Gender and Math: Putting Differences in Perspective

by Meredith Kimball

The construction of gender differences in mathematics achievement is neither simple nor straightforward. For example, the critical question of how large, or small, is this observed difference can be answered in several ways though typically it has been answered using statistical significance. That is, if the probability of a difference of a certain size is low, the researcher makes the judgment that the difference, if found, is large, or at least large enough to be noteworthy. Because statistical significance depends on sample size, two further specific problems arise. If very large samples are used, very small and meaningless differences will be significant; if small samples are used, even medium-sized and perhaps meaningful differences will not be statistically significant.

There remains in Eurocentric cultures a persistent belief that mathematics belongs to the realm of the masculine.

In order to overcome these problems, feminist psychologists have been instrumental in developing and applying a technique called meta-analysis. Ignoring the statistical significance of the findings, the first step in a meta-analysis is to calculate an effect size 2 for each measurement of gender difference. The smallest possible effect size is zero, indicating that the two groups are identical on the particular measurement used. By convention, effect sizes of .20 are considered small, .50 medium, and .80 large (Cohen). Although there is no absolute upper limit to an effect size, in the measurement of human group differences effect sizes of 2.0 to 3.0 are about the largest ones found.

Effect sizes can be expressed in more common sense ways. For example, one national U.S. study of mathematics achievement found an effect size favouring males of .23 (small). This means that 56% of the males and 44% of the females were above the median (mid-point) score for the combined male and female samples. In other words, if one female's score were drawn randomly and compared to one randomly drawn male score, the male's score would be higher 56% of the time. Another study of mathematics achievement found an effect size favouring males of .48 (medium). In this case 62% of males and 38% of females scored above the combined mid point and given two randomly drawn scores, the male's score would be higher 63% of the time (McGraw & Wong).

In the empirical studies of mathematics achievement, two types of measurement have been used: standardized tests and classroom grades. By far the greatest bulk of work has been and continues to be concerned with performance on standardized tests. Janet Hyde and her colleagues (1990a) provide the most thorough review of these studies in a meta-analysis of gender differences. Their examination of over 250 effect sizes yielded a small average effect size of .20 that favoured males. The size of the difference varied considerably depending on the context of the study. For example, in samples of precocious adolescents in special accelerated math programs the difference favouring males was larger (.41).

Les deux sexes et les mathématiques: remettre les différences en perspective

par Meredith Kimball

La théorie selon laquelle les femmes sont moins douées que les hommes en maths a servi à expliquer leur faible participation dans le secteur des sciences physiques et de l'ingénierie. Pourtant, des preuves empiriques, accumulées à la suite d'une vingtaine d'années d'excellentes recherches féministes, ont montré que les filles sont aussi bonnes, voire meilleures, en maths que les garçons et que les différences existent plus entre les cultures et les classes sociales qu'entre les sexes. Toutefois, ces recherches n'ont guère réussi à remettre en question la supériorité des hommes en maths.

Dans la documentation, on retrouve constamment le même parti pris, à savoir que les aptitudes et les compétences des hommes en maths sont supérieures à celles des femmes. Ainsi, on accorde une importance disproportionnée aux tests standardisés, car les garçons ont tendance à y obtenir de bonnes notes, alors que les filles ont de meilleures notes en classe. De toute évidence, démontrer une similitude empirique entre les hommes et les femmes ne suffit pas. Nous devons aussi nous efforcer de modifier la masculinisation symbolique des mathématiques et de mettre en application ces changements pendant les cours. L'élaboration de programmes d'études et de systèmes d'évaluation tenant compte des deux sexes marquerait un pas dans la bonne direction.

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