Abstract
Original article: Stoet, G., & Geary, D. C. (2018). The gender-equality paradox in science, technology, engineering, and mathematics education. Psychological Science, 29, 581–593. doi:10.1177/0956797617741719
Since the original article was published, several readers pointed out ambiguities or omissions in our description of aspects of the study. This Corrigendum is correcting those oversights, as well as some related matters. First, the description of the share of women graduating in science, technology, engineering, and mathematics (STEM) was ambiguously formulated.
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The following passage is being added to the end of the “STEM degrees” subsection (p. 584) to provide clarification: The formula used for calculating the propensity of women to graduate with STEM degrees was a/(a + b), where a is the percentage of women who graduate with STEM degrees (relative to all women graduating) and b is the percentage of men who graduate with STEM degrees (relative to all men graduating). Note that the resulting number can be interpreted as the percentage of women in STEM when equal numbers of men and women enroll at university. Further, it should be noted that in several places, we compare the propensity of women to graduate with STEM degrees with the percentage of girls who would be likely to successfully complete STEM study as inferred from the PISA sample. This comparison is permissible because there is always an equal representation of males and females in the PISA data itself (50/50).
In keeping with this terminology, the sentence following this insertion on p. 584 is being changed to “The propensity of women relative to men to graduate with STEM degrees ranged from 12.4 in Macao to 40.7 in Algeria; the median propensity was 25.4.”
Next, the following information is being added to the end of the “Gender Equality” subsection (p. 584) to clarify how data for China were represented: In the analyses, we did not include the GGGI data for China as a whole. We chose to do this because only two municipalities (Beijing and Shanghai) and two provinces (Guangdong and Jiangsu) were used to represent China in the PISA data. These municipalities and provinces are, however, not representative of China as a whole.
Next, the first sentence of the fourth paragraph in the “Sex Differences in Academic Strengths” section (pp. 585–586) is being changed as follows: Finally, it should also be noted that the difference between the percentage of girls with a strength in science or mathematics was always equally large or larger than the propensity of women to graduate with STEM degrees; importantly, this difference was again larger in more gender-equal countries (rs = .41, 95% CI = [.15, .62], n = 50, p = .003).
Three changes will be made to Figure 3b (p. 587). First, the title of the caption is being changed as follows: “Scatterplots (with best-fitting regression lines) showing the relation between gender equality and sex differences in (a) intraindividual science performance and (b) the propensity of women relative to men to graduate with science, technology, engineering, and math (STEM) degrees.” Second, the last sentence of the caption is being corrected to read as follows: “The propensity of women relative to men to graduate with STEM degrees (b) was lower in more gender-equal countries (rs = −.47).” Third, the label on the x-axis of Figure 3b itself is being updated to “Propensity of Women to Graduate With STEM Degrees.”
The third sentence of the third paragraph in the “Science Attitudes and Gender Equality” section (p. 590) is being changed as follows: “Using these ability criteria, we would expect women’s propensity to graduate with STEM degrees to be much higher than men’s.”
Two changes are being made to Figure 5. First, the three x-axis labels are each being changed to “Women’s Propensity to Graduate in STEM.” Second, the caption is being changed as follows: Scatterplots showing the relation between the percentage of female students estimated to choose further science, technology, engineering, and math (STEM) study after secondary education and the propensity of women to graduate in STEM fields in tertiary education. Red lines indicate the estimated (horizontal) and actual (vertical) average values for the variables graphed on each axis. For instance, in (c), we estimated that women would have a propensity of 34 to graduate college with a STEM degree (internationally), but the actual propensity was only 28. Identity lines (i.e., 45° lines) are colored blue; points above the identity lines indicate a lower propensity of women to graduate in STEM fields than expected. Panel (a) displays the percentage of female students estimated to choose STEM study on the basis of ability alone (see the text for criteria). Although there was considerable cross-cultural variation, on average around 50% of students graduating in STEM fields could be women, which deviates considerably from the estimated percentage via the propensity of women to graduate with a STEM degree. The estimate of women STEM students shown in (b) was based on both ability, as in (a), and being above the international median score in science attitudes. The estimate shown in (c) is based on ability, attitudes, and having either mathematics or science as a personal strength. Because there is always an equal representation of males and females in the study from which the propensity data were obtained, the comparison of percentage and propensity is valid.
In the first paragraph of the Discussion section (pp. 590–591), the last sentence is being changed as follows: Further, our analysis suggests that the percentage of girls who would likely be successful and enjoy further STEM study was considerably higher than the propensity of women to graduate in STEM fields, implying that there is a loss of female STEM capacity between secondary and tertiary education.
In addition, the second to last sentence of the second paragraph on p. 581 is being updated as follows: “Yet, paradoxically, Finland has one of the world’s largest gender gaps in college degrees in STEM fields, and Norway and Sweden, also leading in gender-equality rankings, are not far behind.”
To clarify how averages of PISA scores for science, mathematics, and reading were calculated, we are adding the following paragraph to the “Programme for International Student Assessment (PISA)” section (p. 583, following the first full paragraph):
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PISA 2015 provided 10 plausible values for each student’s scores in the science, mathematics, and reading tests. The use of 10 plausible values is different from previous PISA data sets published from 2000 to 2012, in which 5 plausible values were provided for each test. Given that PISA has not updated its documentation or its published statistical macros, we used the traditional approach of using 5 plausible values. Further, we did not include the data for the Dominican Republic, which participated for the first time in PISA in 2015. Additionally, it should be noted that Kosovo was removed from the data because it was regional data.