During the current reporting period, we developed and employed novel methods aimed at measuring, describing and modeling social networks. We developed a framework for the psychometric evaluation of multiplex networks measuring a common relational construct. By multiplexity, we mean how different types of relationships overlap; this research helps to identify the underlying meaning in that multiplexity. A manuscript describing our framework was published in 2016. We introduced a new method for explicitly modeling network structure within a multi-level framework. In all of our ongoing projects, we obtain multiple networks, with the goal of characterizing the variability of network structure across families. There are few methodological approaches for modeling these multi-level systems. To address this gap, we developed random effects exponential random graph models that allow us to test structural hypotheses, based on information obtained from multiple network systems. This approach advances currently available approaches by allowing for simultaneous estimation of structural parameters and accommodating networks that vary in size. Further, our work on network dynamics focuses on post-intervention changes in network composition on the one hand, and development of novel tools for the analysis of temporally unfolding micro-social processes on the other. We have been principal developers in a family of models broadly called Relational Events Models (REM) for social action. REMs can be employed to understand how a social behavior unfolds in time using an event history perspective. These novel methods have resulted in publicly available software through the R-CRAN and has been applied to animal models of interpersonal behavior. In collaboration with Lise Getoor's lab, we have developed a computational approach for reconciling network data obtained through a multi-informant design. A paper describing and comparing various computational approaches was recently accepted at the IEEE International Conference on Data Mining. We have also developed two scales that capture qualitative aspects of interpersonal ties. The first measures respeto, a cultural belief related to interpersonal processes primarily between older and younger generations in the Hispanic culture. This scale was presented recently at the North American Social Network meeting and a manuscript is in process. As well, we developed an measure capturing perceptions of malfeasance, nonfeasance, and uplift as they relate to caregiving networks of families affected by Alzheimer's disease. A manuscript describing this scale is currently in press. In addition to our research developing a measurement framework for social networks and multiplexity, we have examined the psychometric and measurement properties of genetic literacy scales commonly used in the literature. Our research examined genetic literacy within a large consumer panel representing the US adult population. We showed that commonly used scales measuring three dimensions of genetic literacy (familiarity, skills, and factual knowledge) fit the Rogers' hierarchy of knowledge. knowing this measurement property is important in contextualizing previous research using these scales and guiding measurement of genetic literacy in future research.