Eine interessante Studie zum Gender Equality Paradox, von Gisbjert Stoet und David Geary:
The underrepresentation of girls and women in science, technology, engineering, and mathematics (STEM) fields is a continual concern for social scientists and policymakers. Using an international database on adolescent achievement in science, mathematics, and reading (N =472,242), we showed that girls performed similarly to or better than boys
in science in two of every three countries, and in nearly all countries, more girls appeared capable of college-level STEM study than had enrolled. Paradoxically, the sex differences in the magnitude of relative academic strengths and pursuit of STEM degrees rose with increases in national gender equality. The gap between boys’ science achievement and girls’ reading achievement relative to their mean academic performance was near universal. These sex differences in academic strengths and attitudes toward science correlated with the STEM graduation gap. A mediation analysis suggested that life-quality pressures in less gender-equal countries promote girls’ and women’s engagement with STEM subjects.
Quelle: The Gender-Equality Paradox in Science, Technology, Engineering, and Mathematics Education (Abstract/ Full (Scihub))
Für Leser dieses Blogs nichts neues: Insbesondere die Interessen der Frauen scheinen in eine andere Richtung zu gehen und sie von STEM-Fächern fern zu halten.
Die ersten Grafik zeigt Unterschiede zwischen Jungs und Mädchen in den Naturwissenschaften (Blau), in Mathematik (Grün) und im Lesen (Rot). Die Berechnung erfolgte, indem die Werte der Mädchen von den Werten der Jungs abgezogen wurden, waren die Mädchen also besser, dann schlägt es nach Links aus, waren die Jungs besser, dann nach Rechts.
Unterschiede Wissenschaft Maenner Frauen
Wie man sieht, waren üblicherweise die Mädchen im Lesen besser, aber auch häufiger in Mathe oder Naturwissenschaften, wenn man PISA betrachtet.
Auf der rechten Seite findet sich dann eine Betrachtung, bei der es um die „persönliche Stärken“ ging:
Next, we calculated the percentage of boys and girls who had science, mathematics, or reading as their personal academic strength; this contrasts with the above analysis that focused on the overall magnitude of these strengths independently of whether they were the students’ personal strength. We found that on average (across nations), 24% of girls had science as their strength, 25% of girls had mathematics as their strength, and 51% had reading. The corresponding values for boys were 38% science, 42% mathematics, and 20% reading.
Thus, despite national averages that indicate that boys’ performance was consistently higher in science than that of girls relative to their personal mean across academic areas, there were substantial numbers of girls
within nations who performed relatively better in science than in other areas. Within Finland and Norway, two countries with large overall sex differences in the intraindividual science gap and very high GGGI scores, there were 24% and 18% of girls, respectively, who had science as their personal academic strength, relative to 37% and 46% of boys
Demnach waren Mädchen zwar in vielen Bereichen der Naturwissenschaften oder der Mathematik relativ stark, aber es war nicht ihre persönliche Stärke, vielleicht auch, weil sie eben im Lesen ganz besonders stark waren
Die nächste Grafik vergleicht die Unterschiede zwischen den Geschlechtern bei der Begeisterung für die Naturwissenschaften mit dem Global Gender Gap Index, der insbesondere angeben sollte, inwiefern Frauen in einem Land benachteiligt sind:
Unterschiede wissenschaft Maenner Frauen
Dabei zeigt sich, dass gerade Länder mit sehr großen Unterschieden zwischen den Geschlechtern in dem Interesse für Naturwissenschaften einen hohen Wert bei dem Global Gender Pay Index haben. Ganz oben mit Dabei Länder wie Deutschland, Dänemark, Schweden und Island (das Land mit dem höchsten GGGI).
Die weitere Grafik zeigt in A) wie viele Frauen in der Schule die Befähigung hätten, ein STEM-Fach zu wählen, in B), wie viele die Fähigkeiten und die richtige Einstellung hätten um STEM zu wählen und in C) wie viele die Fähigkeiten und die Einstellung hätten und bei denen die Fähigkeiten auch gerade die persönliche Stärke wären. Und dies im Verhältnis dazu, wie viele Frauen dann tatsächlich in dem Bereich weiter machen.
