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Cancellation Policy: If you cancel your registration at least two weeks before the course is scheduled to begin, you are entitled to a full refund (minus a processing fee of $50).

In the unlikely event that Statistical Horizons LLC must cancel a seminar, we will do our best to inform you as soon as possible of the cancellation. You would then have the option of receiving a full refund of the seminar fee or a credit towards another seminar. In no event shall Statistical Horizons LLC be liable for any incidental or consequential damages that you may incur because of the cancellation.

A 3-Day Livestream Seminar Taught by jimi adams, Ph.D.

Given that networks are everywhere, scholars have developed a wide range of strategies for studying them. This course lays the groundwork for introductory concepts in social network analysis (SNA). SNA’s focus on relational data forces adaptation to analytic approaches ranging from data collection and storage to descriptive and inferential statistics. This course will focus primarily on the breadth of descriptive statistics employed in SNA, sampling from each “family” of measures, employing a conceptual/mathematical perspective, illustrated with empirical examples, and implementing computation (in R).

This approach leverages two organizing principles: (1) the two primary theoretical frameworks capturing reasons networks “matter”; and (2) how each class of measures can be applied across different units of analysis: individuals, groups and “whole” networks. The topics covered will be: data structure, homophily, social balance, local network composition, centralities, subgroup cohesion, network clustering (“communities”), and equivalence. While by no means exhaustive, this approach will develop your beginning SNA toolkit.

Our approach will conceptually build from the intuitions of individual (variable) centric approaches in the social sciences to layer over that a set of relational theoretical concepts. The ideas available for “thinking in networks” provide a fundamentally different orientation to approaching studying social phenomena, which we will begin to elaborate in this course.

To do so, we will break down a variety of descriptive network statistical “families” of measures into understanding what they capture, elaborating how they’re conceptualized and measured, demonstrating their empirical utility in a sample of examples, and introducing how they’re computed in R.

In Day 1 we’ll establish some base terminology, touch on a few overarching theoretical frameworks, elaborate types and structure of network data including how it’s collected and visualized, and focus on some measures that span different levels of analysis (including degree, density, and distance).

In Day 2 we’ll build on the ideas from above to build understanding of some of the most widely used measures in SNA – personal network composition (including homophily), social balance (reciprocity & triadic closure), and centrality.

In Day 3, we’ll expand these descriptive measures further to address cohesion and clustering, then conclude with the notion of equivalence. While the focus of this course is descriptive network statistics, along the way we’ll touch on how these get assessed inferentially (though that’s the focus of a 2nd course).