Biostatistics | Epidemiology | Public health

The course is of particular interest to researchers and professional working in social sciences, psychology, business administration, marketing and management.

The field of Social Network Analysis is one of the most rapidly growing fields of the social sciences. Social network analysis focuses on the relationships that exist between individuals (or other units of analysis) such as friendship, advice, trust, or trade relationships. As such, network analysis is concerned with the visualization and analysis of network structures, as well as with the importance of networks for individuals’ propensities to adopt different kinds of behaviors. Up until now, researchers wishing to implement this type of analysis have been forced to use specialized software for network analysis. A new set of user written commands (developed by Thomas Grund, coauthor of the forthcoming Stata Press title “An Introduction to Social Network Analysis and Agent-Based Modeling Using Stata”) are however, now available for Stata. This workshop introduces the so-called nwcommands suite of over 90 Stata commands for social network analysis. The suite includes commands for importing, exporting, loading, saving, handling, manipulating, replacing, generating, visualizing, and animating networks. It also includes commands for measuring various properties of the networks and the individual nodes, for detecting network patterns and measuring the similarity of different networks, as well as advanced statistical techniques for network analysis including MR-QAP and ERGM.

This course offers an introduction to the visual analysis of spatial data using the statistical software Stata. The course begins with an overview of the peculiar characteristics of spatial data and the implications of such for the analysis of spatial data, before moving on to discuss the concept of spatial proximity and the centrality of this particular concept to spatial data analysis.

This introductory course offers participants the opportunity to acquire the necessary theoretical background and the applied skills to enable them to: i) independently employ micro panel data techniques to their own research topics, and ii) to understand and evaluate micro panel data analysis published in the academic literature.

This intensive introductory course offers therefore an introduction to the standard machine learning algorithms currently applied to social, economic and public health data in order to illustrate (using a series of both official and user written Stata commands), how Machine Learning techniques can be applied to search for patterns in large (often extremely “noisy”) databases, which can subsequently be used to make both decisions and predictions.