality, girls actually outperform boys on math tests. This may occur because in countries with rigid gender roles and high gender inequality, mixed-sex classrooms may harm girls by reinforcing their subordinate status (Ellison & Swanson, 2010; Fryer & Levitt, 2010). This suggests that the effectiveness of single-sex versus mixed-sex education may depend on complex cultural factors.
NO, CHILDREN DO NOT FARE BETTER IN SINGLE-SEX CLASSROOMS
Rather than erasing gender stereotypes, single-sex classrooms may actually reinforce stereotypes because the contrast between the single-sex classroom and the outside (mixed-sex) world simply highlights how the world is organized by sex. Furthermore, educating children in single-sex classrooms can leave them unprepared for a world in which they must interact with people of all sexes and genders (Halpern et al., 2012).
In a recent meta-analysis of the relationship between type of schooling (single-sex versus mixed-sex) and performance, researchers examined cross-cultural data from more than 1.6 million students in Grades K—12 (Pahlke, Hyde, & Allison, 2014). They looked at sex differences in science and math performance, as well as in attitudes toward school, gender stereotyping, aggression, body image, and victimization. The evidence did not support the superiority of single-sex classrooms, showing no significant differences overall in performance or attitudes for boys or girls in single-sex versus mixed-sex classrooms. A 2012 report in Science published by leading cognitive psychologists argued that the push for single-sex education is “deeply misguided, and often justified by weak, cherry picked, or misconstrued scientific claims rather than valid scientific evidence” (Halpern et al., 2012, p. 1706).
Rather than single-sex schooling, some advocate for attending more closely to the unique temperaments and cognitive styles of boys when developing school curricula. For instance, some countries (e.g., Australia, Canada, and the United Kingdom) are experimenting with programs to make schools more “boy friendly” by increasing physical activity and recess time, selecting more male-oriented reading assignments, instituting campaigns to increase male literacy, and recruiting more male teachers (Hoff Sommers, 2013).
Now that you have read both sides of this debate, where do you fall on the issue? Which evidence do you find most and least convincing? Why?
Sex, Gender, and STEM Fields
The evidence reviewed in this chapter suggests that, overall, women and men do not differ in intellectual ability, which challenges the notion that women lack the ability to compete for careers in science and math. And yet, consider the following: Although women represent about half the U.S. workforce, only 28% of workers in STEM fields were women in 2015 (National Science Foundation, 2018). In 2006, women earned about 40.2% of all doctorates in the sciences and engineering, but they constitute only 5.0% of full professors in engineering, 17.4% in computer science, 26.2% in life sciences, 8.3% in physical sciences, and 8.6% in mathematics (Burrelli, 2008). What’s going on here? Is there a gender bias in STEM disciplines? In the following sections, we consider some possible explanations for gender disparities in STEM fields.
Women’s underrepresentation in STEM fields may result from discrimination, either overt or subtle. As we discussed in Chapter 6, gender biases that curtail women’s options can occur at many stages and take many forms. For instance, one study found that U.S. undergraduate women encountered more benevolent sexism (such as paternalistic offers of extra assistance) than hostile sexism (such as overtly insulting comments about their competence) in their STEM courses. Moreover, women who experienced more benevolent sexism—and who were weakly identified with STEM fields—also reported lower confidence in their STEM abilities and weaker intentions to major in a STEM field (Kuchynka et al., 2018). Thus, experiences with sexism in their college courses may discourage women from pursuing STEM as a major, especially if they do not identify especially strongly with STEM as a major.
Even among women who succeed in STEM at the college level, there may be hiring biases that work against them. In one study, science faculty evaluated a fictitious undergraduate student—portrayed as either a woman or a man—who applied for a job as a laboratory manager. Both female and male science professors viewed the male applicant as more competent and hirable compared with the female applicant, even though the applicants varied only in their sex (Moss-Racusin, Dovidio, Brescoll, Graham, & Handelsman, 2012). In another study, when participants considered candidates for a job that required math, they were twice as likely to recommend hiring male candidates as compared with female candidates, even when the groups had identical math skills (Reuben, Sapienza, & Zingales, 2014). In fact, male scientists at top universities employ more male than female graduate students and postdoctoral students in their labs (Sheltzer & Smith, 2014). Finally, once hired, women face discrimination in STEM workplaces. In one study of over 500 women in science, more than a third reported experiencing sex-based harassment in the workplace (J. C. Williams, Phillips, & Hall, 2014). Women of color may be especially likely to face discrimination in STEM workplaces. Their experiences range from having their competence questioned, to having to prove themselves repeatedly, to receiving backlash when they behave assertively.
