Group Dynamics Project

Isolation and Groups Among Students

In this project, we ask about perceptions of social isolation, focusing on membership in digital social groups and interpersonal relationships. Along with this, we also ask whether feelings of isolation produce lower levels of social cohesion. To do so, three research objectives are proposed. The first consists of exploring composite relationship measures to capture different dimensions (cognitive, functional and structural) of interpersonal and digital social groups in such a way as to consider the relational flows where influences operate. Secondly, and using the composite networks identified in the first objective, we seek to explain how social influence mechanisms (assimilation, similarity or repulsion) influence feelings of isolation when social groups are considered. Finally, our third objective is to evaluate the effect that modifying social influence mechanisms would have on the levels of cohesion of a university community, simulating different scenarios of temporal evolution.

To meet the research question and objectives, we seek to replicate part of the Swiss StudentLife study in this project (Vörös et al., 2020). This study aimed to understand the emergence of informal student communities and their effects on different individual outcomes, such as well-being, motivation, and academic success. To do so, multiple dimensions of social ties were assessed, combining computer-based surveys, social sensors, data from social platforms, and field experiments. The dynamics of the social networks explored were measured at various time scales. In our case, we propose to apply a panel survey to a cohort of students from a university degree program at the Faculty of Social Sciences of the Pontifical Catholic University of Chile. The survey will ask a set of network questions that are commonly used to measure ties (e.g., level of interaction, friendship, conflict, who they study with, joint activities, membership in groups through digital media) and other questions related to the perception of groups (groups to which individuals belong or groups identified by students) through a one-year panel study.

Regarding the results, we hypothesise that the different social relationships to be investigated will be grouped into three dimensions (Hypothesis 1), the first corresponding to cognitive interdependencies, the second to structural interdependencies, and the third to functional interdependence processes. In addition, we believe that individuals will show lower levels of social isolation if the average of their social relationships has lower levels of social isolation (Hypothesis 2.1) and when individuals in more cohesive groups will also have lower levels of social isolation (Hypothesis 2.2). On the other hand, individuals who share similar attributes (such as frequency of communication through digital media) will tend to have lower levels of social isolation (Hypothesis 2.3), and those students who share dissimilar attributes to the majority of the group (such as participation focused on groups predominantly on digital platforms where peers of the cohort do not participate) will have higher levels of social isolation (Hypothesis 2.4). When assimilation mechanisms are considered, they will result in individuals at the macrosocial level tending to generate long-term consensus (Hypothesis 3.1). Whereas similarity mechanisms produce that, if consensus is not achieved in the long term, fragmentation will be generated in the network, thus distinguishing multiple homogeneous but different groups (Hypothesis 3.2). Finally, when repulsion mechanisms increase, it is expected that in the long term, conglomerates will be produced that can reach maximum opposition (example: bi-polarization) (Hypothesis 3.3).

This project seeks to make both theoretical and methodological contributions. Theoretically, it aims to consider the current state of discussion on group dynamics and social network studies, considering interpersonal and digital relationships to evaluate how the mesosocial level affects microsocial and macrosocial levels. Methodologically, we will combine new ways of collecting data and analysis strategies to identify composite measures that seek to capture, more precisely, what social groups are. This project will not only generate knowledge for experts on topics related to group dynamics and social networks. Still, it will also make the design, instruments, tutorials and functions available in repositories dedicated to open science.

Informal Groups Among Students in a Technical University

We use the dataset from the Swiss StudentLife (Vörös et al., 2020), a study conducted by the Social Networks Lab at ETH Zürich between 2016 and 2020. The study follows three cohorts of students (N1 = 226, N2 = 244, N3 = 652) who started 3-year bachelor programs at a Swiss technical university in 2016 and 2017. Members of each cohort shared most of their classes and often did courses work in groups. The groups were captured by asking each student to name the informal social groups in the cohort that they felt they belonged to and the peers in their cohort whom they perceived to be co-members of these informal groups.

Working papers:

• Espinosa-Rada, A.; Vörös, A. & Stadtfeld, C. "Identification with Emergent Groups in an Undergraduate Community".

• Espinosa-Rada, A.; Smokovic, I.; Zaretckii, S.; Xu, X. & Stadtfeld, C. “Group Effects for the Emergence of Structural Balance in Signed Networks".

Social Networks and Socio-ecological Sustainability Project (SNA-SES)

Social Networks and Teams Well-being for the Creation of Scientific Knowledge

How can group well-being, leadership, and cohesion during the expeditions foster knowledge?

Previous research about teams in extreme conditions in the Antarctic and NASA’s Human Exploration Research Analog (HERA) project demonstrated that:

1. Clear types of leadership (i.e., expressive leadership) and groups that were highly cohesive achieved better outcomes (Johnson et al., 1986; Johnson et al., 2003; Johnson, 2019; Johnson et al., 2019, 2020; Zurek et al., 2020 ).

2. Researchers who were emotionally well-adjusted confronted the ‘winter-over syndrome’ better (which involves depression, insomnia, and cognitive disorientation) (Palinkas & Johnson, 2000).

What is the aim of the Project?

1. First, to analyse the co-evolution of support networks and the well-being of researchers in teams by investigating the effect of types of leadership and the cohesion of the group during the expedition. 

2. Second, to analyse how network embeddedness and knowledge production co-evolve during and after the expedition, when well-being is considered. 

3. Finally, by following different teams, we will suggest a typology of group tendency toward knowledge production.

Understanding the intricate relationship between selection and influence processes enhances expeditions' efficiency and profoundly influences the teams' objectives and research (Yaqub, 2018). Scientific endeavours often lie on a spectrum, ranging from generating novel knowledge to confirming facts (Kuhn, 2012). This knowledge acquisition occasionally yields unexpected and serendipitous discoveries (Merton & Barber, 2006). The networks among researchers can either facilitate serendipity through information sharing and teamwork or, conversely, inhibit it through groupthink. The mechanisms underlying these discoveries underscore how social factors can shape their occurrence and prevalence (Yaqub, 2018).

How are we going to investigate knowledge formation?

We will follow the experience gained in projects such as the HERA or the SCALE projects. In both projects, a social network perspective was used to analyse teams in extreme conditions, applying surveys, interviewing the members of the groups, and applying novel strategies to collect the data (e.g., sociometric badges, video and facial analysis, lexical analysis of speech and text collected from crew journals, among others).   

What are we going to do differently?

In comparison with previous research, we will use social network models (Snijders et al., 2010; Stadtfeld et al., 2017), capable of investigating simultaneously the co-evolution of the creation of social relationships and the changes in individuals' behaviour, opinions, or attitudes (such as well-being and new knowledge). The main feature of this analytical strategy is that it allows us to investigate how the network (e.g., social support, collaboration) influences individuals (e.g., in their mental well-being) and how the characteristics of individuals (e.g., their mental well-being, leadership) affect their tendencies to select each other to create social relationships (e.g., social support, collaboration). Some of the particularities of these models are that they are hypothesis-testing models that enable the identification of mechanisms that are more predominant in the network (Stadtfeld & Amati, 2021), which explain why social relationships form and how the network impacts individuals.