Classification of longitudinal career paths

Classification of longitudinal career paths The aim of the present article is to classify, in terms of contractual stability, the careers of the workers in a specified territorial context (Province of Milan-Italy), utilizing large administrative archives. The final goal is a synthetic clustering that identifies individuals in homogeneous groups regarding the longitudinal sequences of contractual typologies occurring in the evolution of vocational experiences during their career, identifying, on the one hand, the worker profiles that remain stable in each contractual typology and on the other hand, the profiles that improve or worsen contractual stability over time. Methodologically, our approach uses a combination of scaling methods to estimate stability scores of each contractual typology and Latent mixture models to cluster similar trajectories. Specifically, the scores of contractual stability were performed by Multidimensional Scaling with individual preferences, taking into account the ordinal nature of distances among contractual typologies and the heterogeneity factors of the subjects. Further, Latent Growth Mixture models, capitalizing the longitudinal property of data sequences, were proposed to identify distinctive, prototypical developmental trajectories of contractual stability within the analyzed population. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Quality & Quantity Springer Journals

Classification of longitudinal career paths

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Publisher
Springer Netherlands
Copyright
Copyright © 2011 by Springer Science+Business Media B.V.
Subject
Social Sciences, general; Methodology of the Social Sciences; Social Sciences, general
ISSN
0033-5177
eISSN
1573-7845
D.O.I.
10.1007/s11135-011-9578-y
Publisher site
See Article on Publisher Site

Abstract

The aim of the present article is to classify, in terms of contractual stability, the careers of the workers in a specified territorial context (Province of Milan-Italy), utilizing large administrative archives. The final goal is a synthetic clustering that identifies individuals in homogeneous groups regarding the longitudinal sequences of contractual typologies occurring in the evolution of vocational experiences during their career, identifying, on the one hand, the worker profiles that remain stable in each contractual typology and on the other hand, the profiles that improve or worsen contractual stability over time. Methodologically, our approach uses a combination of scaling methods to estimate stability scores of each contractual typology and Latent mixture models to cluster similar trajectories. Specifically, the scores of contractual stability were performed by Multidimensional Scaling with individual preferences, taking into account the ordinal nature of distances among contractual typologies and the heterogeneity factors of the subjects. Further, Latent Growth Mixture models, capitalizing the longitudinal property of data sequences, were proposed to identify distinctive, prototypical developmental trajectories of contractual stability within the analyzed population.

Journal

Quality & QuantitySpringer Journals

Published: Aug 25, 2011

References

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