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Graphical Representation of Transitions and Sequences

  • Christian Brzinsky-FayEmail author
Chapter
Part of the Life Course Research and Social Policies book series (LCRS, volume 2)

Abstract

A sequence is an ordered list of categorical values of a variable which, in the social sciences, corresponds to social processes or trajectories, such as school-to-work transitions or family formation. Sequence analysis presents the possibility of dealing with complex information because sequences are usually based on longitudinal data and cover a large number of cases. Even scholars who apply elaborated methodological statistical approaches necessarily need to reduce this complexity by restricting their analyses to certain events. For example, many researchers within the field of school-to-work transitions who apply event history analysis reduce the event “labour market entrance” to the status change into employment. The algorithmic approach of sequence analysis has the ability to handle the complexity of social processes empirically. However, the problem of graphical representation persists because the use of categorical time series involving many individuals requires multidimensional visualisation, but the general form of (printed) scientific publications is still black and white.

This article explores the different visualisation possibilities of sequential information that are typically used in the social science literature. It presents conventional (e.g., sequence index plots) as well as less common types of graphs (such as parallel coordinates plots, status proportion plots, and transition plots) and discusses their particular advantages and limitations. The arguments for this assessment are based on the particular scientific interest in certain features of sequences and on Cleveland’s rules of perception. Additionally, an attempt is made to find practical solutions to avoid problems in the depiction of sequential and transition data.

Keywords

Major Conclusion Graphical Display Graph Construction Frequent Sequence Graphical Element 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer New York Heidelberg Dordrecht London 2014

Authors and Affiliations

  1. 1.WZB Berlin Social Science CenterBerlinGermany

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