Using the Relational Event Model (REM)
Course Code  MAN1316/17PR 
Organised by 
University of Manchester 
Presenter  Prof Mark Tranmer 
Date  10/10/2016 
Venue  Cathie Marsh Institute 
Map  View in Google Maps (M13 9PL) 
Contact  Claire Spencer, 0161 275 1980, claire.spencer@manchester.ac.uk 
Description  The course will explain how the Relational Event Model (REM) may be used to investigate patterns in ordered or timed sequences of actions. We begin by giving some examples for which ordered sequences of timed data may be collected, including patterns of behaviour of individuals over time, and interactions in a network of individuals over time. We then introduce the REM in the context of other related methods, such as survival analysis, and also in the context of other ways of looking at sequences of actions, such as sequence analysis. We explain the importance of taking into account the way in which the ordered or timed data were collected, and the actions that were observable at each point in the sequence, when analysing it, and explain how this can be achieved. Next, we explain how an R package called informR can enable us to prepare ordered or timed data for analysis with a REM. Data preparation with InformR allows us to take into account the way in which the data were collected, and the actions that are observable at each point in the sequence. InformR also allows us to create “sequence statistics” to allow us to investigate particular patterns in the timed or ordered sequence of actions that may be of particular substantive interest. Finally we explain how relevent, an R package, can be used to fit REMs to ordered or timed sequence data. We give examples, and explain how the results of a REM analysis that has been carried out using relevent can be interpreted. Objectives
Prerequisites Some familiarity with models for longitudinal data such as the Cox proportional hazards model as used in survival analysis would be advantageous, but is not absolutely essential. Familiarity with regression and logistic regression models is assumed. Knowledge of networks could be useful for some of the examples given in the later part of the course, but is not assumed. The course will use the software R for the analysis, preparation and modelling of the data – this is a free download and available for both Mac and PC using a variety of operating systems. The practical sessions will include all necessary code for R, so that an allround knowledge of R is not required. Some familiarity with R would however be useful, via for example: QuickR: www.statmethods.net but is not absolutely essential. Recommended Preparation: We don’t assume you will read all the material below, but if you did at least look at some of it, it would significantly enhance your learning on the course. Background theory: Butts, C. T. (2008). A relational event framework for social action. Sociological Methodology, 38(1), 155200. Discussion of the R packages informR and relevent: (The reference below includes some realdata examples based on time use by older people.) Marcum C.S. and Butts C.T. (2015) Constructing and Modifying Sequence Statistics for relevent Using informR in R. Journal of Statistical Software, Volume 64, Issue 5. Example application: (this paper is about sequences of events in networks of individuals; includes an empirical example) Tranmer, M., Marcum, C. S., Morton, F. B., Croft, D. P., & de Kort, S. R. (2015) Using the relational event model (REM) to investigate the temporal dynamics of animal social networks. Animal Behaviour, 101, 99105. Quick r website: www.statmethods.net R http://www.rproject.org (R can be downloaded via here) R studio: http://www.rstudio.com (A handy environment for running R). Level: Advanced (specialised prior knowledge)

Level  Intermediate (some prior knowledge) 
Cost  Fees 
Website and registration  http://store.southampton.ac.uk/browse/extra_info.asp?compid=1&modid=5&deptid=8&catid=23&prodid=698 
Region  North West 
Keywords  Longitudinal Research , Event History Analysis, Survival analysis, Time Series Analysis, R 
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