Affectivity and its Role in Predicting Sociometric Position in Small Group Networks
Abstract
An individual’s tendency to experience positive emotions can impact the likelihood they find themselves in advantageous positions within their social circle. Adopting a network perspective to map social relations, the current study examined the extent to which dispositional positive affectivity predicts one’s eigenvector centrality and indegree centrality within interaction networks and status networks, respectively. Gathering data during the Fall of 2023, I collected data from 16 student clubs and the members within them and utilised multilevel modeling to disaggregate data. Controlling for individual demographics and group-structure variables, the results suggest that dispositional positive affectivity significantly predicted eigenvector centrality for interaction networks, but not indegree centrality for status networks. Negative affectivity was found to predict indegree centrality for status networks, but not eigenvector centrality for interaction networks. Group-aggregated positive affectivity was not significant in predicting average interaction network centrality but was significant for indegree status network centrality. Group-aggregated negative affectivity failed to predict for both networks. My thesis therefore demonstrates the importance of considering individuals’ affect to explain how people come to position themselves within small social circles, whilst also descriptively highlighting the differences between interaction and status networks.