Women are underrepresented in math-intensive STEM fields. For example, women constitute only about 25% of computer programmers in the United States. Given few sex differences in cognitive abilities, why do you think so few women pursue programming careers?
Source: © iStockphoto.com/vgajic
Some findings, however, challenge the notion of widespread discrimination against women in STEM. Consider the fact that women now earn 53% of PhDs in biology, 48% of medical degrees, and 78% of veterinary degrees (National Center for Education Statistics, 2012). These statistics indicate that women equal or even surpass men in some areas of STEM. Moreover, one review concluded that female professors in math-intensive STEM disciplines earn as much as men, receive tenure and promotion at comparable rates, and persist equally at their jobs (Ceci, Ginther, Kahn, & Williams, 2014). This review also concluded that women with PhDs now get hired for math-intensive academic jobs at rates comparable to men, suggesting recent changes to the long-standing patterns of gender bias in STEM disciplines, at least in academia. Similarly, in another study, faculty asked to evaluate hypothetical job candidates for STEM faculty positions preferred female applicants 2:1 over equally qualified male candidates (W. Williams & Ceci, 2015). In short, the evidence for overt sexism in STEM disciplines is mixed and suggests that factors other than cognitive abilities may be important in explaining women’s underrepresentation.
Interests, Values, and Expectations
Another potential explanation for the relative lack of women in STEM disciplines is that such careers simply appeal to them less. Supporting this explanation, the values and preferences of men and women differ in ways that can steer them to pursue different careers. For example, women tend to prefer activities and jobs that emphasize interactions with others and that require interpersonal skills, while men tend to prefer activities and jobs that emphasize working with machines or computers (Lippa, 2001; Su, Rounds, & Armstrong, 2009). In one study, Amanda Diekman and her colleagues asked college students about their interests in various careers, as well as how much they endorsed communal goals (e.g., helping others and working with people) and agentic goals (e.g., power over others and mastery). The results showed that the more people endorsed communal goals, the less interest they had in STEM careers (Diekman, Brown, Johnston, & Clark, 2010). This suggests that women may lack interest in STEM careers because they do not view them as offering opportunities to meet communal goals. Note that sex differences in communal and agentic goals, as well as beliefs about whether STEM careers offer opportunities to meet communal goals, emerge early in childhood. Researchers find that the beliefs and values of girls and boys start to differentiate along gender-stereotyped lines in the first grade (Eccles, Wigfield, Flanagan, Harold, & Blumenfeld, 1993). Thus, by the time they enter high school and college, girls may undervalue STEM careers because they do not view them as “helping” careers (Eccles, 2007).
SIDEBAR 7.3 GETTING GIRLS INTERESTED IN COMPUTER PROGRAMMING
Even young children in the United States believe that boys are better at computer programming and robotics than girls. How can we improve girls’ computer technology self-efficacy? Allison Master and colleagues recruited first-grade girls and boys to an experiment in which they programmed robots. In a control group of nonprogrammers, boys showed more interest and self-efficacy in programming than girls did. But among the group that had the programming lesson, the gender gap in interests and self-efficacy disappeared (Master, Cheryan, Moscatelli, & Meltzoff, 2017). Providing girls with early positive technology experiences may be critical to closing the gender gap in STEM.
