The network approach is increasingly employed to explore relationships among concepts, specifically the relationships between co-occurring health conditions (e.g., binary health condition indicators from hospital episode data) and the relationships between psychological variables (continuous scales from survey data). Given the multitude of approaches available for constructing and analyzing such networks and their application across different fields of study, determining the most appropriate methods and analyses can be challenging. In this workshop, we will cover: Theoretical Frameworks: An overview of the theoretical basis for applying network analysis to study relationships among health conditions or individual attributes. Methodological Approaches: An exploration of existing methodologies for constructing networks and robustness testing of their estimations. Analytical Techniques: A comprehensive set of analyses applicable to co-occurrence or correlation networks, including basic descriptive analysis, filtering methods, community detection, centrality analysis, and network comparisons. We will offer a critical assessment of methods tailored to specific types of data and interpretations. Practical demonstrations will cover a range of methodological options and the various R packages to conduct them. In the final segment of the workshop, participants are encouraged to discuss the application of these methods to their specific datasets. Names and contact information of organizers: Srebrenka Letina; Srebrenka.letina@glasgow.ac.uk Mark McCann; Mark.Mccann@glasgow.ac.uk Length of the workshop: 3 hours Maximum number of attendees: 30

This workshop provides a hands-on tutorial to using exponential-family random graph models (ERGMs) for statistical analysis of social networks, using the “ergm” package in statnet. The ergm package provides tools for the specification, estimation, assessment and simulation of ERGMs that incorporate the complex dependencies within networks. Topics covered in this workshop include: * an overview of the ERGM framework; * types of terms used in ERGMs * defining and fitting models to empirical data; * interpreting model coefficients; * goodness-of-fit and model adequacy checking; * simulation of networks using fitted ERG models; * degeneracy assessment and avoidance. Workshop length: 3 hours Max attendees: 30

This workshop provides a hands-on introduction to working with temporal network data in statnet, from exploratory data analysis and visualization to statistical modeling with Temporal Exponential-Family Random Graph Models (TERGMs). TERGMs are a broad, flexible class of models for representing the structure and dynamics observed in temporal networks. They can be used for both estimation from and simulation of dynamic network data. The topics covered in this workshop include: - A brief overview of exploratory data analysis with temporal network data (using the statnet packages ‘tsna’ for descriptive statistics and ‘ndtv’ to create network movies), - different types of dynamic network data (network panel data, a single cross-sectional network with link duration information, and cross-sectional, egocentrically sampled network data) - statistical model elements and specification using the statnet package tergm - model estimation tools for each type of data in tergm - model diagnostics in tergm, and - Simulating dynamic networks from fitted models with tergm.

The goal of this workshop is to provide participants an orientation to conducting personal networks research within Network Canvas and the opportunity to master the skills necessary to apply these tools within their specific domain of interest. Network Canvas (http://www.networkcanvas.com) is a free and open-source software suite that facilitates the collection of self-reported social network data, comprised of applications to support both in-person interviewer-assisted environments as well as remote self-administered studies. In this workshop, we will provide an overview of Architect, the Network Canvas visual survey builder, as well as Interviewer, the Network Canvas app used to collect data directly from participants within an in-person research design. We will also provide an overview of Fresco, the newest Network Canvas tool designed for remote network surveying. Finally, we will explore data export in Interviewer and Fresco, and a brief orientation to analysis. Expect the opportunity to engage in hands-on exercises during the session with assistance from our team. When completed, you will acquire the skills to: Design an egocentric or whole network survey Deploy and manage a study, whether in-person or remote Obtain study data in Interviewer and Fresco, and export it for analysis