AWM Book Review

Why So Slow? The Advancement of Women

Virginia Valian, MIT Press, Cambridge 1998. xiv+401pp. ISBN 0-262-22054-7 (cloth), $33; ISBN 0-262-72031-0 (paper), $17.50.

From: AWM Newsletter, May/June 1999.

Reviewed by: Cathy Kessel, EMST, Tolman Hall #1670, University of California, Berkeley, CA 94720-1670; email: kessel@soe.berkeley.edu.

In discussing tenure, Ms. Mentor ([1]) remarks parenthetically, "It should be no surprise -- though it is galling -- that academic men are often rated more highly than academic women, and paid better, for the same work." Ms. Mentor's readers might not be surprised, but the academic person in the street may be. Moreover, that academic person (being an academic) might like some empirical evidence. Virginia Valian's book Why So Slow? provides this -- and a theoretical framework in which to think about it.

Valian is a professor of psychology and linguistics, so it is not surprising that her book focuses on the individual and psychological, rather than the cultural and social. The evidence comes in two categories: psychological experiments and statistics concerning performance ratings, studies of women and men matched for various attributes, and so on. A 1975 example of the former: Make up some résumés, put men's names on some and women's names on the others. Send to 147 heads of psychology departments with a request to rank the "candidates" according to the professorial rank at which they should be hired. Rotate names so that each résumé sometimes gets a woman's name and sometimes a man's. Result: Résumés with men's names are assigned the rank of associate professor. When the same résumés carried women's names they are assigned the rank of assistant professor.

Valian's framework for thinking about this kind of phenomenon uses gender schemas and role schemas. Schema is a term used in cognitive science to denote an individual's mental construct affecting that person's perceptions. One's gender schema, for example, affects the way in which one perceives the behavior and attributes of women and men. Valian describes a 1991 experiment in which college students were shown photographs of women and men and asked to estimate their heights in feet and inches. The photographs always included a reference object such as a desk or doorway. Result: Although the men and women in the photographs were matched for height, in general, the average estimated height for men was greater than that for women. Valian's explanation in terms of gender schemas: On average, men are taller than women. Frequent experience of that fact helps to create the schema that men are, on average, taller than women, which is then applied to the particular men or women we are looking at.

Role schemas are mental constructs about the behavior and attributes of people in particular roles, for example, that of professor. Interactions between gender and role schemas explain the result mentioned by Ms. Mentor as well as some of the findings on student ratings discussed by Neal Koblitz ([2]); for instance: Kierstead et al.'s conclusion that "Taken as a whole, [our] results suggest that if female instructors want to obtain high student ratings, they must not only be highly competent . . . but also careful to act in accordance with traditional sex role expectations."

Not only do gender and role schemas interact, but the way in which they interact depends on context. Some interesting (and cheering) experimental and statistical findings (pp. 141-142) suggest that women tend to fare better when there are more women around, either in hiring situations when there is a larger percentage of women in the applicant pool or in tenure situations when there are more women in the department.

Analysis in terms of gender and role schemas works well to explain and predict differences in estimating the heights of males and females or ranking résumés. It works less well in explaining educational outcomes such as, for example, the well-publicized gender gap in scores on the mathematics section of the SAT discussed in Chapter 5 (suggestively entitled "Biology and Cognition," although Valian is careful to say that hormonal differences appear to play a small role). I'd like to suggest this kind of phenomenon needs to be analyzed with methods outside as well as inside psychology. Studies of curricula that are different from those usually used in the United States suggest alternative explanations.

Gallagher and De Lisi ([3]) analysed the strategies used by a group of students who received high scores on the mathematics section of the SAT (SAT-M) and found that females tended to use conventional school-taught algorithms (which tend to be more time-consuming) and males tended to use short-cuts not taught in school. Valian discusses this work, and interestingly, gives an account of how she approached one of the problems that the students were given. She looked at the problem, looked at the answers, saw how she could avoid further calculations, and felt vaguely as if she were cheating (p. 92). She recounts a similiar experience of "cheating" in grade school where she had figured out that she could do subtraction by adding (e.g., to subtract 14 from 21 she would ask herself what number added to 14 would give 21).

