Are Stereotypes True?

                               
 


Are African Americans really better at basketball than Caucasians? Are blonds really dumber than brunettes? Are women really worse at math than men? The short answer is yes. The longer answer is no. Let me explain by focusing on the stereotype that women can’t do math. At first glance, this stereotype seems to be true. For instance, men continue to outperform women on the math sections of the SAT and GRE, and men outnumber women in college math courses and math-related jobs. Surely this is evidence that women are not as good at math as men. But as this article will explain stereotypes are self-perpetuating and not only reflect but also cause performance differences between groups.

For instance, if the stereotype that women are worse than men at math reflects true group differences, then women should always score worse than men on a math test, no matter how the test is presented. However, this is not the case. Spencer, Steele, and Quinn, (1999) found that when a math test was described as showing no previous gender differences in performance, women performed as well as men. When the test did not include this description, men outperformed women, implying that the stereotype itself causes stereotypic behavior. In fact, stereotypes reinforce stereotypical behavior by way of two psychological phenomena.

The first phenomenon, which was briefly referred to by Regenberg (2007) on her article in In-Mind on the question whether blonds are really dumb, is called Stereotype Threat. Stereotype threat occurs when someone feels threatened by the possibility of confirming a negative stereotype about their group (Steele, 1997). Ironically, this concern leads to decreased performance, which in turn confirms the stereotype that the person was hoping to avoid. An example of stereotype threat is a when a woman, who considers herself good at math but is aware of the stereotype that women can’t do math, takes a difficult math test. When she encounters difficult questions and experiences frustration, she doesn’t want others to think she is struggling because she is a woman. She feels increased pressure to perform well, which actually works against her and makes her perform worse.

The second phenomenon that reinforces stereotypic behavior is called Stereotype Lift . Walton and Cohen (2003) found that men’s scores were higher on math tests that were described as showing previous gender differences in performance compared to tests that were described as showing no previous gender differences. In other words, men experienced a boost in performance when gender stereotypes were relevant to the situation, compared to when they were irrelevant. Downward Social Comparison , a process whereby people elevate their self-esteem by comparing their group to a lower-status group, is thought to be the basis for this lift in performance (Wills , 1981). Men are able to boost their self-esteem and improve their math performance by comparing themselves to women, who are stereotypically believed to be worse at math than men. They may think to themselves, this test is difficult but at least I know I am better at math than women. However, when stereotypes are made irrelevant to the given test, men are no longer able to use this line of thinking to boost their self-esteem.

Together, decreases in women’s scores caused by stereotype threat combined with increases in men’s scores caused by Stereotype Lift work together to exaggerate the performance differences found between men and women in math domains. These exaggerated performance differences confirm and perpetuate the stereotype that women can’t do math, and the cycle of stereotype threat and Stereotype Lift continues with even greater fervor. It is a vicious cycle: the stereotype causes gender differences in math performance; these differences in performance confirm the stereotype; the stereotype grows stronger and is even more likely to produce exaggerated group differences. As a result of the seemingly inevitability of male superiority in math domains, fewer women enter math-related jobs and college courses because they view their efforts as futile. They may think to themselves, what’s the use of pursuing a career in math if I’m not genetically wired to do math and will always be outperformed by my male colleagues? Instead, they may seek self-worth from success in areas in which women are not negatively stereotyped. The small number of women in math-related fields further confirms and perpetuates the stereotype. It seems to be a never-ending cycle, but the negative effects of stereotypes are not inevitable. First, certain factors must be present for stereotype threat to occur, and second, the negative effects of stereotype threat can be prevented.

In an extensive review, Steele (1997) indicated several factors that need to be present for stereotype threat to have negative effects on performance. First, negative stereotypes about a group must exist for the testing domain, as in the example of women and math used above. Other domains include African Americans and intelligence, the elderly and memory, and women and driving, among others. Second, the stereotype must be relevant to the situation. For example, an African American man will be concerned about confirming the stereotype that African Americans are less intelligent than Caucasians while taking the SAT, but not while playing basketball. Thus, stereotype threat may affect his performance while taking the SAT but not while playing basketball. Third, the testing domain must be difficult enough so that poor performance is likely and could be perceived as stereotype confirming. If a memory test is too easy, an elderly person will not experience frustration and doubt, and thus will not be concerned with confirming the stereotype that the elderly have worse memories than younger people. Fourth, stereotype threat is most harmful to people who identify with the domain and consider it important to their self-worth. For instance, women who gain their self-worth from success in math will be most concerned with being viewed as confirming the negative stereotype. Women who do not gain their self-worth from math will not be as concerned about performing poorly in that domain. As another example, Danica Patrick, a NASCAR racer, would be more concerned about performing poorly in the domain of driving and confirming the stereotype that women can’t drive than Mary Lou Retton, a famous gymnast. Finally, the stereotype must be prevalent and widely known. If a person does not know about the stereotype, they will not be concerned with confirming the stereotype. When the above factors are present, stereotype threat and its negative consequences are likely to ensue.

