Multichannel Sequence Analysis Applied to Social Science Data

Applications of optimal matching analysis in the social sciences are typically based on sequences of specific social statuses that model the residential, family, or occupational trajectories of individuals. Despite the broadly recognized interdependence of these statuses, few attempts have been made to systematize the ways in which optimal matching analysis should be applied multidimensionally—that is, in an approach that takes into account multiple trajectories simultaneously. Based on methods pioneered in the field of bioinformatics, this paper proposes a method of multichannel sequence analysis (MCSA) that simultaneously extends the usual optimal matching analysis (OMA) to multiple life spheres. Using data from the Swiss household panel (SHP), we examine the types of trajectories obtained using MCSA. We also consider a random data set and find that MCSA offers an alternative to the sole use of ex-post sum of distance matrices by locally aligning distinct life trajectories simultaneously. Moreover, MCSA reduces the complexity of the typologies it allows to produce, without making them less informative. It is more robust to noise in the data, and it provides more reliable alignments than two independent OMA.