Sequence analysis in social sciences

In social sciences, sequence analysis (SA) is concerned with the analysis of sets of categorical sequences that typically describe longitudinal data. Analyzed sequences are encoded representations of, for example, individual life trajectories such as family formation, school to work transitions, working careers, but they may also describe daily or weekly time use or represent the evolution of observed or self-reported health, of political behaviors, or the development stages of organizations. Such sequences are chronologically ordered unlike words or DNA sequences for example.

SA is a longitudinal analysis approach that is holistic in the sense that it considers each sequence as a whole. SA is essentially exploratory. Broadly, SA provides a comprehensible overall picture of sets of sequences with the objective of characterizing the structure of the set of sequences, finding the salient characteristics of groups, identifying typical paths, comparing groups, and more generally studying how the sequences are related to covariates such as sex, birth cohort, or social origin.

Introduced in the social sciences in the 1980s by Andrew Abbott,^[1][2] SA has gained much popularity after the release of dedicated software such as the SQ^[3] and SADI^[4] addons for Stata and the TraMineR R package^[5] with its companions TraMineRextras^[6] and WeightedCluster.^[7]

Despite some connections, the aims and methods of SA in social sciences strongly differ from those of sequence analysis in bioinformatics.