Could it be that Valian's grade school teachers focused, as did many U.S. textbooks, on the "take-away" interpretation of subtraction? ([4]) And could it be that her teachers focused on the usual "borrowing" algorithm for subtraction? A study of third and fourth graders' multidigit subtraction strategies found, consistent with other studies of elementary students' arithmetic, that significantly more boys used a strategy not taught in school -- in this case, regrouping ([5]). Each boy explained to the interviewer that he had learned this strategy from a brother, uncle, or father in the context of an activity such as measuring wood for carpentry, measuring wire to be laid down in the house, or homework problems.

If Valian had gone to school in China, she would have learned many different ways of regrouping as well as the usual algorithm. She would have learned several interpretations of subtraction including "take-away" ([6]), and the idea that addition and subtraction are inverse operations would have been an explicit part of the curriculum ([7]). Teachers would have emphasized the practice of solving a problem in multiple ways and knowing why the different solution methods were correct ([8]). These differences in curriculum and instructional practices suggest an explanation for the finding that a group of Chinese students had no gender differences in their SAT-M scores ([9]). Differences in curriculum and instruction also suggest an explanation for the finding that a group of U.S. students in the Interactive Mathematics Program had no gender differences in their SAT-M scores ([10]). But cognitive psychologists tend not to examine curriculum and instruction -- and I suggest that this is an example of why psychology does not tell us all that we need to know about gender differences in educational outcomes.

In my view, the whole story of why women's progress in academe has been so slow requires (at least) the viewpoints of education, history, and anthropology offered by, for instance, Elizabeth Fennema and Gilah Leder's Mathematics and Gender; Margaret Rossiter's Women Scientists in America: Struggles and Strategies to 1940 and Women Scientists in America: Before Affirmative Action, 1940-1972; Elaine Seymour and Nancy Hewitt's Talking About Leaving; and Nadya Aisenberg and Mona Harrington's Women of Academe.

Although it may not tell us everything, Why So Slow? offers us a framework that explains an important part of the story. Valian points out (p. 166) that professional women "face a cruel set of choices: make an accurate intellectual evaluation of the situation and feel helpless; or make an inaccurate evaluation and feel in control." She offers a third option: "learn how gender schemas work, recognize instances of disadvantage, and develop methods of correcting imbalances. Knowledge is power."

References

1. Toth, Emily. 1997. Ms. Mentor's impeccable advice for women in academia. University of Pennsylvania Press, Philadelphia, p. 176. Reviewed in the AWM Newsletter, 28(2), 23-24.

2. Koblitz, Neal. 1990. Are student ratings unfair to women? AWM Newsletter 20(5) 17-19.

3. Gallagher, Ann M. & De Lisi, R. 1994. Gender differences in Scholastic Aptitude Test-Mathematics problem solving among high-ability students. Journal of Educational Psychology_

4. Fuson, Karen C. 1992. Research on whole number addition and subtraction. In Douglas A. Grouws (Ed.), Handbook of research on teaching and learning. New York: Macmillan, pp. 243-275.

5. Butler, Lisa. 1999. Gender differences in children's arithmetical problem solving procedures. Unpublished M.A. thesis, University of California, Los Angeles.

6. Fuson (see note 4).

7. Ma, Liping. 1999. Knowing and teaching elementary mathematics: Teachers' understanding of fundamental mathematics in China and the United States. Mahwah, NJ: Lawrence Erlbaum Associates.

8. Ma, p. 111 (see note 7).

9. Byrnes, James, Hong, Li, & Xing, Shaoying. 1997. Gender differences on the math subtest of the Scholastic Aptitude Test may be culture-specific. Educational Studies in Mathematics 34, 49-66.

10. Evaluation Updat. Issue number 1, 1995. Available from: Interactive Mathematics Program, 2420 Van Leyden Way, Modesto, CA 95356.