However, numerous interventions have been developed to eliminate the negative effects of stereotypes. One such intervention involves preventing the activation of stereotypes. Steele and Aronson (1995) found that when they primed racial stereotypes by having participants indicate their race before taking an intelligence test, African Americans performed worse than when they were not primed . This implies that collecting demographic information at the end of a test rather than at the beginning could reduce stereotype threat . Focusing on similarities between groups rather than differences could also prevent the activation of stereotypes. Rosenthal and Crisp (2006) found that women performed better on a math test when they thought about similarities between men and women rather than differences before taking the test. Avoiding categorical labels in the classroom is another way to reduce the salience of group differences. For instance, using gender functionally in the classroom (e.g., the teacher saying, “Good morning boys and girls”; “Girls line up at the door first and then the boys”; “Boys take a bathroom break first, and then the girls”) leads children to focus on gender differences rather than similarities and increases the use of stereotypes among children (Patterson & Bigler, 2006). Instead, teachers can divide students into groups for daily activities based on malleable characteristics that change every day such as shoe type or clothing color. Furthermore, providing successful role models and pointing out group achievements increases performance among negatively stereotyped groups by breaking down the barriers of stereotypes and deeming them irrelevant (Marx & Roman, 2002; McIntyre, Paulson, & Lord, 2003). It is for this reason that it is especially important to include the achievements of women and minorities in textbooks of math, science, history, etc., so students can be exposed to these successful role models.

While stereotypes seem to be confirmed in the real world, it is important to realize that stereotypes themselves contribute to this confirmation. This realization is an important first step towards preventing the consequences of those stereotypes. With this understanding and with the help of interventions, such as those mentioned above, the vicious cycle of negative stereotypes can be broken.

Glossary

A Stereotype Threat is a situational threat whereby an individual is concerned with being viewed as conforming to a negative stereotype associated with their group ( Steele, 1997).

Stereotype Lift is a boost in performance caused by comparing oneself to a negatively stereotyped group (Walton & Cohen, 2003).

Downward Social Comparison is a process whereby people elevate their self-esteem by comparing their group to a lower-status group ( Wills, 1981).

Priming is activating thoughts about a particular subject, topic, or object.

References

Marx, D. M., & Roman, J. S. (2002). Female role models: Protecting women’s math test performance. Personality and Social Psychology Bulletin, 28(9), 1183-1193.

McIntyre, R. B., Paulson, R. M., & Lord, C. G. (2003). Alleviating women’s mathematics stereotype threat through salience of group achievements. Journal of Experimental Social Psychology, 39, 83-90.

Patterson, M. M., & Bigler, R. S. (2006). Preschool children's attention to environmental messages about groups: Social categorization and the origins of intergroup bias. Child Development, 77(4), 847-860.

Regenberg, N. (2007). Are blonds really dumb? Inquisitve Mind, 3.

Rosenthal, H. E. S., & Crisp, R. J. (2006). Reducing stereotype threat by blurring intergroup boundaries. Personality and Social Psychology Bulletin, 32(4), 501-511.

Spencer, S. J., Steele, C. M., & Quinn, D. M. (1999). http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6WJB-45K10P5-D... " target="_blank">Stereotype threat and women's math performance. Journal of Experimental Social Psychology, 35(1), 4-28.

Steele, C. M. (1997). A threat in the air: How stereotypes shape intellectual identity and performance. American Psychologist, 52(6), 613-629.

Steele, C. M., & Aronson, J. (1995). Stereotype threat and the intellectual test performance of African Americans. Journal of Personality and Social Psychology, 69(5), 797-811.

Walton, G. M., & Cohen, G. L. (2003). Stereotype lift. Journal of Experimental Social Psychology, 39(5), 456-467.

Wills, T. A. (1981). Downward comparison principles in social psychology. Psychological Bulletin, 90(2), 245-271.

Jessica Cundiff

Jessica Cundiff received her bachelor’s degree in psychology at the University of Texas at Austin. She is currently pursuing a PhD in social psychology at Penn State University. Her research focuses on the social mechanisms involved in perpetuating and maintaining inequality (e.g., power, stereotypes, prejudice, and discrimination), and ways to eliminate social inequities and promote social change.