Georgia

The research design for this project is longitudinal and cross-sectional, and is designed to generate evidence which is descriptive (descriptive statistics) and causal (statistical modeling: detailed statistical analysis of network factors for women in STEM). This project collects original data using self-completion online questionnaires. The core feature of the survey is that it also gathers data on social network structure and resources. The survey uses an ego-centric network design to explore the respondents’ relationships with individuals in the respondents’ collaborative and advice networks. Through the use of detailed survey questions respondents describe their networks for select activities and their relations with network members., As a result, the survey captures multiple dimensions of the collaborative and advice networks that are not accessible through existing data such as bibliometrics. To capture these data, the survey included a series of name generator and name interpreter questions. Respondents were first asked to write in the names of “close collaborators” or advisors in research collaboration as well as advice and support networks into five name generator questions. These included closest collaborators within their own university, closest collaborators outside their university, individuals with whom “they talk about their research but have never collaborated” and individuals in two types of advice scenarios – those with whom they talk about career advice and with whom they discuss departmental matters. Although, the first three (research) networks are mutually exclusive, there is, as expected, some overlap between the research and advice networks. Once the survey respondent provided names in each of the five name generator questions, the respondent was then asked a series of name interpreter questions about each of the individuals they had named. Name interpreter questions addressed the nature of the collaboration (nature of research product), details about the level of relationship and origin of acquaintance, closeness of research expertise, communication frequency, grant activity, and general demographics. It allows us to gather collaborative and relationship-based data specific to the named individuals. In the analysis, these data then form the foundation for the network measures. In addition, respondents were asked about their research activities, including grant submission and success rate, teaching and committee responsibilities, attitudes about and involvement in interdisciplinary research, satisfaction with work-related resources, work environment, and detailed demographic and academic background questions.

Descriptive statistical analysis of the network characteristics of academic scientists are used to determine key gender, rank and other differences. Various explanatory models (depending on the nature of the models and variables) are used to test many different hypotheses that have been refined through the project.