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Dear all,<br>
<br>
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 : <a
class="moz-txt-link-freetext"
href="http://smr.sagepub.com/content/40/3/471">http://smr.sagepub.com/content/40/3/471</a>
. <br>
<br>
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. <br>
<br>
All the numerical results and plots presented in the article can
easily be
reproduced with the R script provided here: <a
class="moz-txt-link-freetext"
href="http://mephisto.unige.ch/traminer/scripts/discrepancy_analysis.R">http://mephisto.unige.ch/traminer/scripts/discrepancy_analysis.R</a><br>
<br>
Best regards, <br>
<br>
Matthias, Gilbert, Alexis et Nicolas<br>
<br>
PS: A preprint version can be downloaded from the TraMineR website.<br>
<br>
<br>
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.<br>
<br>
Abstract<br>
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.<br>
<br>
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