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Estimating the Relationship between Time-varying Covariates and Trajectories: The Sequence Analysis Multistate Model Procedure

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  • Studer, Matthias
  • Struffolino, Emanuela
  • Fasang, Anette Eva

Abstract

The relationship between processes and time-varying covariates is of central theoretical interest in addressing many social science research questions. On the one hand, event history analysis (EHA) has been the chosen method to study these kinds of relationships when the outcomes can be meaningfully specified as simple instantaneous events or transitions. On the other hand, sequence analysis (SA) has made increasing inroads into the social sciences to analyze trajectories as holistic “process outcomes.” We propose an original combination of these two approaches called the sequence analysis multistate model (SAMM) procedure. The SAMM procedure allows the study of the relationship between time-varying covariates and trajectories of categorical states specified as process outcomes that unfold over time. The SAMM is a stepwise procedure: (1) SA-related methods are used to identify ideal-typical patterns of changes within trajectories obtained by considering the sequence of states over a predefined time span; (2) multistate event history models are estimated to study the probability of transitioning from a specific state to such ideal-typical patterns. The added value of the SAMM procedure is illustrated through an example from life-course sociology on how (1) time-varying family status is associated with women’s employment trajectories in East and West Germany and (2) how German reunification affected these trajectories in the two subsocieties.

Suggested Citation

  • Studer, Matthias & Struffolino, Emanuela & Fasang, Anette Eva, 2018. "Estimating the Relationship between Time-varying Covariates and Trajectories: The Sequence Analysis Multistate Model Procedure," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 48(1), pages 103-135.
    • Handle: RePEc:zbw:espost:191543
    DOI: 10.1177/0081175017747122
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    References listed on IDEAS

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    1. Courgeau, Daniel & Lelievre, Eva, 1993. "Event History Analysis in Demography," OUP Catalogue, Oxford University Press, number 9780198287384.
    2. Jan M. Hoem & Michaela Kreyenfeld, 2006. "Anticipatory analysis and its alternatives in life-course research," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 15(16), pages 461-484.
    3. Michaela Kreyenfeld, 2004. "Fertility Decisions in the FRG and GDR: An Analysis with Data from the German Fertility and Family Survey," Demographic Research Special Collections, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 3(11), pages 275-318.
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    9. 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.
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    11. Jan M. Hoem & Michaela R. Kreyenfeld, 2006. "Anticipatory analysis and its alternatives in life-course research. Part 1: Education and first childbearing," MPIDR Working Papers WP-2006-006, Max Planck Institute for Demographic Research, Rostock, Germany.
    12. Michaela R. Kreyenfeld, 2004. "Fertility decisions in the FRG and GDR," MPIDR Working Papers WP-2004-008, Max Planck Institute for Demographic Research, Rostock, Germany.
    13. Jan M. Hoem & Michaela R. Kreyenfeld, 2006. "Anticipatory analysis and its alternatives in life-course research. Part 2: Marriage and first birth," MPIDR Working Papers WP-2006-007, Max Planck Institute for Demographic Research, Rostock, Germany.
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    Cited by:

    1. Cristina Samper Mejia, 2023. "The Interplay Between the Early Work and Family Trajectories of Young Adult Women Born in West Germany: Differences by Parental Origins," Journal of International Migration and Integration, Springer, vol. 24(1), pages 345-368, March.
    2. Marcantonio Caltabiano & Silvia Meggiolaro & Valentina Tocchioni, 2023. "The impact of parental separation on the pattern of transition to adulthood in Italy," Econometrics Working Papers Archive 2023_07, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti".
    3. Struffolino, Emanuela & Van Winkle, Zachary, 2019. "Is there only one way out of in-work poverty? Difference by gender and race in the US," Discussion Papers, Research Group Demography and Inequality SP I 2019-601, WZB Berlin Social Science Center.
    4. Giorgos Bakoyannis & Dipankar Bandyopadhyay, 2022. "Nonparametric tests for multistate processes with clustered data," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 74(5), pages 837-867, October.
    5. Florianne C. J. Verkroost, 2022. "A Bayesian multivariate hierarchical growth curve model to examine cumulative socio‐economic (dis)advantage among childless adults and parents," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(4), pages 2234-2276, October.
    6. Liao, Tim F. & Bolano, Danilo & Brzinsky-Fay, Christian & Cornwell, Benjamin & Fasang, Anette Eva & Helske, Satu & Piccarreta, Raffaella & Raab, Marcel & Ritschard, Gilbert & Struffolino, Emanuela & S, 2022. "Sequence analysis: Its past, present, and future," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 107, pages 1-1.

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