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An Integrated Heuristic for Validation in Sequence Analysis

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  • Piccarreta, Raffaella
  • Struffolino, Emanuela

Abstract

Sequence Analysis is a collection of tools to describe life courses represented as sequences that are increasingly applied in different fields, particularly in demography, sociology, and political sciences. Identifying typologies through cluster analysis, thus disregarding individual sequences’ peculiarities, is the aim of most applications. However, a substantive interpretation of such typology can be questionable when clusters include sequences deviating from the others. We propose an integrated approach to identify such sequences, distinguishing between sequences presenting structural peculiarities and randomly deviating sequences. We monitor the quality of partitions with respect to the amount and type of deviation in each cluster relying on novel graphical tools allowing to properly visualize and closely inspect the structure of deviating sequences. We demonstrate that the identification of deviating sequences provides relevant insights also when clusters are used as dependent or independent variables in an explanatory framework, for example in combination with multinomial logistic regression analysis.

Suggested Citation

  • Piccarreta, Raffaella & Struffolino, Emanuela, 2019. "An Integrated Heuristic for Validation in Sequence Analysis," SocArXiv v7mj8, Center for Open Science.
    • Handle: RePEc:osf:socarx:v7mj8
    DOI: 10.31219/osf.io/v7mj8
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    References listed on IDEAS

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    1. Raffaella Piccarreta & Orna Lior, 2010. "Exploring sequences: a graphical tool based on multi‐dimensional scaling," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 173(1), pages 165-184, January.
    2. Van Winkle, Zachary & Fasang, Anette Eva, 2017. "Complexity in Employment Life Courses in Europe in the Twentieth Century—Large Cross-National Differences but Little Change across Birth Cohorts," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 96(1), pages 1-30.
    3. Andrew Abbott, 1992. "From Causes to Events," Sociological Methods & Research, , vol. 20(4), pages 428-455, May.
    4. Matthias Studer & Gilbert Ritschard, 2016. "What matters in differences between life trajectories: a comparative review of sequence dissimilarity measures," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 179(2), pages 481-511, February.
    5. Hahsler, Michael & Hornik, Kurt & Buchta, Christian, 2008. "Getting Things in Order: An Introduction to the R Package seriation," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 25(i03).
    6. Anette Fasang & Marcel Raab, 2014. "Beyond Transmission: Intergenerational Patterns of Family Formation Among Middle-Class American Families," Demography, Springer;Population Association of America (PAA), vol. 51(5), pages 1703-1728, October.
    7. Duncan McVicar & Michael Anyadike‐Danes, 2002. "Predicting successful and unsuccessful transitions from school to work by using sequence methods," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 165(2), pages 317-334, June.
    8. Struffolino, Emanuela & Studer, Matthias & Fasang, Anette Eva, 2016. "Gender, education, and family life courses in East and West Germany: Insights from new sequence analysis techniques," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 29, pages 66-79.
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    Cited by:

    1. Andrade, Stefan B. & Fasang, Anette Eva & Helske, Satu & Karhula, Aleksi, 2023. "Typologies in Sequence Analysis: Practical Guidelines for Identifying Robust Cluster Solutions," SocArXiv kj8d5, Center for Open Science.

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