Other research looks more closely at how children’s academic attitudes and beliefs predict their pursuit of specific STEM disciplines. As noted, adolescent boys have more positive math self-views and expectations of success than do adolescent girls. These sex differences emerge among White, Latinx, Black, and Asian American adolescents, with effect sizes in the small to moderate range (d values ranging from 0.16 to 0.62; Else-Quest, Mineo, & Higgins, 2013). Accordingly, male students are more likely than female students to take classes in computer and information science, engineering, science technologies, and physics, all of which are math intensive. In contrast, girls (particularly White and Asian American girls) tend to place more value than boys do on science, with moderate effect sizes (d = −0.43 and −0.49; Else-Quest et al., 2013). Moreover, female students are more likely than male students to take classes that are more science oriented and less math intensive, such as chemistry, advanced biology, health science, algebra II, and precalculus (B. Cunningham, Hoyer, & Sparks, 2015). These patterns suggest that it may not make sense to consider STEM disciplines as a block when talking about sex differences because women may outnumber men in some of these disciplines, while men outnumber women in others.
Gendered Family Responsibilities
Thus far, our analysis of sex differences in STEM focuses on factors that attract girls and women to—or repel them from—STEM fields. But what about outside factors that might pull women in other directions? One interesting hypothesis states that women’s underrepresentation in STEM fields has less to do with how women feel about STEM than it does with the gendered family responsibilities that fall disproportionately to women. Ceci and Williams (2009) argue that women often opt out of competitive STEM careers at a young age due to desires for and responsibilities of family. To examine this idea, Kimberly Robertson and her colleagues tracked top-performing math and science students from youth through adulthood and found sex differences in work hours and preferences by the time the students reached their mid-30s (Robertson, Smeets, Lubinski, & Benbow, 2010). Specifically, women willingly worked fewer hours per week than men due to increased family obligations. Moreover, women in science fields who have children face discrimination in that they are promoted less often than men who have children (Ceci, Williams, & Barnett, 2009). Thus, even when women have STEM careers, they may not advance to the highest ranks of their field at the same rates that men do because of sex-based labor divisions in which the bulk of childcare and household management falls to them.
STOP AND THINK
Consider the finding that people’s interests in STEM disciplines are shaped by their assumptions about whether or not STEM fields allow opportunities for helping others versus achievement and mastery. Is it true that STEM disciplines are not “helping” disciplines? Can you think of ways in which careers in STEM can help others, improve quality of life, and fulfill communal goals? Imagine that you were tasked with designing an educational campaign to change widespread beliefs about the lack of communal opportunities in STEM fields. What examples or messages might you include?
In summary, women’s underrepresentation in STEM fields may occur, in part, due to lifestyle choices and the demands placed on career women to rear children and manage homes. The same tug of war between family and career that pulled at two-time Nobel Prize—winning physicist and chemist Marie Curie 100 years ago (see Sidebar 7.4) still operates today. In Chapter 11 (“Work and Home”), we examine in greater detail how the clash between career demands and gendered expectations for family obligations can impact women’s career paths.
We now have the benefit of over 100 years of research on gender and cognitive abilities. Although little evidence exists for consistent sex differences in overall general mental ability or IQ, some research indicates small to moderate sex differences in specific cognitive abilities, although the size of these differences varies across time, place, and population. There may also be differences in the variability of cognitive performance that lead men to outnumber women at both the very high and very low ends of some cognitive test distributions. Rather than drawing a conclusion about what all of this means, we instead raise a provocative question: Is it possible that we, as a culture, place too much importance on intelligence? Perhaps so. Intelligence is not the only—or even the most important—predictor of academic and career success. Other behavioral and personality factors, such as self-discipline (Duckworth & Seligman, 2005) and conscientiousness (Conrad, 2006), predict performance in school, often better than performance on standardized tests, like IQ exams or the SAT. Of course, being smart has its advantages, but intelligence is only part of what leads to academic and career success.
SIDEBAR 7.4 “IT HAS NOT BEEN EASY”
French physicist Marie Curie won the Nobel Prize for science not once but twice. First, in 1903, she and her husband Pierre Curie shared the Nobel Prize in Physics, and second, in 1911, Curie won the Nobel Prize in Chemistry. Curie had this to say about gendered family responsibilities:
I have frequently been questioned, especially by women, of how I could reconcile family life with a scientific career. Well, it has not been easy.
—Marie Curie (1867—1934)
· 7.1 Explain the historical origins of research on sex differences in cognitive abilities.
