If there were no group differences in aptitude scores, psychologists could politely debate hereditary and environmental influences in their ivory towers. But there are group differences. What are they? And what shall we make of them?
In science, as in everyday life, differences, not similarities, excite interest. Compared with the anatomical and physiological similarities between men and women, our differences are minor. In that 1932 testing of all Scottish 11-year-olds, for example, girls’ average intelligence score was 100.6 and boys’ was 100.5 (Deary et al., 2003). So far as g is concerned, boys and girls, men and women, are the same species.
Yet, most people find differences more newsworthy. Girls are better spellers, more verbally fluent, better at locating objects, better at detecting emotions, and more sensitive to touch, taste, and color (Halpern et al., 2007). Boys outperform girls in tests of spatial ability and complex math problems, though in math computation and overall math performance, boys and girls hardly differ (Else-Quest et al., 2010; Hyde & Mertz, 2009; Lindberg et al., 2010). Males’ mental ability scores also vary more than females’. Thus, boys worldwide outnumber girls at both the low extreme and the high extreme (Machin & Pekkarinen, 2008; Strand et al., 2006). Boys, for example, are more often found in special education classes. And among 12-to 14-year-olds scoring extremely high (700 or higher) on the SAT® exam math section, boys outnumber girls 4 to 1 (Wai et al., 2010).
The most reliable male edge appears in spatial ability tests like the one shown in FIGURE 64.1. The solution requires speedily rotating three-dimensional objects in one’s mind (Collins & Kimura, 1997; Halpern, 2000). Today, such skills help when fitting suitcases into a car trunk, playing chess, or doing certain types of geometry problems. From an evolutionary perspective, those same skills would have helped our ancestral fathers track prey and make their way home (Geary, 1995, 1996; Halpern et al., 2007). The survival of our ancestral mothers may have benefited more from a keen memory for the location of edible plants – a legacy that lives today in women’s superior memory for objects and their location.
But experience also matters. One experiment found that playing action video games boosts spatial abilities (Feng et al., 2007). And you probably won’t be surprised to know that among entering American collegians, six times as many men (23 percent) as women (4 percent) report playing video/computer games six or more hours a week (Pryor et al., 2010).
Evolutionary psychologist Steven Pinker (2005) argues that biological as well as social influences appear to affect gender differences in life priorities (women’s greater interest in people versus men’s in money and things), in risk-taking (with men more reckless), and in math reasoning and spatial abilities. Such differences are, he notes, observed across cultures, stable over time, influenced by prenatal hormones, and observed in genetic boys raised as girls. Culturally influenced preferences also help explain women selecting people-rather than math-intensive vocations (Ceci & Williams, 2010, 2011).
Other critics urge us to remember that social expectations and divergent opportunities shape boys’ and girls’ interests and abilities (Crawford et al., 1995; Eccles et al., 1990). Gender-equal cultures, such as Sweden and Iceland, exhibit little of the gender math gap found in gender-unequal cultures, such as Turkey and Korea (Guiso et al., 2008).
Fueling the group-differences debate are two other disturbing but agreed-upon facts:
There are many group differences in average intelligence test scores. New Zealanders of European descent outscore native Maori New Zealanders. Israeli Jews outscore Israeli Arabs. Most Japanese outscore most Burakumin, a stigmatized Japanese minority. Those who can hear outscore those born deaf (Braden, 1994; Steele, 1990; Zeidner, 1990). And White Americans have outscored Black Americans. This Black-White difference has diminished somewhat in recent years, especially among children (Dickens & Flynn, 2006; Nisbett, 2009). Such group differences provide little basis for judging individuals. Worldwide, women outlive men by 4 years, but knowing only that you are male or female won’t tell us much about how long you will live.
We have seen that heredity contributes to individual differences in intelligence. But group differences in a heritable trait may be entirely environmental. Consider one of nature’s experiments: Allow some children to grow up hearing their culture’s dominant language, while others, born deaf, do not. Then give both groups an intelligence test rooted in the dominant language, and (no surprise) those with expertise in that language will score highest. Although individual performance differences may be substantially genetic, the group difference is not (FIGURE 64.2) .
Might the racial gap be similarly environmental? Consider:
Moreover, consider the striking results of a national study that looked back over the mental test performances of White and Black young adults after graduation from college. From eighth grade through the early high school years, the average aptitude score of the White students increased, while that of the Black students decreased – creating a gap that reached its widest point at about the time that high school students like you take college admissions tests. But during college, the Black students’ scores increased “more than four times as much” as those of their White counterparts, thus greatly decreasing the aptitude gap. “It is not surprising,” concluded researcher Joel Myerson and his colleagues (1998), “that as Black and White students complete more grades in high school environments that differ in quality, the gap in cognitive test scores widens. At the college level, however, where Black and White students are exposed to educational environments of comparable quality . . . many Blacks are able to make remarkable gains, closing the gap in test scores.”
If one assumes that race is a meaningful concept, the debate over race differences in intelligence divides into three camps, note Earl Hunt and Jerry Carlson (2007):
Are intelligence tests biased? The answer depends on which of two very different definitions of bias we use.
We consider a test biased if it detects not only innate differences in intelligence but also performance differences caused by cultural experiences. This in fact happened to Eastern European immigrants in the early 1900s. Lacking the experience to answer questions about their new culture, many were classified as feeble-minded.