Unterschiede wissenschaft Maenner Frauen
Hier noch einmal der Erläuterungstext:
Fig. 5. 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 estimated percentage of female STEM graduates in tertiary education. Red lines indicate the estimated (horizontal) and actual (vertical) average graduation percentage of women in STEM fields. For instance, in (c), we estimated that 34% of women would graduate college with a STEM degree (internationally), but only 28% did so. Identity lines (i.e., 45° lines) are colored blue; points above the identity lines indicate fewer women STEM graduates 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 actual percentage of women among STEM graduates. 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
Wie man sieht sinkt der Anteil immer mehr, wenn man mehr Variablen dazu nimmt.
Es wäre interessant, noch einmal die Zahlen für die Jungs daneben zu stellen.
Aus der Studie:
Thus far, we have shown that the sex differences in STEM graduation rates and in science literacy as an academic strength become larger with gains in gender equality and that schools prepare more girls for further STEM study than actually obtain a STEM college degree. We will now consider one of the factors that might explain why the graduation gap may be larger in the more gender-equal countries. Countries with the highest gender equality tend to be welfare states (to varying degrees) with a high level of social security for all its citizens; in contrast, the less gender-equal countries have less secure and more difficult living conditions, likely leading to lower levels of life satisfaction (Pittau et al., 2010). This may in turn influence one’s utility beliefs about the value of science and pursuit of STEM occupations, given that these occupations are relatively high paying and thus provide the economic security that is less certain in countries that are low in gender equality. We used OLS as a measure of overall life circumstances; this is normally distributed and is a good proxy for economic opportunity and hardship and social and personal well-being (Pittau et al., 2010). In more equal countries, overall life satisfaction was higher (rs = .55, 95% CI = [.35, .70], p < .001, n = 62). Accordingly, we tested whether low prospects for a satisfied life may be an incentive for girls to focus more on science in school and, as adults, choose a career in a relatively higher paid STEM field. If our hypothesis is correct, then OLS should at least partially mediate the relation between gender equality and the sex differences in STEM graduation. A formal mediation analysis using a bootstrap method with 5,000 iterations confirmed the mediational model path of life satisfaction for STEM graduation (mean indirect effect = −0.19, SE = 0.08, Sobel’s z = −2.24, p < .025, 95% CI of bootstrapped samples = [−0.39, −0.04]). The effect of the direct path in the mediation model was statistically significant (mean direct effect = −0.34, SE = 0.135, 95% CI of bootstrapped samples = [−0.65, −0.02], p = .038), and the mediation was considered partial (proportion mediated = 0.35, 95% CI = [0.06, 0.95], p = .013; Table S3 in the Supplemental Material). A sensitivity analysis of this mediation (Imai, Keele, & Tingley, 2010; Tingley, Yamamoto, Hirose, Keele, & Imai, 2014) showed the point at which the average causal mediation effect (ACME) was approximately zero (ρ = −0.4, 95% CI = [−0.11, 0.15], R RM Y 2* 2* = 0.16, R R M Y 2 2 = 0.07; Fig. S1 in the Supplemental Material). The latter finding suggests that an unknown third variable may have confounded the mediation model (see Discussion)
Das wäre also eine Erklärung darüber, dass in Ländern mit hohem GGGI üblicherweise auch ein gewisser Wohlstand und eine hohe Sicherheit geboten wird, so dass man eher meint seinen Neigungen nachgehen zu können, während in Ländern mit niedrigeren GGGI diese Sicherheit gerade in Berufen mit gutem Verdienst liegt.
Vielleicht auch nicht zu unterschätzen: Entsprechende Berufe erlauben auch eher das Verlassen des Landes in reichere, modernere Länder mit einem höheren Lebensstandard. Ich kenne einige Osteuropäer (m/w), die hier mit relativ technischen Berufen gute Stellen gefunden haben.
