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      Comparison of two approaches in multichannel sequence analysis using the Swiss Household Panel

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      Longitudinal and Life Course Studies
      Bristol University Press

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          Abstract

          Sequence analysis is an established approach to study life courses. When several life domains are considered simultaneously, multichannel sequence analysis (MSA) and the extended alphabet (EA) approach are the most frequently used strategies. We compare these two methods using real data composed of four life domains (cohabitational status, children, professional status, health), and we focus on clustering since sequence analysis usually aims to identify typical patterns in sequences. As professional status trajectories, and potentially their relationship with other domains, proved to be different between men and women, the analyses were run separately by sex. We describe step by step the approach followed and the different criteria to judge the relevance of a typology. Neither of the two approaches is clearly superior, and the typologies obtained with both methods are often close. However, even if MSA is generally easier to use and applies to a broader range of situations, EA can provide original typologies in specific cases and we therefore propose guidelines for choosing between the two approaches depending on the context.

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            Ward’s Hierarchical Agglomerative Clustering Method: Which Algorithms Implement Ward’s Criterion?

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              A general method applicable to the search for similarities in the amino acid sequence of two proteins

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                Author and article information

                Journal
                Longitudinal and Life Course Studies
                Bristol University Press
                1757-9597
                December 20 2022
                December 20 2022
                : 1-32
                Affiliations
                [1 ]Swiss National Centre of Competence in Research LIVES and University of Lausanne, Switzerland
                Article
                10.1332/175795921X16698302233894
                b7c4ab07-d7fc-4f89-8cb7-ba8784e439fc
                © 2022

                https://creativecommons.org/licenses/by-nc/4.0/

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