Research on cognitive ability, or intelligence, began at the end of the 18th century with crude studies measuring the bumps on people’s heads to predict various mental capabilities. By the early 20th century, researchers began developing tests to measure intelligence and IQ. Much of this early research started with an assumption of female intellectual inferiority, and researchers often selectively sought and interpreted evidence to support this biased view. Over the next 100 years, researchers employed increasingly sophisticated cognitive tests and brain scanning techniques. In addition to improvements in the methods used for measuring cognitive abilities, there have also been reductions in blatant gender biases in the study of cognitive abilities. While research demonstrates no sex differences in general mental ability, there are some differences in more specialized cognitive abilities.
· 7.2 Analyze the specific domains of cognitive performance that show sex similarities and differences.
Cognitive psychologists examine performance on tests of verbal, quantitative, and visual-spatial abilities. Both the size and consistency of sex differences in cognitive abilities varies across specific tests, methods, and populations. Some verbal abilities, like speech production, reading comprehension, and writing, tend to favor girls over boys, but the size of the differences is generally small to moderate. In contrast, data do not support the stereotype of male superiority in math. Some studies find sex differences favoring boys on complex math problem solving, but these differences vary across time and culture. Boys in most cultures do report somewhat more positive attitudes about math, and girls often report less confidence and more anxiety about math.
One domain that produces reliable sex differences is visual-spatial abilities. This includes skills such as rotating figures mentally, determining relationships between objects, predicting trajectories of moving objects, and remembering locations of objects. Boys and men tend to outperform girls and women on most visual-spatial tests, with the exception of spatial location memory, which shows a female advantage.
In addition to average differences in cognitive performance, some researchers focus on sex differences in variability of performance. The cognitive performance of boys and men is typically more variable than that of girls and women. Thus, disproportionately more boys and men are at both the high and low extremes of performance. However, the causes of this greater variability—whether biological, environmental, or some combination of the two—are not well understood.
· 7.3 Evaluate contextual and individual difference factors that can influence cognitive performance.
Sex differences in cognitive performance, which seem to be decreasing over time, show inconsistency across studies and methods. They also vary in magnitude across cultures based on factors such as gender equality. When sex differences emerge, we do not fully understand the cause(s) of these differences—that is, whether they are “innate” and biological or products of culture, learning, and life experiences. Even if there are intrinsic aptitude differences, we lack definitive evidence for differences in brain structures or hormones that would explain them. Furthermore, performance in testing situations is affected by contextual and motivational factors, such as access to quality education, discrimination, math anxiety, achievement motivation, and sensitivity to feedback.
· 7.4 Apply research on gender and cognitive performance to real-world issues, such as gender disparities in educational systems, school performance, and STEM disciplines.
Boys tend to have greater access to education around the globe than girls. Increasing educational access for girls can lead to psychological and physical health benefits for girls and to economic benefits for communities. Moreover, cultural values can predict school performance. Even though East Asian girls report lower levels of interest in math as compared with U.S. boys, their cultural emphasis on effort-based learning leads them to achieve higher levels of math performance. Children have malleable academic interests, which means that parents, teachers, and learning environments—both at home and at school—can shape them in significant ways. Parents with math anxiety can pass this along to their children, but doing regular math activities together at home can increase children’s math achievement. Gender-based discrimination at school can decrease academic performance of sexual minority and gender identity minority high school students. Researchers disagree about whether single-sex or mixed-sex school environments are more effective for learning, with each side offering unique advantages and disadvantages.
Women tend to be underrepresented in some STEM disciplines. In contrast to the mostly small and inconsistent sex differences in cognitive abilities, sex differences in interests, values, and expectations appear to be larger. Some theorists propose that women’s underrepresentation in some STEM disciplines has less to do with differences in cognitive aptitude and more to do with factors such as discrimination and different interests and family responsibilities.
Test Your Knowledge: True or False?
