Abstract
This study examines international data on mathematics and science achievement to illuminate the ways in which macrostructural factors influence gender differences in mathematics, particularly among high achievers. Examining the importance of macrostructural factors for gender differences not only helps us better understand the role that larger contexts play in contributing to these differences, but international variation also provides a unique opportunity to present simple and powerful arguments for the continued importance of social factors vis-à-vis biological considerations. We show that there is considerable international variation in gender differences, and that gender differences among high achievers in both mathematics and science literacy are related to gender inequality in the labor market and differences in the overall status of men and women. We conclude by discussing the implications of our findings for mathematics education and suggesting fruitful avenues for future research.
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Notes
- 1.
It is important to note that this is not the same thing as saying that biological factors are unimportant. As an example, it could be that diet interacts with hormones so that the gender differences we observe are in some sense biological, but importantly, social factors are still central. International variation thus precludes a deterministic biological account, but is eminently compatible with bio-social interactions.
- 2.
For an excellent recent review that synthesizes biological and social factors, see Ceci and Williams (2010b).
- 3.
This raises an interesting question, as it suggests that most of the estimates that we receive about the relative importance of genetic versus environmental influences are from contexts with relatively homogeneous environments, and thus underestimate the amount of variation attributable to the environment. It is possible, for example, that estimates of the genetic and environmental portions of the variance in cognitive abilities might differ if estimated in a context where international environmental differences were able to be taken into account.
- 4.
It is worth noting, however, that due to the correlational nature of this study, even when we are modeling social factors we are examining necessary (but not sufficient) conditions.
- 5.
Mathematics and science literacy scores are used as they are less susceptible to biases arising from country-level differences in curriculum. The mathematics literacy items in TIMSS are designed to assess students’ knowledge, their ability to execute routine and complex procedures, and their problem solving skills in the content areas of number sense, algebraic sense, and measurement and estimation (Martin and Kelly 1996). Science literacy items focus on student’s ability to apply scientific knowledge to real-world questions, and cover content in earth science, life science, and physical science (Mullis et al. 2000).
- 6.
These variables were created using information from 1995 wherever possible, and from the nearest adjacent year when no information was available for 1995. Principle components were estimated using an eigen decomposition of the correlation matrix, and are unrotated.
- 7.
It is worth noting the that the underlying metrics of the variables that go into these factor scores vary, so that we cannot really compare the metrics. Rather, we can compare the effects of moving one standard deviation in the different distributions.
- 8.
Other analyses not reported establish that the variation across countries is statistically significant. It is also worth noting that a more recent study of advanced mathematics students in the final year of secondary school finds that in Lebanon girls outscore boys—though in Sweden, Russia, Slovenia, Iran, and the Philippines boys outscore girls, and the Netherlands, Italy, Norway, and Armenia have no statistically significant differences between boys and girls (Mullis et al. 2009).
- 9.
It is worth mentioning that although males are typically assumed to be more variable in mathematical abilities, Feingold (1994), in a cross-national review, also finds that differences in variability for mathematical and spatial abilities differed across countries, with males being more variable in some countries and females being more variable in others.
- 10.
Other analyses (not shown) find that in this sample countries with greater female representation also tend to have higher degrees of occupational gender segregation in the labor market. It is also worth noting this counterintuitive effect of labor market representation is net of the other factors in the model, and that the sample of countries includes only European and other western countries.
- 11.
In supplementary analyses (not shown) we use simulations to examine how often one would expect to find countries with greater female variability if students were randomly assigned to countries. This was done to address concerns that there might be a relatively high likelihood of observing greater female variation among any given subgroup in the data. We stopped the simulations when, after over 50,000 simulations, we had still failed to find even one country in which boys had a lower variance than girls. Given that our data contain three such countries, we believe that this is unlikely to have arisen due to chance.
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Acknowledgements
Portions of this chapter previously appeared in Penner (2008). We are grateful to Jim Dietz and to the Irvine Comparative Sociology Workshop at the University of California, Irvine for useful comments and discussions.
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Appendix
Appendix
The analyses presented in this study focus on gender differences in the final year of secondary school, allowing us to compare the degree to which national education systems vary in the gender differences that they produce at this key juncture. However, it is also informative to examine how these gender differences diverge from those observed among students earlier in their educational careers. Table A.1 thus augments the results reported above by presenting information on gender differences for 4th, 8th, and 12th grade students for the 13 countries with information on students at all three grade levels. For each grade level we present information on the mean gender difference (OLS), as well as gender differences at the 10th and 90th percentiles, and the p-value from an ANOVA test examining whether the gender differences at the 10th and 90th percentiles are different. Given that the students examined are at the most around eight years apart, we adopt a synthetic cohort approach in discussing these results.
We see that there are substantial differences in both the number of significant coefficients and the magnitude of the coefficients as students progress through school. Looking first at gender differences at the bottom of the distribution, we see that in 4th grade the 10th percentile score of boys was higher than the 10th percentile score of girls in 4 of the countries, and lower in one country (New Zealand). By 8th grade there were no countries where boys had higher 10th percentile scores than girls, and in 2 countries girls had higher 10th percentile scores than boys (Australia and Cyprus). In contrast, by 12th grade, boys had higher 10th percentile scores than girls in 8 of the countries, while girls had higher 10th percentile scores in one country (Hungary). These findings highlight the temporal variability of the results, and suggest that differences at the bottom of the distribution emerge between 8th and 12th grade.
By contrast, differences at the top of the distribution emerge between the 4th and 8th grades—in 4th grade five countries had significant differences favoring boys, by 8th grade there were significant differences favoring boys in 10 countries, and by 12th grade 12 of the 13 countries had gender differences favoring boys at the 90th percentile. However, there were still profound changes in the magnitude of the coefficients between the 8th and 12th grades. Examining the 8th grade results, we see that only one country (Israel) had a male advantage of more than .03 (roughly 3 percent), while by 12th grade, all 12 of the countries with significant differences had male advantages of more than .03. We believe that differences between the results for 4th, 8th, and 12th grade students are important not only for what they reveal about how gender differences emerge over time, but also because they underscore the importance of selecting the appropriate grade level for making international comparisons. In this study, given our interest in comparing the gender differences produced by different educational systems, we believe that it is important to examine students in the final year of secondary school.
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Penner, A.M., CadwalladerOlsker, T. (2012). Gender Differences in Mathematics and Science Achievement Across the Distribution: What International Variation Can Tell Us About the Role of Biology and Society. In: Forgasz, H., Rivera, F. (eds) Towards Equity in Mathematics Education. Advances in Mathematics Education. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27702-3_41
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