Using the most recent and largest international database on adolescent achievement, we confirmed that girls performed similarly or better than boys on generic science literacy tests in most nations. At the same time, women obtained fewer college degrees in STEM disciplines than men in all assessed nations, although the magnitude of this gap varied considerably. Further, our analysis suggests that the percentage of girls who would likely be successful and enjoy further STEM study was considerably higher than the percentage of women graduating in STEM fields, implying that there is a loss of female STEM capacity between secondary and tertiary education. One of the main findings of this study is that, paradoxically, countries with lower levels of gender equality had relatively more women among STEM graduates than did more gender-equal countries. This is a paradox, because gender-equal countries are those that give girls and women more educational and empowerment opportunities and that generally promote girls’ and women’s engagement in STEM fields (e.g., Williams & Ceci, 2015). In our explanation of this paradox, we focused on decisions that individual students may make and decisions and attitudes that are likely influenced by broader socioeconomic considerations. On the basis of expectancy-value theory (Eccles, 1983; Wang & Degol, 2013), we reasoned that students should at least, in part, base educational decisions on their academic strengths. Independently of absolute levels of performance, boys on average had personal academic strengths in science and mathematics, and girls had strengths in reading comprehension. Thus, even when girls’ absolute science scores were higher than those of boys, as in Finland, boys were often better in science relative to their overall academic average. Similarly, girls might have scored higher than boys in science, but they were often even better in reading. Critically, the magnitude of these sex differences in personal academic strengths and weaknesses was strongly related to national gender equality, with larger differences in more gender-equal nations. These intraindividual differences in turn may contribute, for instance, to parental beliefs that boys are better at science and mathematics than girls (Eccles & Jacobs, 1986; Gunderson, Ramirez, Levine, & Beilock, 2012). We also found that boys often expressed higher selfefficacy, more joy in science, and a broader interest in science than did girls. These differences were also larger in more gender-equal countries and were related to the students’ personal academic strength. We discuss some implications below (Interventions).
Dass Jungs die Naturwissenschaften mehr interessieren als die Mädchen und diese dafür andere Fächer eher interessieren, könnte auch daran liegen, dass es eben einen großen Dinge-Leute-Unterschied in den Interessen gibt.
Dazu noch einmal aus einer anderen Studie:
ocational interests predict educational and career choices, job performance, and career success (Rounds & Su, 2014). Although sex differences in vocational interests have long been observed (Thorndike, 1911), an appropriate overall measure has been lacking from the literature. Using a cross-sectional sample of United States residents aged 14 to 63 who completed the Strong Interest Inventory assessment between 2005 and 2014 (N 1,283,110), I examined sex, age, ethnicity, and year effects on work related interest levels using both multivariate and univariate effect size estimates of individual dimensions (Holland’s Realistic, Investigative, Artistic, Social, Enterprising, and Conventional). Men scored higher on Realistic (d 1.14), Investigative (d .32), Enterprising (d .22), and Conventional (d .23), while women scored higher on Artistic (d .19) and Social (d .38), mostly replicating previous univariate findings. Multivariate, overall sex differences were very large (disattenuated Mahalanobis’ D 1.61; 27% overlap). Interest levels were slightly lower and overall sex differences larger in younger samples. Overall sex differences have narrowed slightly for 18-22 year-olds in more recent samples. Generally very small ethnicity effects included relatively higher Investigative and Enterprising scores for Asians, Indians, and Middle Easterners, lower Realistic scores for Blacks and Native Americans, higher Realistic, Artistic, and Social scores for Pacific Islanders, and lower Conventional scores for Whites. Using Prediger’s (1982) model, women were more interested in people (d 1.01) and ideas (d .18), while men were more interested in things and data. These results, consistent with previous reviews showing large sex differences and small year effects, suggest that large sex differences in work related interests will continue to be observed for decades.