· 7.1. Most sex differences in cognitive abilities map directly onto sex differences in specific brain structures. (False: It is unclear whether or how sex differences in brain structures map onto sex differences in cognitive abilities.) [p. 235]
· 7.2. Girls and boys do not differ significantly in average levels of general mental ability (intelligence). (True: There are no consistent sex differences in average intelligence, although there are some small to moderate sex differences in specific cognitive abilities.) [p. 245]
· 7.3. Countries with greater gender equality show smaller sex differences in verbal abilities. (False: In countries that have more gender equality, girls tend to outperform boys in reading performance by the widest margins.) [p. 245]
· 7.4. Reminding women about negative stereotypes about women’s math abilities can lower their math test performance. (True: When reminded of negative stereotypes about women’s math abilities, women experience stereotype threat, which can interfere with their performance on math tests.) [p. 255]
· 7.5. Women’s underrepresentation in careers in science, technology, engineering, and math (STEM) is due, at least to some degree, to lifestyle choices and demands placed on them to rear children and manage homes. (True: Women opt out of STEM careers at higher rates than men, at least in part, because of family obligations.) [p. 264]
Descriptions of Images and Figures
Back to image
The section below the eye is labeled language. The four sections above the eyebrow from left to right are: weight, color, order, and calculation. The first outer layer of the skull lists the following sections from the front to the back:
· Human nature
· Parental love
The second layer shows the following sections from front to back:
· Friendship or adhesiveness
The third layer shows the following sections from front to back:
The fourth layer shows the following sections from front to back:
The region in front of the ear is labeled alimentivesness. The region above the ear is labeled destructiveness. The region at the back of the ear is labeled E. vitativeness.
The numbering and definition of the organs are listed as follows:
· 1: Amativeness, Love between the sexes
· A: Conjugality, Matrimony — love of one
· 2: Parental love, Regard for offspring, pets
· 3: friendship, Adhesiveness — sociability
· 4: Inhabitiveness, Love of home
· 5: Continuity, One thing at a time
· E: Vitativeness, Love of life
· 6: Combativeness, Resistance — defense
· 7: Destructiveness, Executiveness — force
· 8: Alimentiveness, Appetite — hunger
· 9: Acquisitiveness, Accumulation
· 10: Shoretiveness, Policy — management
· 11: Cautiousness, Prudence — provision
· 12: Approbativeness, Ambition — display
· 13: Self-esteem, Self-respect — display
· 14: Firmness, Decision — perseverance
· 15: Conscientiousness, Justice, equity
· 16: Hope, Expectation — enterprise
· 17: Spirituality, Intuition — faith — credulity
· 18: Veneration, Devotion — respect
· 19: Benevolence, Kindness — goodness
· 20: Constructiveness, Mechanical ingenuity
· 21: Ideality, Refinement — taste — purity
· B: Sublimity, Love of grandeur — infinitude
· 22: Imitation, Copying — patterning
· 23: Mirthfulness, Jocoseness — wit — fun
· 24: Individuality, Observation
· 25: Form, Recollection of shape
· 26: Size, Measuring by the eye
· 27: Weight, Balancing — climbing
· 28: Color, Judgment of colors
· 29: Order, Method — system — arrangement
· 30: Calculation, Mental arithmetic
· 31: Locality, Recollection of places
· 32: Eventuality, Memory of facts
· 33: Time, Cognizance of duration
· 34: Tune, Sense of harmony and melody
· 35: Language, Expression of ideas
· 36: Causality, Applying causes to effect
· 37: Comparison, Inductive reasoning - illustration
· C: Human nature, Perception of motives
· D: Agreeableness, Pleasantness — suavity.
Back to Figure
· The first figure shows an object made up of small cubes. Four cubes arranged horizontally with one cube attached to the first cube vertically and three cubes attached to the fourth cube vertically. Two cubes are stacked on the top the third vertical cube.
· The other four figures are:
o A: Four horizontal cubes with three cubes stacked above the first cube and a single cube stacked below the fourth cube. Two cubes are attached backwards to the top vertical cube.
o B: Three horizontal cubes to which three vertical cubes are attached to the third cube. On the top of the third vertical cube, two cubes are stacked, to which another vertical cube is attached.
o C: Three horizontal cubes to which three vertical cubes are attached to the third cube. Three cubes are attached backward to the bottom vertical cube to which another cube is attached downward.
o D: Three horizontal cubes to which three vertical cubes are attached to the first cube. Three cubes are attached backward to the bottom vertical cube to which another cube is attached downward.
