Is Optimal Matching Suboptimal?

Abstract

Optimal matching (OM) is a method for measuring the similarity between pairs of sequences (e.g., work histories). This article discusses two problems with optimal matching. First, the author identifies a flaw in OM ‘‘indel costs’’ and proposes a solution to this flaw. Second, the author discusses the need for benchmarks to measure the added value of OM and to test competing versions. To that end, the author conducts an empirical test of traditional OM, the alternative localized OM, and sequence comparison. The test documents the problem with traditional OM and shows that it is solved by localized OM. The test also demonstrates the value of OM and sequence comparison in examining occupational sequences; both methods capture variation beyond traditional human capital and status attainment measures, although the marginal improvements of OM over sequence comparison may not justify its computational complexity. These results point to the need for more systematic approaches to sequence analysis methods.

Diverse pathways in becoming an adult: The role of structure, agency and context

“…Methodological advances, such as the development of sequence analysis (Abbott, 1995;Abbott & Tsay, 2000) enable a more holistic understanding of changing youth transitions, taking into account multiple transitions simultaneously as well as the complexity and reversibility in their timing and sequencing. Despite criticism of its underlying assumptions (Hollister, 2009;Wu, 2000) recent evolutions of sequence analysis (Aisenbrey & Fasang, 2010;Brzinsky-Fay & Kohler, 2010;Gauthier et al, 2014) suggest that it has the potential to enhance our understanding of changing youth transitions, their patterning and temporal nature, and will be applied in this study.…”

Ethnic differences in family trajectories of young adult women in the Netherlands: Timing and sequencing of events

“…The distance between two sequences is defined as the total cost of the smallest number of operations required to match the sequences. Although OM is by far the most frequently used metric for sequence analysis in the social sciences, the literature is still inconclusive about the best solution for specifying the costs of the three operations (Gauthier et al 2009;Hollister 2009). In this study, we opt for unitary insertion/deletion costs and empirically define substitution costs as the inverse of the transition rates, following the approach of previous studies (e.g., Aassve, Billari, and Piccarreta 2007;Kleinepier, de Valk, and van Gaalen 2015;Widmer and Ritschard 2009).…”

Partnership formation and dissolution over the life course: applying sequence analysis and event history analysis in the study of recurrent events

“…The latter way is automatic and has been said to reduce subjectivity (Aisenbrey & Fasang, 2010;Gauthier, Widmer, Bucher, & Notredame, 2009 ongoing debate and many modifications to the basic options have been suggested (e.g. Aisenbrey & Fasang, 2010;Gauthier et al, 2009;Halpin, 2010;Hollister, 2009;Lesnard, 2010). We set costs that would lead to clusters that separate histories of stable and unstable partnerships from those with long periods of living single or divorced/separated.…”