Skip to content

Journal Article

Clustering work and family trajectories by using a divisive algorithm

Authors

Publication date

2007

Abstract

We present an approach to the construction of clusters of life course trajectories and use it to obtain ideal types of trajectories that can be interpreted and analysed meaningfully. We represent life courses as sequences on a monthly timescale and apply optimal matching analysis to compute dissimilarities between individuals. We introduce a new divisive clustering algorithm which has features that are in common with both Ward's agglomerative algorithm and classification and regression trees. We analyse British Household Panel Survey data on the employment and family trajectories of women. Our method produces clusters of sequences for which it is straightforward to determine who belongs to each cluster, making it easier to interpret the relative importance of life course factors in distinguishing subgroups of the population. Moreover our method gives guidance on selecting the number of clusters.

Published in

Journal of the Royal Statistical Society Series A (Statistics in Society)

Volume

170 (4):1061-1078

DOI

http://dx.doi.org/10.1111/j.1467-985X.2007.00495.x

Subjects

Statistical Analysis and Demography

Links

http://serlib0.essex.ac.uk/search?/sjournal+of+the+royal+statistical+society/sjournal+of+the+royal+statistical+society/1%2C7%2C13%2CB/exact&FF=sjournal+of+the+royal+statistical+society+series+a+statistics+in+society&1%2C2%2C/indexsort=-

Notes

Albert Sloman Library Periodicals *restricted to Univ. Essex registered users*

#509813


Research home

Research home

News

Latest findings, new research

Publications search

Search all research by subject and author

Podcasts

Researchers discuss their findings and what they mean for society

Projects

Background and context, methods and data, aims and outputs

Events

Conferences, seminars and workshops

Survey methodology

Specialist research, practice and study

Taking the long view

ISER's annual report

Themes

Key research themes and areas of interest