Eine interessante Studie zu der Wahl von Studiengängen:
This article examines whether gender differences in preferences for field of study characteristics can explain gendered major choice. Specifically, this study focuses on a broad range of subject characteristics that are often simultaneously present: systemizing skills required (math intensity, reasoning style, affinity for technical work tasks), future job characteristics corresponding with the male breadwinner model (materialism, work–family compatibility), and characteristics invoked by behavioural preferences (risky situations and a competitive environment). To disentangle these co-occurring characteristics and minimize the influence of other factors in the decision-making process (e.g. admission likelihood), this study uses a choice experiment incorporated in the Swiss panel study TREE. In it, a representative sample of high school students choose their preferred field of study from two artificial fields with varying characteristics. The results show the largest gender differences in preferences for characteristics related to reasoning style (abstract versus creative) and affinity for work tasks (technical versus social), and smaller differences for math intensity, competitive climate, and work–family compatibility, while there are no gender differences in preferences for materialistic characteristics (salary and prestige). Unexpectedly, the gender differences are primarily caused by female students’ preferences, while male students are neutral towards most characteristics.
Quelle: Women’s aversion to majors that (seemingly) require systemizing skills causes gendered field of study choice
Aus den Fragen:
Es wird also abgefragt, ob man viel mit Mathematik zu tun hat und ob man kreativ arbeitet oder eher analytisch und in Systemen denken muss. Zudem ob der Wettbewerb unter den Studenten hoch ist oder nicht. Da wäre es interessant die Frage auf den Job zu erweitern, wobei das ja immer schwer zu sagen ist, da die meisten Felder ganz verschiedene Jobs haben. Vielleicht geht es darum in wie weit diese um spätere gute Jobs konkurrieren oder statt dessen jeder irgendwie eine 1 bekommt.
Dann geht es darum wie leicht man damit einen Job bekommt, ob man bei seiner professionellen Arbeit eher Mitgefühl und soziale Fähigkeiten einsetzt oder seine Affinität zu Technik und Technologie.
Das Gehalt wird sehr grob angesprochen: Mit „über dem Durchschnitt“ und „unter dem Durchschnitt“, was aus meiner Sicht das Ergebnis vergleichsweise uninteressant macht. Natürlich will jeder lieber gut bezahlt werden. Die Frage ist aber, was er dafür machen muss.
Dann wird der Status des Berufs abgefragt und die „ob eine Arbeitsbelastung unter 60% möglich ist und zwar mit den Antwortmöglichkeiten „meistens möglich“ und „kaum möglich“. Da wäre auch eine Frage nach Überstunden interessant gewesen.
Dann werden diese Angaben noch einmal eingeordnet:
„Breadwinner model“ ist eine interessant Bezeichnung, wobei man am Prestige ja sieht, dass es um mehr geht, nämlich auch Ansehen in der Gesellschaft und damit auch etwas, was eher in den Bereich intrasexuelle Konkurrenz unter Männern hineinspielt.
Ob man bei den Verhaltenspräferenzen wirklich von „Risk-seeking“ ausgehen kann, weil schlechte Jobchancen bestehen ist interessant. Wer meint, dass er später eh aussetzt, der brauch auch weniger einen Beruf. Wer meint, dass er wenn er kein Einkommen hat, beim anderen Geschlecht schlecht ankommt wird vielleicht eher einen Bereich nehmen, in dem die Jobeigenschaften besser sind. Oder er wird sich vornehmen in dem Bereich so gut zu sein, dass er jedenfalls einen Job findet.
Dann zu den Ergebnissen:
Ich habe es zur besseren Sichtbarkeit noch einmal geteilt:
Frauen wollen sehr eindeutig lieber „people Skills“ und auch lieber Teilzeit. Sie wollen weniger Konkurrenz und eher creatives Denken.
IN
Insgesamt relativ kleine Unterschiede in sehr vielen Bereichen, aber aufgrund der breiten Frage ja auch schwierig.
