Optimal Matching Analysis and Life-Course Data: The Importance of Duration
By Brendan Halpin. 2010
108 citations recorded by the source corpus.
The optimal matching (OM) algorithm is widely used for sequence analysis in sociology. It has a natural interpretation for discrete-time sequences but is also widely used for life-history data, which are continuous in time. Life-history data are arguably better dealt with in terms of episodes rather than as strings of time-unit observations, and in this article, the author examines whether the OM algorithm is unsuitable for such sequences. A modified version of the algorithm is proposed, weighting OM’s elementary operations inversely with episode length. In the general case, the modified algorithm produces pairwise distances much lower than the standard algorithm, the more the sequences are composed of long spells in the same state. However, where all the sequences in a data set consist of few long spells, and there is low variability in the number of spells, the modified algorithm generates an overall pattern of distances that is not very different from standard OM.
Subfields: Artificial Intelligence, Genetics, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/d28b897ef84a5716e878fbdf88c13b3f441c1a88
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Citations in the corpus
- What matters in differences between life trajectories: a comparative review of sequence dissimilarity measures (2016, methodology): “However, in the first case, the insertion affects only the time that is spent in the spell in state a, whereas, in the second case, it changes the sequencing (Hollister, 2009; Halpin, 2010).”