Search

Through the development and field testing of an instrument designed to measure youth social capital in the context of postsecondary transitions, this research addresses the need for theory-driven measures of social capital among youth. The results offer preliminary evidence that dimensions of youth social capital, including network structure and network content, can be reliably measured and that these dimensions of social capital are interrelated in a manner consistent with theory. The results also offer initial support for the validity of the social capital construct within the domain of youth postsecondary transitions. Taken together, the findings provide a foundation for continued research that might surmount inadequate measures and theoretical disputes to encompass more careful and rigorous empirical scrutiny when it comes to the measurement of social capital among children and adolescents.

The hierarchical network model (HNM) is a framework introduced by Sweet, Thomas, and Junker for modeling interventions and other covariate effects on ensembles of social networks, such as what would be found in randomized controlled trials in education research. In this article, we develop calculations for the power to detect an intervention effect using the hierarchical latent space model, an important subfamily of HNMs. We derive basic convergence results and asymptotic bounds on power, showing that standard error for the treatment effect is inversely proportional to the product of the number of ties and the number of networks; a result rather different from the usual effect of cluster size in hierarchical linear models, for example. We explore these results with a simulation study and suggest a tentative approach to power for practical applications.

Intervention studies in school systems are sometimes aimed not at changing curriculum or classroom technique, but rather at changing the way that teachers, teaching coaches, and administrators in schools work with one another--in short, changing the professional social networks of educators. Current methods of social network analysis are ill-suited to modeling the multiple partially exchangeable networks that arise in randomized field trials and observational studies in which multiple classrooms, schools, or districts are involved, and to detecting the effect of an intervention on the social network itself. To address these needs, we introduce a new modeling framework, the "Hierarchical Network Models (HNM) framework. The HNM framework can be used to extend single-network statistical network models to multiple networks, using a hierarchical modeling approach. We show how to generalize the latent space model for a single network to the HNM/multiple-network setting, and illustrate our approach with real and simulated social network data among education professionals. (Contains 1 table, 9 figures, and 1 note.)