When looking at a social network of individuals, it is normally easier to identify correlations between constructs, but it is more difficult to pinpoint the causality of different constructs and their interplay with underlying network. However, in recent years, development of methods based on stochastic actor-based models for network dynamics implemented in SIENA, Simulation Investigation for Empirical Network Analysis, (Snijders, Steglich, & Van de Bunt, 2010; Snijders T. A., 2005) enabled a simultaneous study of the co-evolution of networks and behaviour of its members. Based on longitudinal data of individuals' behaviour and their social connections, SIENA allows the user to differentiate between two types of processes: selection and influence. Selection processes focus on criteria for actors to choose other actors, based on their assigned attributes, with whom to make or break a link with. Influence processes focus on dynamics of actors' observed qualities or attributes (which can be behaviour, attitude, or another measurable state) and its influence on individuals to whom actors are directly or indirectly connected.