TY - JOUR
T1 - What matters in differences between life trajectories: A comparative review of sequence dissimilarity measures
JF - Journal of the Royal Statistical Society: Series A (Statistics in Society)
Y1 - 2016
A1 - Studer, Matthias
A1 - Ritschard, Gilbert
KW - dissimilarity
KW - distance
KW - duration
KW - optimal matching
KW - sequencing
KW - spells
KW - state sequences
KW - timing
AB - This is a comparative study of the multiple ways of measuring dissimilarities between state sequences. For sequences describing life courses, such as family life trajectories or professional careers, the important differences between the sequences essentially concern the sequencing (the order in which successive states appear), the timing, and the duration of the spells in the successive states. Even if some distance measures underperform, it has been shown that there is no universally optimal distance index and that the choice of a measure depends on which aspect we want to focus on. This study also introduces novel ways of measuring dissimilarities that overcome the flaws in existing measures.
VL - 179
CP - 2
PY - 10.1111/rssa.12125
ER -
TY - JOUR
T1 - A comparative review of sequence dissimilarity measures
JF - LIVES Working Papers
Y1 - 2014
A1 - Studer, Matthias
A1 - Ritschard, Gilbert
KW - dissimilarity
KW - distance
KW - duration
KW - optimal matching
KW - sequencing
KW - spells
KW - state sequences
KW - timing
AB - This is a comparative study of the multiple ways of measuring dissimilarities between state sequences. For sequences describing life courses, such as family life trajectories or professional careers, the important differences between the sequences essentially concern the sequencing (the order in which successive states appear), the timing, and the duration of the spells in the successive states. Even if some distance measures underperform, it has been shown that there is no universally optimal distance index and that the choice of a measure depends on which aspect we want to focus on. This study also introduces novel ways of measuring dissimilarities that overcome the flaws in existing measures.
PB - NCCR LIVES
CY - Lausanne
VL - 2014
CP - 33
PY - 10.12682/lives.2296-1658.2014.33
ER -
TY - JOUR
T1 - WeightedCluster Library Manual: A practical guide to creating typologies of trajectories in the social sciences with R
JF - LIVES Working Papers
Y1 - 2013
A1 - Studer, Matthias
KW - analysis of sequences
KW - cluster
KW - cluster quality measure
KW - distance
KW - life course
KW - optimal matching
KW - R
KW - trajectory
KW - typology
KW - weighting
AB - This manual has a twofold aim: to present the WeightedCluster library and offer a step-by-step guide to creating typologies of sequences for the social sciences. In particular, this library makes it possible to represent graphically the results of a hierarchical cluster analysis, to group identical sequences in order to analyse a larger number of sequences, to compute a set of measures of partition quality and also an optimized PAM (Partitioning Around Medoids) algorithm taking account of weightings. The library also offers procedures to facilitate the choice of a particular clustering solution and to choose the optimal number of groups. In addition to the methods, we also discuss the building of typologies of sequences in the social sciences and the assumptions underlying this operation. In particular we clarify the place that should be given to the creation of typologies in the analysis of sequences. We thus show that these methods offer an important descriptive point of view on sequences by bringing to light recurrent patterns. However, they should not be used in a confirmatory analysis, since they can point to misleading conclusions.
PB - NCCR LIVES
CY - Lausanne
VL - 2013
CP - 24
PY - 10.12682/lives.2296-1658.2013.24
ER -
TY - JOUR
T1 - Analyzing and visualizing state sequences in R with TraMineR
JF - Journal of Statistical Software
Y1 - 2011
A1 - Gabadinho, Alexis
A1 - Ritschard, Gilbert
A1 - Nicolas S Müller
A1 - Studer, Matthias
KW - categorical sequences
KW - dissimilarities
KW - optimal matching
KW - R
KW - representative sequences
KW - sequence complexity
KW - sequence visualization
KW - state sequences
AB - This article describes the many capabilities offered by the TraMineR toolbox for categorical sequence data. It focuses more specifically on the analysis and rendering of state sequences. Addressed features include the description of sets of sequences by means of transversal aggregated views, the computation of longitudinal characteristics of individual sequences and the measure of pairwise dissimilarities. Special emphasis is put on the multiple ways of visualizing sequences. The core element of the package is the state sequence object in which we store the set of sequences together with attributes such as the alphabet, state labels and the color palette. The functions can then easily retrieve this information to ensure presentation homogeneity across all printed and graphical displays. The article also demonstrates how TraMineR’s outcomes give access to advanced analyses such as clustering and statistical modeling of sequence data.
PB - The American Statistical Association
CY - Alexandria, VA
VL - 40
UR - http://www.jstatsoft.org/v40/i04
CP - 4
ER -
TY - JOUR
T1 - Discrepancy Analysis of State Sequences
JF - Sociological Methods & Research
Y1 - 2011
A1 - Studer, Matthias
A1 - Ritschard, Gilbert
A1 - Gabadinho, Alexis
A1 - Nicolas S Müller
KW - analysis of variance
KW - dissimilarities
KW - distance
KW - homogeneity in discrepancies
KW - Levene test
KW - optimal matching
KW - permutation test
KW - regression tree
KW - state sequence
KW - tree-structured ANOVA
AB - 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-R2 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.
VL - 40
Y1 - 08/2011
CP - 3
J1 - Sociological Methods & Research
PY - 10.1177/0049124111415372
ER -