In this popular sense, intelligence tests are biased. They measure your developed abilities, which reflect, in part, your education and experiences. You may have read examples of intelligence test items that make middle-class assumptions (for example, that a cup goes with a saucer). Do such items bias the test against those who do not use saucers? Could such questions explain racial differences in test performance? If so, are tests a vehicle for discrimination, consigning potentially capable children, some of whom may have a different native language, to dead-end classes and jobs? And could creating culture-neutral questions – such as by assessing people’s ability to learn novel words, sayings, and analogies – enable culture-fair aptitude tests (Fagan & Holland, 2007, 2009)?
Defenders of the existing aptitude tests note that racial group differences persist on nonverbal items, such as counting digits backward (Jensen, 1983, 1998). Moreover, they add, blaming the test for a group’s lower scores is like blaming a messenger for bad news. Why blame the tests for exposing unequal experiences and opportunities? If, because of malnutrition, people were to suffer stunted growth, would you blame the measuring stick that reveals it? If unequal past experiences predict unequal future achievements, a valid aptitude test will detect such inequalities.
The second meaning of bias – its scientific meaning – is different. It hinges on a test’s validity – on whether it predicts future behavior only for some groups of test-takers. For example, if the SAT® exam accurately predicted the college achievement of women but not that of men, then the test would be biased. In this statistical meaning of the term, the near-consensus among psychologists (as summarized by the U.S. National Research Council’s Committee on Ability Testing and the American Psychological Association’s Task Force on Intelligence) is that the major U.S. aptitude tests are not biased (Hunt & Carlson, 2007; Neisser et al., 1996; Wigdor & Garner, 1982). The tests’ predictive validity is roughly the same for women and men, for Blacks and Whites, and for rich and poor. If an intelligence test score of 95 predicts slightly below-average grades, that rough prediction usually applies equally to all.
Throughout this text, we have seen that our expectations and attitudes can influence our perceptions and behaviors, and we find this effect in intelligence testing. When Steven Spencer and his colleagues (1997) gave a difficult math test to equally capable men and women, women did not do as well-except when they had been led to expect that women usually do as well as men on the test. Otherwise, the women apparently felt apprehensive, which affected their performance. With Claude Steele and Joshua Aronson, Spencer (2002) also observed this self-fulfilling stereotype threat with Black students. When reminded of their race just before taking verbal aptitude tests, they performed worse. Follow-up experiments confirm that negatively stereotyped minorities and women may have unrealized academic potential (Nguyen & Ryan, 2008; Walton & Spencer, 2009). If, when taking an exam, you are worried that your type often doesn’t do well, your self-doubts and self-monitoring may hijack your working memory and impair your performance (Schmader, 2010). For such reasons, stereotype threat may also impair attention and learning (Inzlicht & Kang, 2010; Rydell et al., 2010).
Critics note that stereotype threat does not fully account for the Black-White aptitude score difference (Sackett et al., 2004, 2008). But it does help explain why Blacks have scored higher when tested by Blacks than when tested by Whites (Danso & Esses, 2001; Inzlicht & Ben-Zeev, 2000). It gives us insight into why women have scored higher on math tests with no male test-takers present, and why women’s chess play drops sharply when they think they are playing a male opponent (Maass et al., 2008). And it explains “the Obama effect” – the finding that African-American adults performed better if taking a verbal aptitude test administered immediately after watching Barack Obama’s stereotype-defying nomination acceptance speech or just after his 2008 presidential victory (Marx et al., 2009).
Steele (1995, 2010) concludes that telling students they probably won’t succeed (as is sometimes implied by remedial “minority support” programs) functions as a stereotype that can erode performance. Over time, such students may detach their self-esteem from academics and look for recognition elsewhere. Indeed, as African-American boys progress from eighth to twelfth grade, there is a growing disconnect between their grades and their self-esteem and they tend to underachieve (Osborne, 1997). One experiment randomly assigned some African-American seventh-graders to write for 15 minutes about their most important values (Cohen et al., 2006, 2009). That simple exercise in self-affirmation had the apparent effect of boosting their semester grade point average by 0.26 in a first experiment and 0.34 in a replication. Minority students in university programs that challenge them to believe in their potential, or to focus on the idea that intelligence is malleable and not fixed, have likewise produced markedly higher grades and had lower dropout rates (Wilson, 2006).
What, then, can we realistically conclude about aptitude tests and bias? The tests are indeed biased (appropriately so, some would say) in one sense – sensitivity to performance differences caused by cultural experience. But they are not biased in the scientific sense of failing to make valid statistical predictions for different groups.
Bottom line: Are the tests discriminatory? Again, the answer can be Yes or No. In one sense, Yes, their purpose is to discriminate – to distinguish among individuals. In another sense, No, their purpose is to reduce discrimination by reducing reliance on subjective criteria for school and job placement – who you know, what school you’re from, or whether you are the “right kind of person.” Civil service aptitude tests, for example, were devised to discriminate more fairly and objectively by reducing the political, racial, and ethnic discrimination that preceded their use. Banning aptitude tests would lead those who decide on jobs and admissions to rely more on other considerations, such as personal opinion.
Perhaps, then, our goals for tests of mental abilities should be threefold. First, we should realize the benefits Alfred Binet foresaw – to enable schools to recognize who might profit most from early intervention. Second, we must remain alert to Binet’s fear that intelligence test scores may be misinterpreted as literal measures of a person’s worth and potential. Third, we must remember that the competence that general intelligence tests sample is important; it helps enable success in some life paths. But it reflects only one aspect of personal competence. Our practical intelligence and emotional intelligence matter, too, as do other forms of creativity, talent, and character. Because there are many ways of being successful, our differences are variations of human adaptability.
Finally, life’s great achievements result not only from “can do” abilities but also from “will do” motivation. Competence + Diligence --> Accomplishment.