ABSTRACT
This chapter discusses how longitudinal network analysis can be useful for theory development, especially social capital theory. Established social capital theories refer to the access and use of resources (e.g., information, knowledge) in the network. Various resources enable individuals to achieve their individual goals, such as passing exams and obtaining a job. A longitudinal social network approach provides a better understanding of how networks change over time and how the underlying selection and influence mechanisms contribute to social capital formation and, hence, to performance or attitude changes. Selection and social influence are crucial social network mechanisms, but these mechanisms are not explicitly addressed in social capital theory. The longitudinal social network approach, stochastic actor-oriented modelling (SAOM), enables us to disentangle selection from influence. This is illustrated by students’ social capital building in peer networks in higher education. Higher education students establish social capital when they interact with their peers within the learning context. They select each other when they need academic help (selection) or the academic help relationships may impact students’ grades (social influence). Overall, SAOM can provide a better understanding of social network dynamics and advance social theories, such as the social capital theory.
