Advanced methods for the analysis of complex event history data

Sequence analysis for social scientists

Dr Alexis Gabadinho and Matthias Studer, University of Geneva

This course is devoted to the analysis of state or event sequences describing life trajectories such as family life courses or employment histories. The course is practically oriented, including an introduction to the R statistical environment and training in the TraMineR library for mining and visualizing sequences.  Alexis Gabadinho and Matthias Studer are two of the developers of the TraMineR software.

Topics

• Introduction to sequence analysis
  • Example studies: The mvad and biofam data sets
•  Introduction to R and the TraMineR library
  •  R language basics
  • Importing data from other statistical packages
• Sequence data representations and data preparation
• Descriptive statistics and visualization of state sequence sets
  • Sequence frequency tables
  • State distribution by time point and entropy index
• Measuring complexity of life trajectories
  • Transitions
  • Longitudinal entropy
  • Turbulence
  • Complexity index
• Measuring sequence dissimilarity
  • Optimal matching
  • Overview of other metrics
• Dissimilarity based sequence analysis
  • Building typologies by clustering
  • Heterogeneity measures
  • Representative sequences
  • ANOVA-like analysis of sets of sequences
• Mining event sequences
• Brief overview of available tools for sequence analysis in Stata

Course materials

• Sequence analysis slides (including R code) (PDF, 4,432kB)
• Solutions to R exercises (PDF, 57kB)

If you have questions or problems concerning TraMineR, you can subscribe to the TraMineR mailing list

Further reading

• For an introduction to R see cran.r-project.org/doc/manuals/R-intro.pdf for example [5] (especially chapters 1-3) or [7] (especially chapters 1-7).
• A TraMineR user's guide [3] is available at TraMineR
• For applications of sequence analysis in the social sciences see for example [1, 2, 4, 6, 8]. The data set presented in [4] will be one of those used for computer exercises.
• [1] Andrew Abbott and Angela Tsay. Sequence analysis and optimal matching methods in sociology, Review and prospect. Sociological Methods and Research, 29(1):3-33, 2000 (with discussion, pp 34-76).
• [2] Francesco C. Billari. The analysis of early life courses: complex descriptions of the transition to adulthood. Journal of Population Research, 18(2):119(24)-, November 2001.
• [3] Alexis Gabadinho, Gilbert Ritschard, Matthias Studer, and Nicolas Müller. Mining sequence data in R with TraMineR: A user's guide. Technical report, Department of Econometrics and Laboratory of Demography, University of Geneva, Geneva, 2009.
• [4] Duncan McVicar and Michael Anyadike-Danes. Predicting successful and unsuccessful transitions from school to work by using sequence methods. Journal of the Royal Statistical Society, Series A  (Statistics in Society), 165(2):317-334, 2002.
• [5] Emmanuel Paradis. R for Beginners. Institut des Sciences de l' Evolution Université Montpellier II, F-34095 Montpellier, septembre 2005.
• [6] Gary Pollock. Holistic trajectories: a study of combined employment, housing and family careers by using multiple-sequence analysis. Journal of the Royal Statistical Society, Series A (Statistics in Society), 170(1):167-183, 2007.
• [7] W. N. Venables, D. M. Smith, and the R Development Core Team. An Introduction to R, 2009.
• [8] Eric Widmer and Gilbert Ritschard. The de-standardization of the life course: Are men and women equal? Advances in Life course Research, 14(1-2):28-39, 2009.

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