[Traminer-users] "Discrepancy Analysis of State Sequences" published in "Sociological Methods & Research"

[Matthias Studer]

Dear all,

We are very pleased to announce the publication of our article 
"Discrepancy Analysis of State Sequences" in the latest issue of 
"Sociological Methods & Research". It is available here : 
http://smr.sagepub.com/content/40/3/471 .

This article presents the methods for discrepancy analysis made 
available in TraMineR. Those methods allow to measure the relationship 
between state sequences and explanatory variables and to assess the 
significance of the association.

All the numerical results and plots presented in the article can easily 
be reproduced with the R script provided here: 
http://mephisto.unige.ch/traminer/scripts/discrepancy_analysis.R

Best regards,

Matthias, Gilbert, Alexis et Nicolas

PS: A preprint version can be downloaded from the TraMineR website.


Studer, M., Ritschard, G., Gabadinho, A. & Müller, N.S. (2011), 
"Discrepancy Analysis of State Sequences", Sociological Methods and 
Research. Vol. 40(3), pp. 471-510.

Abstract
In this article, the authors define a methodological framework for 
analyzing the relationship between state sequences and covariates. 
Inspired by the principles of analysis of variance, this approach looks 
at how the covariates explain the discrepancy of the sequences. The 
authors use the pairwise dissimilarities between sequences to determine 
the discrepancy, which makes it possible to develop a series of 
statistical significance--based analysis tools. They introduce 
generalized simple and multifactor discrepancy-based methods to test for 
differences between groups, a pseudo-R 2 for measuring the strength of 
sequence-covariate associations, a generalized Levene statistic for 
testing differences in the within-group discrepancies, as well as tools 
and plots for studying the evolution of the differences along the time 
frame and a regression tree method for discovering the most significant 
discriminant covariates and their interactions. In addition, the authors 
extend all methods to account for case weights. The scope of the 
proposed methodological framework is illustrated using a real-world 
sequence data set.

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