Aus der Studie:
To go into detail, this section starts with the systemizing skills dimension and whether these characteristics matter more for men (Hypotheses 1). The analyses show that the average male student in the hypothetical subject choice situation is 11 percentage points (P = 0.005) more likely to choose a math-intensive subject compared to a non-math-intensive field. In contrast, the average female student is neutral toward this characteristic (P = 0.92). Regarding reasoning style, women are 15 percentage points more likely to choose the subject requiring associative reasoning (instead of analytical reasoning, P < 0.001), while men do not consider this subject characteristic in their choice (P = 0.16). Finally, gender-specific work tasks do not affect male students’ choice (P = 0.77), while women are 25 percentage points less likely (P < 0.001) to choose a field of study that prepares for occupations requiring technical skills rather than a field that prepares for occupations requiring social skills. All gender differences for systemizing characteristics are significant: Affinity for gender-specific work tasks displays the largest difference between men and women (24 percentage points, P < 0.001), followed by thinking style preference (21 percentage points, P < 0.001),11 and preference for math-intense subjects (11 percentage points, P = 0.0303). Summarizing, Hypothesis 1 is confirmed: The results overwhelmingly show that women dislike systemizing characteristics, whereas men are neutral toward these attributes in the experiment (except for men’s clear preference for mathematics and women’s neutral stance).
In a next step, we examine the effect of job characteristics that correspond to the male breadwinner model. The analysis shows that both genders prefer fields of study that prepare for jobs with higher materialistic returns (preference for above vs. below average salary: men = 29 percentage points, P < 0.001; women = 20 percentage points, P < 0.001; preference for high vs. average prestige: men = 10 percentage points, P = 0.009; women = 10 percentage points, P = 0.001). Figure 2 also suggests that young women might anticipate potential work–family conflicts, as they show an aversion to fields that prepare for jobs requiring full-time employment (instead of jobs with part-time work opportunities, probability = 11 percentage points, P < 0.001), in contrast to men (P = 0.71). However, significant gender differences can only be found in preference for full-time work (10 percentage points, P = 0.042), while there is no significant gender difference for materialistic job characteristics (difference in salary: P = 0.08; difference in prestige: P = 0.99). In summary, the results only partially support Hypothesis 2 that women and men differ in their preferences for job characteristics corresponding with the male breadwinner model. Contrary to the hypothesis, both genders show a clear preference for materialistic job characteristics. Consistent with the hypothesis, 18-year-old women already seem to anticipate potential work–family conflicts and thus prefer fields that prepare them for jobs with the possibility of part-time work.
Finally, regarding behavioural preferences, Hypothesis 3 assumed that men would show a higher preference for both risk and competition. However, neither men nor women react to subject characteristics associated with risk (Pmen = 0.44; Pwomen = 0.30). In contrast, female respondents are 14 percentage points less likely to choose an artificial field of study in which competition among students is high (instead of low, P < 0.001), while men are neutral (P = 0.59). The gender difference in preference for competition is significant (difference = 12 percentage points, P = 0.0137). Therefore, the hypotheses concerning behavioural preferences is only partially supported: There is no gender difference in preference for risky job environments, while women dislike competitive features in fields of study (in contrast to men who are neutral towards this characteristic).
Einen Job zu wählen, bei dem man schlechte Jobaussichten hat, ist eben auch keine reine Risikosache. Es könnte auch einfach darauf hinweisen, dass der Bereich schwer verwertbar ist. Risiko wäre es zB einen Bereich zu studieren, in dem man schwer einen Job findet, aber wenn man einen findet, dann wird er richtig gut bezahlt. Dann gäbe es einen Grund etwas zu riskieren. Aber einen Bereich zu studieren, in dem es keine Jobs gibt, kann auch einfach dumm sein und nicht risikofreudig. Zumal „ein Job bekommen“ auch wenig aussagt.
Und weiter:
The analyses show that beliefs about required reasoning styles and work task affinity are the main drivers of gender segregation in fields of study. While these characteristics do not affect men’s choice in the experiment, they are the strongest predictor of women’s choices, resulting in the largest gender differences in the experiment. This confirms previous findings on the importance of communal goals for women’s STEM career choice (e.g. Diekman et al., 2010; Cech, 2013, Su and Rounds, 2015; Ochsenfeld, 2016; Simon, Wagner and Killion, 2017). Interestingly, the math intensity of a subject is not relevant for a women’s hypothetical choice (as opposed to men’s). Another unexpected result is the lack of gender differences in preferences for materialistic benefits—both men and women value high prestige and above average salary. However, unlike men, women have a high preference for subjects that prepare for jobs with a high work–family compatibility (confirming previous research, e.g., Weisgram and Diekman, 2017) and no competitive climate (see, e.g., Buser, Peter and Wolter, 2017), although, the gender difference in these preferences is rather small compared to the large gender difference in required reasoning styles and work tasks affinity.
