Social networks and favourite subjects: can friends explain gender differences in STEM preferences?
Isabel Raabe | University of Oxford | United Kingdom
Considering the higher pay and prestige of jobs in Science, Technology, Engineering, and Maths (STEM), the under- or overrepresentation of sub-groups of the population contribute to social and economic inequality. Relevant demographic characteristics in this regard are both gender and ethnicity, with gender having received more academic attention. Despite that fact that the traditional gender gap in educational attainment has been reversed and boys and girls perform almost equally in Maths, women are still underrepresented in STEM occupations. A popular argument is the so-called “leaky pipeline” which proposes that girls, over their educational career, drop out of a STEM career trajectory. Indeed, many studies document different tendencies in aspirations (instead of actual performance) based on gender. Therefore, it is crucial to understand how these different patterns of aspirations come about.
Gender differences in aspirations can be explained by a variety of factors, of which peer effects in school are particularly important. However, studying peer effects has been methodologically problematic, especially in terms of separating between selection and influence effects. While social network models are appropriate for such analyses, their results often lack statistical power due to their classroom-level focus. In our study, we address and aim to rectify this by applying multilevel analysis of classroom-level social networks. For this, we analyse the co-evolution of friendship and two-mode favourite-subject networks applying the random-coefficient multilevel SAOM framework. We use a two-wave dataset of 251 Swedish classrooms collected as part of the Children of Immigrants Longitudinal Survey in Four European Countries (CILS4EU).
Peer Effects in Online Mentoring – A Longitudinal Network Analysis
Manuel Hopp | Friedrich-Alexander University of Erlangen-Nuremberg | Germany
Longitudinal communication network and evaluation data was analyzed to get a better understanding of the interplay between peer influence and the outcome of online mentoring. These data contain detailed information about the communication networks and relevant outcome variables of mentees participating in the online mentoring program CyberMentor. To distinguish selection and influence processes a method (Simulation Investigation for Empirical Network Analyses, SIENA) was used in which network formation and changes in the outcome variables are simulated simultaneously within the context of other network processes.
The online mentoring program CyberMentor has the goal of effecting a lasting increase in the participation of girls in STEM (Science, technology, engineering and mathematics). In this program, more than 600 12- to 18-year-old girls are assigned a personal female mentor each year. The mentors are all STEM experts, either graduate students doing advanced degree work in STEM or university-educated professionals with careers in a STEM field. Mentors communicate with their respective mentees on a weekly basis for at least one year. Together, mentors and mentees discuss interesting STEM topics and work on joint projects. Communication takes place on a secure web-based community platform with internal email, chat, and forum systems. Several outcome variables (among others: interest in STEM, elective intentions, certainty about carrier goals, anxiety towards STEM) are assessed by online questionnaires in three points in time (beginning of mentoring, after 6 months, after 12 months).
We analyzed multiple subsets of mentees (N > 100), who communicated with each other by email. By conducting a dictionary based corpus linguistic analysis (LIWC) with the email data, we could create several different topic related communication networks, e.g. a STEM-related network and a leisure time related network, each in three points in time (beginning of mentoring, after 6 months, after 12 months). In the longitudinal network analysis we utilized several outcome variables for the coevolution of mentee network and behavior. First analyses with a subset of the data show promising results of peer influence on anxiety towards STEM in the STEM topic related communication network of mentees.
Memory of ties: An educational network study over 10 years
Vanina Torlo | University of Greenwich | United Kingdom
This study is part of a wider project on the co-evolutionary dynamics of social networks and individual behaviour. We have extensively studied the educational setting where the effects of peer influence are viewed as consequences of interactions between students, and where the behavioural outcome of interest is the level of individual academic achievement. By studying a cohort of seventy-five MBA students, we have shown that students tend to assimilate the average performance of their friends and of their advisors. At the same time, students attaining similar levels of academic performance are more likely to develop friendship and advice ties. In other words, we have shown that – in a given timeframe of two years – processes of social influence and social selection are sub-components of a more general co-evolutionary process linking network structure and individual behaviour. This co-evolutionary process holds true even when considering the evolution of one-mode networks (such as friendship and advice) and two-mode networks (such as represented by the student’s employment preferences). In particular, we have found that advice ties between students lead to agreement with respect to employment preferences, a crucial aspect of their future career as managers.
But what happens after another ten-year time? Which emotional and professional relations are maintained and which get lost? And how do individuals' career paths affect these changes?
In order to answer these questions we have collected data on the same seventy-five MBA students 10 years after the end of the programme. We have collected information about their communication, friendship and advice ties, as well as information about their professional careers, such as the companies they work for and their roles. Preliminary results show some interesting patterns about the maintenance of different ties over a long period of time.