Analysis of Sequence Data with Relational Event Models



Organised by:



Mark Tranmer


Intermediate (some prior knowledge)


Claire Spencer, 0161 275 4917,


View in Google Maps  (M13 9PL)


Cathie Marsh Institute
Humanities Bridgeford street
University of Manchester


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.


Fees per day:
For UK registered postgraduate students
For staff at UK academic institutions, ESRC funded researchers and registered charity organisations
For all other participants

Website and registration:


North West


Longitudinal Data Analysis, Event History Analysis, Time Series Analysis, Relational Event Models , sequence analysis , life course , life-course , lifecourse

Related publications and presentations:

Longitudinal Data Analysis
Event History Analysis
Time Series Analysis

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