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PRODID:-//Columba Systems Ltd//NONSGML CPNG/SpringViewer/ICal Output/3.3-
 M3//EN
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CALSCALE:GREGORIAN
METHOD:PUBLISH
BEGIN:VEVENT
DTSTAMP:20160223T105650Z
DTSTART:20160711T113000Z
DTEND:20160715T120000Z
SUMMARY:methods@manchester Summer School: Statistical analysis of social 
 networks
UID:{http://www.columbasystems.com/customers/uom/gpp/eventid/}o1cg-ikzart
 94-m553ja
DESCRIPTION:This is an introduction to statistical analysis of networks. 
 While no strict prerequisites are assumed\, you might find it helpful to
  have some basic knowledge of social network analysis beforehand. In par
 ticular\, “Introduction to social network analysis using UCINET and Netd
 raw”\, given in the preceding week (4 - 8 July 2016) in the methods@manc
 hester Summer School provides a good background. To benefit fully from t
 he course requires a basic knowledge of standard statistical methods\, s
 uch regression analysis. The course aims to give a basic understanding o
 f and working handle on drawing inference for structure and attributes\,
  both cross-sectionally as well as longitudinally. A fundamental notion 
 of the course will be how the structure of observed graphs relate to var
 ious forms of random graphs. This will be developed in the context of no
 n-parametric approaches and elaborated to analysis of networks using exp
 onential random graph models (ERGM) and stochastic actor-oriented models
 . The main focus will be on explaining structure but an outlook to expla
 ining individual-level outcomes will be provided.\n\nThe participant wil
 l be provided with several hands-on exercises\, applying the approaches 
 to a suite of real world data sets. We will use the stand-alone graphica
 l user interface package MPNet and R. In R we will learn how to use the 
 packages ‘sna’\, ‘statnet’\, and ‘RSiena’. No familiarity with R is assu
 med but preparatory exercises will be provided ahead of the course.\n\nC
 ourse objectives\nThe course will:\n1. Introduce how statistical evidenc
 e relates to social networks\n2. Explain how to draw inference about key
  network mechanisms from observations\n3. Provide hands-on training to u
 se software to investigate\ni. social network structure\nii. tie-formati
 on in cross-sectional data\niii. tie-formation in longitudinal data\niv.
  take into account network dependencies between individuals
STATUS:TENTATIVE
TRANSP:TRANSPARENT
CLASS:PUBLIC
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