Dann aus der hier besprochenen Studie weiter:
Explanations
We propose that when boys are relatively better in science and mathematics while girls are relatively better at reading than other academic areas, there is the potential for substantive sex differences to emerge in STEM-related educational pathways. The differences are expected on the basis of expectancy-value theory and are consistent with prior research (Eccles, 1983; Wang & Degol, 2013). The differences emerge from a seemingly rational choice to pursue academic paths that are a personal strength, which also seems to be common academic advice given to students, at least in the United Kingdom (e.g., Gardner, 2016; Universities and Colleges Admissions Service, 2015). The greater realization of these potential sex differences in gender-equal nations is the opposite of what some scholars might expect intuitively, but it is consistent with findings for some other cognitive and social sex differences (e.g., Lippa, Collaer, & Peters, 2010; Pinker, 2008; Schmitt, 2015). One possibility is that the liberal mores in these cultures, combined with smaller financial costs of foregoing a STEM path (see below), amplify the influence of intraindividual academic strengths. The result would be the differentiation of the academic foci of girls and boys during secondary education and later in college, and across time, increasing sex differences in science as an academic strength and in graduation with STEM degrees. Whatever the processes that exaggerate these sex differences, they are abated or overridden in less genderequal countries. One potential reason is that a well-paying STEM career may appear to be an investment in a more secure future. In line with this, our mediation analysis suggests that OLS partially explains the relation between gender equality and the STEM graduation gap. Some caution when interpreting this result is needed, though. Mediation analysis depends on a number of assumptions, some of which can be tested using a sensitivity analysis, which we conducted (Imai, Keele, & Yamamoto, 2010). The sensitivity analysis gives an indication of the correlation between the statistical error component in the equations used for predicting the mediator (OLS) and the outcome (STEM graduation gap); this includes the effect of unobserved confounders. Given the range of ρ values in the sensitivity analysis (Fig. S1), it is possible that a third variable could be associated with OLS and the STEM graduation gap. A related limitation is that the sensitivity analysis does not explore confounders that may be related to the predictor variable (i.e., GGGI). Future research that includes more potential confounders is needed, but such data are currently unavailable for many of the countries included in our analysis. Relation to previous studies of gender equality and educational outcomes Our current findings agree with those of previous studies in that sex differences in mathematics and science performance vary strongly between countries, although we also believe that the link between measures of gender equality and these educational gaps (e.g., as demonstrated by Else-Quest, Hyde, & Linn, 2010; Guiso, Monte, Sapienza, & Zingales, 2008; Hyde & Mertz, 2009; Reilly, 2012) can be difficult to determine and is not always found (Ellison & Swanson, 2010; for an in-depth discussion, see Stoet & Geary, 2015). We believe that one factor contributing to these mixed results is the focus on sex differences in absolute performance, as contrasted with sex differences in academic strengths and associated attitudes. As we have shown, if absolute performance, interest, joy, and selfefficacy alone were the basis for choosing a STEM career, we would expect to see more women entering STEM career paths than do so (Fig. 5). It should be noted that there are careers that are not STEM by definition, although they often require STEM skills. For example, university programs related to health and health care (e.g., nursing and medicine) have a majority of women. This may partially explain why even fewer women than we estimated pursue a college degree in STEM fields despite obvious STEM ability and interest. Interventions Our results indicate that achieving the goal of parity in STEM fields will take more than improving girls’ science education and raising overall gender equality. The generally overlooked issue of intraindividual differences in academic competencies and the accompanying influence on one’s expectancies of the value of pursuing one type of career versus another need to be incorporated into approaches for encouraging more women to enter 12 Stoet, Geary the STEM pipeline. In particular, high-achieving girls whose personal academic strength is science or mathematics might be especially responsive to STEM-related interventions. In closing, we are not arguing that sex differences in academic strengths or wider economic and life-risk issues are the only factors that influence the sex difference in the STEM pipeline. We are confirming the importance of the former (Wang et al., 2013) and showing that the extent to which these sex differences manifest varies consistently with wider social factors, including gender equality and life satisfaction. In addition to placing the STEM-related sex differences in broader perspective, the results provide novel insights into how girls’ and women’s participation in STEM might be increased in gender-equal countries.
Der Rat „Wähle Fächer nach deinen Stärken“ ist ja in der Tat etwas, was man Leuten raten würde. Und da würden eben viele Mädchen in der Schule dann beispielsweise eher Sprachen als Leistungskurs wählen oder Deutsch oder andere Fächer, in denen sie solche Stärken besser ausspielen können. Wer aber einen Englisch und Geschichte als Leistungskurs hat, der wird dann üblicherweise nicht Physik studieren, selbst wenn derjenige auch in Physik keine schlechten Noten hatte.
Zudem gibt es eben auch Fächer, die weniger „theoretisch“ sind und in denen man mehr mit Leuten zu tun hat, in denen man ebenfalls bestimmte Fähigkeiten aus dem oben geprüften Bereichen braucht, etwa BWL oder medizinische Fächer.
Das alles mag dazu beitragen, dass Mädchen sich bereits früh für andere Bereiche entscheiden und dort ihre Stärken ausbauen, so dass sie dann später nicht mehr die STEM-Fächer studieren, für die sie sich zudem auch weniger interessieren.
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