Publication type
Journal Article
Authors
Publication date
June 1, 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
Volume: 170 (4):1061-1078
DOI
http://dx.doi.org/10.1111/j.1467-985X.2007.00495.x
Subjects
Notes
Albert Sloman Library Periodicals *restricted to Univ. Essex registered users*
#509813