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General topics Include:
It is recommended that participants bring their own data to analyze during the workshop in order to make progress on personal project and research goals. However, data will also be provided as needed by the instructor for all practice exercises during the workshop. The morning and afternoon sessions are coordinated so that each day includes a training component first, followed by a computer lab session designed to provide participants an opportunity to practice the techniques learned earlier that day.
This workshop focuses on methods for analyzing longitudinal social network data, focusing on techniques and approaches using the RSiena program. The workshop will address issues related to longitudinal social network data collection and data restructuring, a brief introduction to the R statistical package (although some prior experience with the R program is helpful), an introduction to concepts and hypotheses related to network structure (e.g., reciprocity, transitivity), an introduction to concepts and hypotheses related to social selection (e.g., network tie (or relationship) formation) and deselection (e.g., network tie (or relationship) dissolution), and an introduction of concepts and hypotheses related to social selection and influence (e.g., socialization). The workshop will conclude with discussions of additional possibilities and future directions for the application of longitudinal social network models (e.g., multilevel options, question of mediation and moderation, unique and/or co-evolving network structures, Goodness of Fit (GOF) tests, applications to intervention programs).
Upon completion of the workshop, participants should understand basic concepts of longitudinal social network analysis and be able to carry out basic analyses using the RSiena program. Thus, this workshop is meant to be a first introduction to RSiena, as well as a hands-on and practical training seminar for the applications of longitudinal social network analysis in various forms of research.
Experience with regression models, logistic regression models, and data analysis using the R program is helpful.
1. Charles Kadushin. 2012. Understanding Social Networks: Theories, Concepts, and Findings. Oxford University Press.
2. John Scott. 2013. Social Network Analysis. Sage Publications Inc.
3. Joseph Adler. 2009. R in a Nutshell. California: O'Reilly Media Inc.
PLEASE NOTE. The texts above are recommended as helpful resources and are not necessarily workshop requirements.
Articles and chapters on social network analysis will be assigned to enrolled participants. Most articles can be accessed through Google Scholar or through a university library. Students are responsible for their own copies of this material.
Each participant should bring his or her own laptop computer with the R statistical program installed..
We will rely exclusively on the R statistical package. The R statistical package is a flexible tool for a wide range of statistical analysis, not just social network analysis, and is freely available at: http://www.r-project.org/. We will also likely use several specific R programs such as (a) sna, (b) network, (c) RSiena, and (d) igraph. The recommended test by Joseph Adler (Ch. 4) provides guidance for program installation, and details on other programs that may be needed will be provided to workshop participants.