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 force 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.
In common with TStat’s workshop philosophy, each individual session, is composed of both a theoretical component (in which the techniques and underlying principles behind them are explained), and an applied (hands-on) segment, during which participants have the opportunity to implement the techniques using real data under the watchful eye of the course tutor. Throughout the workshop, theoretical sessions are reinforced by case study examples, in which the course tutor discusses current research issues, highlighting potential pitfalls and the advantages of individual techniques. The intuition behind the choice and implementation of a specific technique is of the utmost importance. In this manner, course leaders are able to bridge the “often difficult” gap between abstract theoretical methodologies, and the practical issues one encounters when dealing with real data. At the end of the course, participants are expected to be able to autonomously implement the theories and methodologies discussed during the workshop.
The workshop provides an interdisciplinary opportunity for social scientists, mathematicians, computer scientists, ethnologists, epidemiologists, organizational theorists to acquire the necessary statistical tools required to analyse social networks in Stata.
Working knowledge of Stata.
SESSION I: INSTALLING NETWORK COMMANDS
Networks and node attributes
Finding help: help
Return vector: return, ereturn
User written commands: adopath
Installation of nwcommands
Dialog boxes for network commands
SESSION II: GETTING STARTED WITH NETWORKS
Setting networks: nwset
Listing networks: nwds
Current network: nwcurrent
Using and saving networks: nwuse, nwsave
Importing and exporting networks: nwexport, nwimport
Dropping and keeping networks: nwdrop, nwkeep, nwclear
Network transformation: nwtoedge, nwfromedge
SESSION III: NETWORK VISUALIZATION
Network visualization: nwplot, nwplotmatrix, nwplotjs
Animation of networks: nwmovie
SESSION IV: NETWORK EXAMINATION
Summarize networks: nwsummarize
Tabulate networks: nwtabulate
Dyads, triads: nwdyads, nwtriads
Simmelian ties: nwsimmelian
SESSION V: DISTANCE AND PATHS
Distance and paths: nwgeodesic, nwpath
Local and global bridges: nwbridge
SESSION VI: NEIGHBOURS AND CONTEXT
Network neighbours: nwneighbor
Attributes of neighbours: nwcontext
Attributes of neighbours at certain distance
SESSION VII: CENTRALITY AND CENTRALIZATION
Importance in networks
Degree centrality: nwdegree
Betweenness centrality: nwbetween
Katz centrality: nwkatz
Closeness centrality: nwcloseness
Centralization in networks
SESSION VIII: CHANGING NETWORKS
Extract tie values
Change networks: nwreplace, nwreplacemat, nwrecode
SESSION IX: CALCULATING WITH NETWORKS
Network generators: nwgen
SESSION X: NETWORK SIMULATION
Preferential attachment networks
Commands: nwrandom, nwsmall, nwhomophily, nwdyadprob, nwpref, nwring, nwlattice
SESSION XI: HYPOTHESIS TESTING 1
Correlation of networks
Conditional uniform graphs
Permutation tests: nwpermute
SESSION XII: REGRESSION BASED HYPOTHESIS TESTING
Logistic regression: logit
Network transformation: nwtoedge, nwfromedge
Quadratic assignment procedure: nwqap
Short introduction to P2 models and their estimation in Stata
The course will be held in Munich from the 10th to 12th of December 2018 from 9.00 am to 17.00 pm.
Students*: € 735.00
Academic: € 1225.00
Non-Profit/Public Research Centres: € 1513.00
Commercial: € 1800.00
*To be eligible for student prices, participants must provide proof of their full-time student status for the current academic year.
Fees are subject to VAT (applied at the current Italian rate of 22%). Under current EU fiscal regulations, VAT will not however applied to companies, Institutions or Universities providing a valid tax registration number.
Please note that a non-refundable deposit of €100.00 for students and €200.00 for Academic, Non-Profit/Public Research Centres and Commercial participants, is required to secure a place and is payable upon registration. The number of participants is limited to 15. Places will be allocated on a first come, first serve basis.
Course fees cover: teaching materials (handouts, Stata do files and datasets to used during the course), a temporary licence of Stata valid for 30 days from the beginning of the workshop, light lunch and coffee breaks.
To maximize the usefulness of this workshop, we strongly recommend that participants bring their own laptops with them, to enable them to actively participate in the empirical sessions.
Individuals interested in attending this workshop must return their completed registration forms either by email (email@example.com) or by fax (+39 0864 206014) to TStat by the 20th of November 2018.
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 force 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.