Das bestimmte Unterschiede klein sind liegt hier auch an der Art der Frage. Man könnte dort denke ich weit aus höhere Unterschiede finden, wenn man diese anders formuliert.
Overall, these results contribute to the study of gendered subject choice by connecting previous research findings based on different methodological approaches. First, the article’s finding that reasoning styles and affinity towards work tasks exhibit the largest gender differences reflects Barone’s (2011) finding that gender segregation in fields of study is primarily due to women and men sorting into subjects along a humanistic–scientific and a care–technical divide. Second, the results also explain why gender differences in mathematical ability and grades explain only a small part of women’s lower preference for male-dominated STEM fields (e.g. Ceci, Williams and Barnett, 2009; Morgan, Gelbgiser and Weeden, 2013; Barone and Assirelli 2020). While the math-intensity of a subject is irrelevant for women’s choice in the experiment, they avoid field of study characteristics with strong gender stereotypical connotations—logical thinking and an affinity of technical tasks, both characteristics being clearly associated with STEM fields. In line with previous research (e.g. Correll, 2001; Cech, 2013) and without having tested this assumption, I speculate that women’s actual math ability might not be sufficient to overcome their stereotyped beliefs about their systemizing ability. Future research should investigate this question in more detail.
Oder Männer und Frauen denke einfach lieber auf bestimmte Weise. Einfach weil ihre Gehirne anders ausgerichtet sind.
Even though this article has provided new evidence for several open research questions, some points remain to be discussed. First, female students’ decision-making process is much more influenced by the subject characteristics presented in the experiment compared to male students. Whether female students react more strongly to stereotypical expectations or whether male students’ choices depend primarily on the field of study characteristics that were not assessed in this experiment remains an open question and should be explored in more detail in the future. Second, the experiment only measures respondents’ preferences in a hypothetical choice situation. While previous choice experiments on a similar topic have demonstrated strong external validity (Reuben, Wiswall and Zafar, 2017), and our own analysis based on the field of study plans supports this finding, investigating final subject choices would be a valuable addition to this analysis. Third, the experiment combined the dimension of analytical and creative reasoning style and of technical and social skills for theoretical and methodological reasons. Due to the importance of these factors in the results, it would be valuable to differentiate between these preferences and investigate whether women genuinely prefer social work tasks and creative thinking, or whether they simply have an aversion to technical work tasks and analytical thinking. Finally, it must be stressed that the experiment focused on the influence of field of study characteristics on the field of study choice. Therefore, this article does not inform on other likely factors such as parental preferences for specific subjects or peer effects.
Ja, Fragen zu beantworten ist immer etwas anderes als die tatsächlichen Entscheidungen treffen zu müssen. Und weil das sehr subjektive innere Vorgänge sind wird es auch schwierig sein den genauen Entscheidungsprozess jeweils zu ermitteln, da das ja auch letztendlich eine Selbstauskunft wäre.
The results of this study have two important policy implications. First, the experiment revealed that job characteristics play a role in the choice of field of study. Therefore, higher financial rewards in female-dominated occupations might attract more men, while STEM occupations might attract more women if they offer jobs with a good work-life balance. Second, the experiment showed that the largest gender differences in subject preferences emerge in the stereotypical notions of required thinking skills and affinity for work tasks. Therefore, it is necessary to change the common perception that associates certain subject characteristics exclusively with male- or female-dominated subjects, as already suggested by Cheryan (2012). For example, male-dominated engineering also requires creative thinking and social skills (i.e. developing new products as part of a team), while female-dominated psychology also requires analytical thinking and technical skills (i.e. setting up and analyzing eye-tracking experiments). Thus, an easy to implement recommendation is that universities not only continue to provide role models and advertise male- and female-dominated subjects as gender-inclusive, but also specifically could try to reframe the content of their subjects, thereby encouraging equally qualified women to choose currently male-dominated subjects (and vice-versa).
Es ist die Frage, ob man das einfach drehen kann. Natürlich werden technische Aufgaben auch gewisse Kreativität erfordern und soziale Fähigkeiten. Aber ein Ingenieur wird eben bei beruflichen Gesprächen weniger emotionale Themen haben als ein Lehrer, ein Sozialarbeiter, eine Krankenschwester.
vgl auch:
Gefällt mir:
Like Wird geladen …
Alles Evolution 