· Four glasses that are tilted toward the left show water line in different angles.
o The first glass has a horizontal line parallel to the surface plane.
o The second glass has a slightly tilted line.
o The third glass has a line diagonal to the glass.
o The fourth glass has a line parallel to the base of the glass.
· Three figures show the process of folding a square paper and punching a hole as follows:
o The first square has a solid rectangle in the bottom half with dotted boundary in the upper half.
o The second square has a small solid square in the fourth quadrant with remaining boundary as dotted.
o The third square has a small solid square in the fourth quadrant with remaining boundary as dotted. A small dot is located near the top right of the solid square.
· Five squares show placement of four dots as follows:
o The first square has four dots in four corners.
o The second square has two dots on the opposite sides of the square.
o The third square has four dots located in the center.
o The fourth square has two pairs of squares on either sides of the square.
o The fifth square has two pairs of squares on the top and bottom of the square.
Back to Figure
The x axis labeled performance on a cognitive test ranges from 70 to 130 in increments of 10. The y axis is labeled frequency. A dotted bell curve labeled men’s scores starts at x axis equals 70 and ends at x axis equals 130 with less frequency. A solid bell curve labeled women’s scores starts at x axis equals 80 and ends at x axis equals 120 with higher frequency than men’s scores.
Back to Figure
The central component at the top is labeled brain and other central nervous system development. It flows in the clockwise direction and gets divided into three components:
· Experience/ Environments
An external component labeled culture converges with experiences/ Environments. It then flows clockwise towards brain and other central nervous development through two branches:
· Internal changes (Example: hormone secretions)
Another branch from experiences/ environments converges with an external component labeled genetic predispositions and flows toward brain and other central nervous development.
Back to Figure
· The left axis is labeled boys score higher and it ranges from 0 to 40 in increments of 20 from right to left respectively.
· The right axis labeled girls score higher ranges from 0 to 40 in increments of 20 from left to right respectively.
· The countries along with respective scores are listed below:
o Girls score higher:
§ Australia: 30.15
§ Austria: 23.27
§ Belgium: 30.4
§ Canada: 30.4
§ Cyprus: DM
§ Czech Republic: 32.5
§ Denmark: 24.6
§ France: 25
§ Germany: 31.3
§ Greece: 32
§ Hong Kong: DM
§ Hungary: 30.5
§ Iceland: 37
§ Iran Islamic Rep: DM
§ Ireland: 26.3
§ Japan: 31.6
§ Korea: 22
§ Latvia: 48
§ Lithuania: DM
§ Netherlands: 32.2
§ New Zealand: 45
§ Norway: 43.5
§ Portugal: 28
§ Russian Federation: 38.5
§ Singapore: DM
§ Slovak Rep: DM
§ Spain: 28.6
§ Sweden: 38.5
§ Switzerland: 34
§ United Kingdom: 23
§ England: DM
§ Scotland: DM
§ United States: 33.4
· The left axis is labeled boys score higher and it ranges from 0 to 40 in increments of 20 from right to left respectively.
· The right axis labeled girls score higher ranges from 0 to 40 in increments of 20 from left to right respectively.
· The countries along with respective scores are listed below:
o Girls score higher:
§ Australia: 20
§ Canada: 17.3
§ Cyprus: 16
§ Lithuania: 13.81
§ Singapore: 12.8
o Boys score higher:
§ Austria: 17.4
§ Belgium: 17.5
§ Czech Republic: 17.7
§ Denmark: 25
§ France: 18.5
§ Germany: 12.1
§ Greece: 18
§ Hong Kong: 25.3
§ Hungary: 12.4
§ Iceland: 19
§ Iran Islamic Rep: 19.6
§ Ireland: 18.5
§ Japan: 25.1
§ Korea: 13.3
§ Latvia: 20
§ Netherlands: 20
§ New Zealand: 13.6
§ Norway: 20.2
§ Portugal: 14
§ Russian Federation: 20
§ Slovak Rep: 14
§ Spain: 20
§ Sweden: 14.5
§ Switzerland: 20.6
§ United Kingdom: DM
§ England: 14.2
§ Scotland: 27.3
§ United States: 21.