The inclusion of social interactions into models explaining facets of behavior is becomingrecognized as a necessity in the pursuit of higher accuracy in explaining and predicting behavior.Among these efforts, researchers have focused on issues such as the composition of socialnetworks, and the constraints and influences that others have on spatial decisions. An importantaspect that has been understudied however is the variability or heterogeneity of individuals bothas social network members and as participants in these social networks. Understanding the roleindividuals play in decision-making in different social networks can further define our models toinclude more accurate representations of human behavior. This research explores the differencesbetween social network composition, and the decision roles members play within different socialnetworks specifically when deciding where to participate in activities. A survey was conductedin Santa Barbara, California on social network involvement, network attributes and decision13making roles within each network. Two separate latent class cluster analysis (LCCA) modelswere developed to classify social network involvement and roles. Results show that there areclearly different types of social involvement and roles within networks. Further data collectionand analysis will be used to better understand how these decision-making roles manifestthemselves in activity decision-making.
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