*** apologies for cross-posting ***
The Centre for Networks and Enterprise (CNE) is hosting a workshop by Prof Mark Tranmer from the University of Glasgow on the 13th March, 14:00-17:00. Please see below for more information. Everyone is welcome to attend and events are free, but please book your ticket through our Eventbrite page.
Relational Event Modelling
Instructor: Prof Mark Tranmer
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 discuss data preparation for a REM analysis, and various software available to do this. 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.
Introduce the Relational Event Model (REM) in the context of existing models and approaches. Explain the advantages of the REM over other methods, given particular substantive aims and data.
Explain the need to take into account the data collection mechanism in the analysis of ordered or timed sequences of actions.
Outline software to prepare the ordered or timed sequence data for analysis with a REM.
Outline software for a REM analysis such as the "relevent" package in R.
Understand how to interpret the results of such analyses.
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 demonstrate the use of 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. An all-round knowledge of R is not required. Some familiarity with R would however be useful, via for example: Quick-R: www.statmethods.net but is not essential.
There is a list of recommended reading material on the Eventbrite page. We don’t assume you will read all the material, but if you did at least look at some of it, it would significantly enhance your learning on the course.
The Workshop will take place at Heriot-Watt University JN116 (James Nasmyth Building), 13th March 14:00-17:00. Please meet at Edinburgh Business School, Heriot-Watt University from 13:15-13:45 for arrival teas & coffees and registration. We will then walk across to the workshop room. Book your free place at our Eventbrite page.