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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. [ABSTRACT FROM AUTHOR]

Almost a decade and a half after the start of the civil disturbances in Ulster, there remains scholarly disagreement over how extensive economic disparities between Protestants and Catholics were when the disturbances began in the late 1960s. This paper applies multivariate regression techniques to 1968 survey data to examine these disparities. The picture that emerges is a complex one. The apparently lower levels of occupational status among Catholics appear not to be due to discrimination but largely reflect differences in education and family background. But a substantial disparity in income remains, and cannot be accounted for so easily. More detailed analysis shows that Catholics born into elite families were greatly disadvantaged, while those born into average families were only slightly disadvantaged, if at all. [ABSTRACT FROM AUTHOR]