Social Network Dynamics and Social Development among Preschoolers (2021-2022)
A growing body of research indicates that early environmental experiences during the first five years of life significantly shape an individual’s risk or resilience to a number of potentially negative mental health (e.g., depression, substance abuse) and physical (e.g., diabetes, Alzheimer’s Disease) outcomes. Based on this research, as well as studies supporting the first five years of life as a rapid period of development for social skills and brain function, it is crucial to investigate how children develop an understanding of being with and cooperating with others. This may be crucial to addressing many of the social problems faced by today’s society.
To date, however, there has been a paucity of data available to inform the complex interactions between early life experience, social context and individual differences that place an individual at greater or lesser risk for poor social outcomes – such as isolation, bullying and aggression – over time. And, unfortunately, the salience of this knowledge gap continues to grow with the increasing number of interpersonal acts of violence occurring in schools. Given that attending preschool is the first point of entry to a social world beyond the family for many children, learning how the underlying “rules” of sociality develop and work to shape one's interactions within this context may be especially illuminating.
This project builds on an effort to collect and analyze social network data from video evidence using seven classrooms in two local preschools. It is part of a multiyear effort that will build in additional data sources, including brain development as it relates to social network development. The ultimate goal of this project is to begin collecting data that can help illuminate how sociality and social cohesion develop among young children, and what role interactions with peers and teachers in the preschool environment (classrooms and free time) play in learning how to be social.
Activities will involve noninvasive recording of daily classroom behaviors using three microcameras mounted discreetly on classroom walls. Recorded segments will be approximately 10 minutes on average. Segments will be collected three to five times per day, one to five days per week. Team members will plan and carry out the social network data collection, converting these video recordings into dynamic network data of children’s positive and negative interactions with one another. Additional data on students’ characteristics and educational records will also be collected.
Learn more about this project team by viewing the team's video.
Social network dataset on several preschool classes; journal publications; theses; dissertations
Ideally, this project team will be comprised of 1 graduate student and 4-6 undergraduate students. Interested graduate students will be from sociology and well-versed in social network theory and methods, with an interest in modeling complex dynamic networks. Interested undergraduate students will be from a variety of majors and backgrounds, including psychology, sociology, education and human development. Students most likely to benefit from this research will also be interested in data analytics and learning cutting-edge technology methods to understand social development and social networks.
Team members will gain important experience in the research process. They will work closely with faculty to better understand how data is collected and stored, as well as the operationalization of social networks from streaming interactional data. Students will learn about social network models, data visualization techniques and programming in the R environment.
Team members will be involved in creating a digital archive of the raw video footage, the creation of a codebook for assessing the qualities of interactions, as well as doing the coding and validating the coding scheme across multiple, smaller teams. The team will learn about the techniques of social network analysis and gain hands-on experience using a large, dynamic video corpus to derive and analyze relational network data. This will entail learning to visualize network data and cutting-edge dynamic network modeling techniques to statistically analyze these data. In order to participate in the interpretation of study findings as they emerge, team members will also learn about normative patterns of child social development through video review and more formal discussions during team meetings.
Tom Wolff will serve as project manager.
Fall 2021 – Spring 2022
- Fall 2021: Application of social network analysis to coded data; visualization, descriptive network metrics, and dynamic network models
- Spring 2022: Overview of social network analysis, visualization, R statistical environment, etc.; introduction to the gathered footage and coding scheme; applying the coding scheme to new video footage
Academic credit available for fall and spring semesters; summer funding available
See earlier related team, Social Network Dynamics and Social Development Among Preschoolers (2020-2021).
Image: Preschool programs, by Seattle Parks, licensed under CC BY 2.0
- Michael Gaffrey, Arts & Sciences-Psychology and Neuroscience
- Craig Rawlings, Arts & Sciences-Sociology
/graduate Team Members
Rhayoung Park, Interdisciplinary Data Science - Masters
Thomas Wolff, Sociology-AM, Sociology-PHD
/undergraduate Team Members
Elissa Harris, Psychology (BS)
Mihika Rajvanshi, Neuroscience (BS)
Carrie Wang, Statistical Science (BS)
Kelsey Zhong, Psychology (BS)
/zcommunity Team Members
The Little School of Duke
The Little School of Hillsborough