Exploring sequences: a graphical tool based on multi‐dimensional scaling

Sequence analysis has become one of the most used and discussed tools to describe life course trajectories. We introduce a new tool for the graphical exploratory analysis of sequences. Our plots combine standard sequence plots with the results that are provided by multi-dimensional scaling. We apply our procedure to describe work and family careers of Israeli women by using data from the Israel Social Mobility Survey. We first focus on some preliminary choices relative to the definition of the sequences: the age span, the length of the sequences and the set of states registered in each time period. We then describe how our plots can be used to gain insights about the main features of sequences and about the relationships between sequences and external information. Copyright (c) 2009 Royal Statistical Society.

[1]  Miguel A. Malo,et al.  Employment status mobility from a life-cycle perspective , 2003 .

[2]  Michael Anyadike-Danes,et al.  Predicting successful and unsuccessful transitions from school to work by using sequence methods , 2002 .

[3]  Ulrich Kohler,et al.  Sequence Analysis with Stata , 2006 .

[4]  A. Abbott Sequence analysis: new methods for old ideas , 1995 .

[5]  Gilbert Ritschard,et al.  Visualisation et classification des parcours de vie , 2008, EGC.

[6]  Raffaella Piccarreta,et al.  Clustering work and family trajectories by using a divisive algorithm , 2007 .

[7]  Gary Pollock,et al.  Holistic trajectories: a study of combined employment, housing and family careers by using multiple‐sequence analysis , 2007 .

[8]  Stefani Scherer,et al.  Early Career Patterns - A Comparison of Great Britain and West Germany , 2001 .

[9]  Brendan Halpin,et al.  Class careers as sequences : An optimal matching analysis of work-life histories , 1998 .

[10]  Jengnan Tzeng,et al.  Multidimensional scaling for large genomic data sets , 2008, BMC Bioinformatics.

[11]  Brendan Halpin,et al.  Tracks through time and continuous processes: transitions, sequences, and social structure , 2003 .

[12]  Shin-Kap Han,et al.  Clocking Out: Temporal Patterning of Retirement1 , 1999, American Journal of Sociology.

[13]  J. H. Ward Hierarchical Grouping to Optimize an Objective Function , 1963 .

[14]  Cees H. Elzinga,et al.  Sequence Similarity , 2003 .

[15]  A. Abbott,et al.  Measuring Resemblance in Sequence Data: An Optimal Matching Analysis of Musicians' Careers , 1990, American Journal of Sociology.

[16]  Richard D. Wiggins,et al.  Transitions from school to work in a changing social context , 2001 .

[17]  Ulrich Kohler,et al.  Stata Tip 25: Sequence Index Plots , 2005 .

[18]  T. Chan,et al.  Optimal Matching Analysis: A Methodological Note on Studying Career Mobility , 1995 .

[19]  M. Savage,et al.  Ascription into Achievement: Models of Career Systems at Lloyds Bank, 1890-1970 , 1996, American Journal of Sociology.

[20]  Brian Francis,et al.  Visualization of Event Histories , 1996 .

[21]  Raffaella Piccarreta,et al.  Strings of Adulthood: A Sequence Analysis of Young British Women’s Work-Family Trajectories , 2007 .