Introduction to sequence analysis for social sciences

20-21 January 2022


The course gives an introduction to the theoretical and practical concepts of sequence analysis for social sciences. Sequence analysis, originally developed in biology to analyse strings of DNA, has attracted increasing attention in the social sciences for the analysis of longitudinal data. Most applications in the social sciences study life course processes, such as labour market careers, educational careers, or family formation. During the short course, we will discuss the usefulness of sequence analysis in applied social sciences as a holistic approach to investigate timing, quantum and sequencing of life course events. We will consider the practical implementation of these methods using available data. Concepts covered include the statistical representations of categorical time series, measures of sequence dissimilarity (i.e., Optimal Matching Algorithm); patterns identification in life course trajectories; classification techniques; criticisms to sequence analysis and new developments.


Day 1 (20/01/2022)

9:00-11:00 Zoom link

Slides I

(break 15 minutes)

Slides II

14:00-16:00 (Lab session) Zoom link

Code Lab I


Day 2 (21/01/2022)

9:00-10:00 Zoom link

Slides III

Barban (2013) Family Trajectories and Health: A Life Course Perspective. European Journal of Population

Pesando et al. (2021) A Sequence-Analysis Approach to the Study of the Transition to Adulthood in Low- and Middle-Income Countries. Population and Development Review

14:00-16:00 (Lab session) Zoom link

Code Lab II

Computer software

Online references

  1. A Aassve, Francesco C Billari, and R Piccarreta. Strings of adulthood: A sequence analysis of young british women’s work-family trajectories. Eur J Population, Jan 2007.
  2. A Abbott and A Hrycak. Measuring resemblance in sequence data: An optimal matching analysis of musicians’ careers. American journal of sociology, 96(1):144–185, 1990.
  3. Andrew Abbott. Sequence analysis: new methods for old ideas. Annual Review of Sociology, 21(1):93– 113, 1995.
  4. Andrew Abbott and John Forrest. Optimal matching methods for historical sequences. Journal of Interdisciplinary History, 16(3):471–494, Jan 1986.
  5. Andrew Abbott and Alexandra Hrycak. Measuring resemblance in sequence data: An optimal matching analysis of musicians’ careers.
  6. Andrew Abbott and A Tsay. Sequence analysis and optimal matching methods in sociology: Review and prospect. Sociological Methods & Research , 29(1):3, 2000.
  7. S Aisenbrey and A Fasang. New life for old ideas: The”second wave”of sequence analysis bringing the”course”back into the life course. Sociological Methods & Research, Jan 2010.
  8. N. Barban. Family trajectories and health: A life course perspective. European Journal of Population (2013)
  9. N. Barban, De Luna X., Svensson I. and Billari, F.C. “Causal effects of the timing of life course events: age at retirement and subsequent health.” (2018) ”Sociological Methods and Research
  10. Barban, N. and M. Sironi (2018) “Sequence Analysis as a Tool for Family Demography,’’ in Analytical Family Demography, The Springer Series on Demographic Methods and Population Analysis, Ed. R. Schoen , Germany: Springer.
  11. Barban N and F. C. Billari, (2012) “Classifying life course trajectories. A comparison between la- tent class and sequence analysis,” Journal of the Royal Statistical Society Series C (Applied Statistics), 61(5):765–784.
  12. Francesco C Billari and R Piccarreta. Analyzing demographic life courses through sequence analysis. Mathematical Population Studies, Jan 2005.
  13. Mary Blair-Loy. Career patterns of executive women in finance: An optimal matching analysis. American Journal of Sociology, 104(5):1346–1397, Mar 1999.
  14. Christian Brzinsky-Fay, Ulrich Kohler, and M Luniak. Sequence analysis with stata. STATA JOURNAL, Jan 2006.
  15. Cees Elzinga. Sequence analysis: Metric representations of categorical time series. Sociological Methods & Research, 2006.
  16. Cees Elzinga and Aart C Liefbroer. De-standardization of family-life trajectories of young adults: A cross-national comparison using sequence analysis. Eur J Population, 23(3):225–250, 2007.
  17. Brendan Halpin. Optimal matching analysis and life-course data: The importance of duration. Socio- logical Methods & . . . , 38(3):365, Feb 2010.
  18. ShinKap Han and Phyllis Moen. Clocking out: Temporal patterning of retirement. American Journal of Sociology, 105(1):191–236, Jul 1999.
  19. D.P Hogan. The variable order of events in the life course. American Sociological Review, 43(4):573–586, 1978.
  20. Duncan McVicar and Michael Anyadike-Danes. Predicting successful and unsuccessful transitions from school to work by using sequence methods. J Roy Stat Soc A Sta, 165(2):317–334, Jan 2002.
  21. T Mouw. Sequences of early adult transitions. On the frontier of adulthood: Theory, Jan 2005.
  22. Luca Maria Pesando, Maria Sironi, Nicola Barban, Frank F Furstenberg (2021) A Sequence-Analysis Approach to the Study of the Transition to Adulthood in Low-and Middle-Income Countries Population Development Review
  23. R Piccarreta and F.C Billari. Clustering work and family trajectories by using a divisive algorithm. Journal of the Royal Statistical Society: Series A (Statistics in Society), 170(4):1061–1078, 2007.
  24. Gary Pollock. Holistic trajectories: a study of combined employment, housing and family careers by using multiple-sequence analysis. J Roy Stat Soc A Sta, 170:167–183, Jan 2007.
  25. Sironi M., N. Barban and R. Impicciatore (2015), “The Role of Parental Social Class in the Transition to Adulthood,” Advances in Life Course Research 26, 89-104
  26. N Shoval and M Isaacson. Sequence alignment as a method for human activity analysis in space and time. Annals of the Association of American Geographers, 97(2):282–297, 2007.
  27. LL Wu. Some comments on” sequence analysis and optimal matching methods in sociology: Review and prospect”. Sociological Methods & Research 29(1):41–64, 2000.