Siena Winter School 2021



Online Workshop

Advanced Siena Users' Meeting 2021

February 11-12, 2021



Don't use Internet Explorer or Microsoft Edge as a browser for these pages. These do not reproduce the R scripts in a nice fashion.


Basic Preparations

The course will be online. It will consist of an alternation of lectures, Q&A sessions, and practical work on assignments.

It is assumed that the participants have a good working knowledge of the RSiena package.

This page contains an overload of material. For the workshop, a selection will be used, depending on the participants' interests.
Many of the slides below have two versions: the first is the slide show that will be used in the presentation, the second is a handout version. You might wish to save the handout versions on your computer to make notes on the pdf; or print some of them out to make notes on the paper version.

The workshop will take place via zoom; the zoom address will be sent in a message to the participants.



To prepare your computer setup for the course

  • In R install the packages RSiena, sna, network, igraph, xtable.
    Visit the new (it started late summer 2020) RSiena repository at GitHub: https://github.com/snlab-nl/rsiena/.
    Take a look around, and try to install RSiena version 1.2.29.
    Since this is rather new, we like to get feedback!
    If installing version 1.2.29 does not succeed, that will not be a problem; the 'official' CRAN version 1.2-23 (or a newer version) is also suitable.


Programme

There will be short breaks in the middle of each hour and much opportunity for asking questions.

As the workshop proceeds, there may be discussions and questions, and the program may be adapted.

  1. Thursday, February 11.

    10.00-11.00
    11.30-12.30
    14.00-15.00
    • Master class: paper 1.
      Carlos de Mates Fernandez and co-authors:
      Does cooperativeness homophily arise among first-year students in higher education?
    15.30-16.30
    • Master class: paper 2.
      Ashwin Rambaran and co-authors:
      A Relational Approach to Ethnic-Racial Discrimination: Testing Selection, Influence, and Group Membership
    17.00-18.00
    • Discussion: Relations with other models.

      If there is time earlier for this topic, it will be discussed earlier.

  2. Friday, February 12.

    10.00-11.00
    11.30-12.30
    • Simulation, part 2.

      Materials: see above.

    14.00-15.00
    • Master class: paper 3.
      Eleonora Marucci and co-authors:
      The role of classroom social climate on friendship selection and influence processes related to academic achievement: Examining the effects of an educational intervention
    15.30-16.30
    • Master class: paper 4.
      Debbie Vermond and co-authors:
      The evolution and coevolution of a primary care cancer research network: from academic social connections to co-authorship relations
    17.00-18.00
    • Discussion: Standards for reporting; guidelines for reviewing.

      If there is time earlier for this topic, it will be discussed earlier.



Materials

Here you find a long list of materials, some of which will be used in the course, and many that will not be used. You can take a look at them; and we might turn to some of them for topics not treated above.

Sets of slides

The slides below have two versions: the first (name ending with "_s") is the slide show that will be used in the presentation, the second (name ending with "_p" or "_ha") is a handout version.

Manual

  • Ruth M. Ripley, Tom A.B. Snijders, Zsófia Boda, Andras Vörös, and Paulina Preciado (2020). Manual for SIENA version 4.0. Oxford: University of Oxford, Department of Statistics; Nuffield College.

Texts

Scripts

Don't use Internet Explorer or Microsoft Edge as a browser for these pages. These do not reproduce the R scripts in a nice fashion.

    First some fundamental scripts.

  1. basicRSiena.r
    An example of a basic sequence of commands for estimating a model by function siena07 of RSiena.
  2. Rscript01DataFormat.R with some basic information about R, networks, data formats etc;
    with an example data file arclistdata.dat.
  3. RSienaSNADescriptives.R with some descriptives, using package sna.
  4. Rscript02SienaVariableFormat.R for how to specify data as variables in RSiena, and specify the model; using the s50 data set.
  5. Rscript03SienaRunModel.R for how to carry out the estimation and look at the results;
  6. Rscript04SienaBehaviour.R for how to specify models for dynamics of networks and behaviour;

    7. In addition there are some other scripts.

  7. RscriptSienaMultiple.R for how to specify models for dynamics of multiple networks;
    this uses a manufactured data set (longitudinal, 2 waves, 2 networks);
  8. RscriptSienaTimeTest.R an example of testing time heterogeneity, using the van de Bunt students data set.
  9. RscriptMultipleGroups_meta.R an example of meta-analysis with the metafor package and siena08, and of the multiple group option, using Chris Baerveldt's Dutch Social Behavior data set.
  10. RscriptMultipleGroups.R a more limited example of the multiple group option and of meta-analysis using function siena08, using Chris Baerveldt's Dutch Social Behavior data set.
  11. RscriptSienaOrdered.R an example of modeling a network with ordered ties (i.e., valued ties with a small set of ordered categories), by representing them as multiple, ordered networks, using the van de Bunt students data set.
  12. RscriptSienaBipartite.R an example of co-evolution of a one-mode and a two-mode network, using a set with substance use variables of the Glasgow Teenage Friends and Lifestyle Study data.
  13. RscriptSienaSymmetric.R a brief example of modeling a non-directed network, using the s50 data set.
  14. Composition change in a network: R script and data for illustration, also see the slides.
  15. Simulation of networks with a given model specification: NetworkSimulation.R.
  16. Goodness of fit:
    An example script for how to use sienaGOF() is in sienaGOF_vdB.R.
  17. Scripts to help making selection tables (see the corresponding section in the manual):
    SelectionTables.r.
  18. Scripts to help making influence tables (see the corresponding section in the manual):
    InfluenceTables.r.
  19. Random coefficient multilevel estimation of the Stochastic Actor-Oriented Model:



Further reading, not for the course, but for the scientific context

  1. Stephen P. Borgatti, Ajay Mehra, Daniel J. Brass, Giuseppe Labianca (2009). Network Analysis in the Social Sciences. Science, 323, 892-895.
    DOI: http://dx.doi.org/10.1126/science.1165821
  2. Garry Robins (2013). A tutorial on methods for the modeling and analysis of social network data. Journal of Mathematical Psychology, 57, 261-274.
    DOI: http://dx.doi.org/10.1016/j.jmp.2013.02.001
  3. Tom A.B. Snijders (2011). Statistical Models for Social Networks. Annual Review of Sociology, 37, 129-151.
    DOI: http://dx.doi.org/10.1146/annurev.soc.012809.102709




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