Fundamental principles in drawing inference from sequence analysis , Southampton, GB Southampton Statistical Sciences Research Institute, University of Southampton 39pp.
(S3RI Applications & Policy Working Papers, A10/02).
Individual life courses are dynamic and can be represented as a sequence of states for some portion of their experiences. More generally, study of such sequences has been made in many fields around social science; for example, sociology, linguistics, psychology, and the conceptualisation of subjects progressing through a sequence of states is common. However, many models and sets of data allow only for the treatment of aggregates or transitions, rather than interpreting whole sequences. The temporal aspect of the analysis is fundamental to any inference about the evolution of the subjects but assumptions about time are not normally made explicit. Moreover, without a clear idea of what sequences look like, it is impossible to determine when something is not seen whether it was not actually there. Some principles are proposed which link the ideas of sequences, hypothesis, analytical framework, categorisation and representation; each one being underpinned by the consideration of time. To make inferences about sequences, one needs to: understand what these sequences represent; the hypothesis and assumptions that can be derived about sequences; identify the categories within the sequences; and data representation at each stage. These ideas are obvious in themselves but they are interlinked, imposing restrictions on each other and on the inferences which can be drawn
||sequence analysis, dissimilarity algorithms, stochastic models, event histories, inference, visualisation, clustering
|15 March 2010||Published|
||15 Mar 2010
||18 Apr 2017 20:15
|Further Information:||Google Scholar|
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