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In the original version of this book, the second author Emanuela Struffolino was missed to be added as the corresponding author and the affiliation of the author Camilla Borgna was incorrect. The second author Emanuela Struffolino has now also been included as the corresponding author and the affiliation of Camilla Borgna is corrected as Collegio Carlo Alberto, Turin, Italy.

This introductory chapter briefly describes the development of sequence analysis in social sciences from the pioneering contributions made by Andrew Abbott to date. We then discuss the future of sequence analysis, which, from our point of view, calls for a tighter interaction with other methods for longitudinal data. Lastly, we show how the papers in this bundle set the foundation towards this future.

This paper will first explore some of the tools for studying the dynamics that drives the trajectories: Event-duration models which lead to event history analysis; event-sequences models which lead to sequence analysis; multiple level models which lead to multilevel analysis; social network models which lead to multilevel social-network analysis. It then shows that these models can be classified under some more general concepts: The statistical individual concept covers event history and sequence analysis; the statistical network concept covers multilevel and social-network analysis. It seems then necessary to set up a more robust research program for demography. This research program may follow the induction’s way given by Bacon in searching for the structure of the studied phenomena and the interactions between the networks created by people. Such a program will be able to lead to a convergence of these different models.

This methodological paper presents three case studies of life course analysis in which the clustering of the sequences and probabilistic modelling have been combined or contrasted when analysing a particular research question. Latent variable hierarchical modelling is used in various ways to account for correlation in multidimensional response variables and to model underlying structures in the life course. We conclude that, while sequence analysis allows to use multidimensional and time-dependent criteria either for comparing life patterns of groups of individuals or to extract subgroups for further analysis, the SA outcome may be rather unspecific for causal-like questions.

Drawing from the literature on “glass ceilings” and “glass escalators”, we analyze gender differences in career advancement across occupations. We argue that gender-typical occupations provide different opportunities for upward mobility in part due to varying institutional rules and work organizational logics. We further extend previous research by looking at two aspects: accessibility to and likelihood of staying in leadership. Using data from the German National Education Panel Study, we ask: (1) Do men demonstrate an advantage regarding access to and staying in leadership? (2) To what extent does occupational segregation explain gender differences in upward mobility? (3) Do gender effects vary across occupations? Using event history analysis, results confirm that occupational gender segregation largely explains gender differences in upward mobility. We further find that the probability of upward mobility is lower in female and higher in male occupations; however, the male advantage is nevertheless weaker in male occupations.

The aim of this study is to investigate how disabilities and the experiences of work and family during early adulthood affected subsequent mortality in nineteenth century Sundsvall, Sweden. To achieve this, sequence analysis and event history analyses are combined, using digitised parish registers from nineteenth-century Sweden. First, occurrence and type of disability, noted at latest on their 15th birthday, is recorded. Second, life trajectories are analysed using sequence analysis between ages 15 and 33 in order to determine homogeneous groups, given their experience of work and family in their early adulthood. Important demographic events that occur in the life of young adults—first occupation, first marriage and first child—are recorded yearly and cause the person’s trajectory to change state. Third, the groups derived are used as explanatory variables in combination with disability and other variables in Cox regressions with mortality as outcome. The individuals are followed from their 33rd birthday as long as the registers permit and it is noted if the period ends with death or if the observation is censored. The main findings are that the groups found for men are significantly associated with mortality and that mentally disabled women seem to have excess mortality. They also show that sequence analysis can be a valuable tool in summarising individuals’ life paths for use in subsequent analysis.

In this article, we propose an innovative method which is a combination of Sequences Analysis and Event History Analysis. We called this method Sequence History Analysis (SHA). We start by identifying typical past trajectories of individuals over time by using Sequence Analysis. We then estimate the effect of these typical past trajectories on the event under study using discrete-time models. The aim of this approach is to estimate the effect of past trajectories on the chances of experiencing an event. We apply the proposed methodological approach to an original study of the effect of past childhood co-residence structures on the chances of leaving the parental home in Switzerland. The empirical research was based on the LIVES Cohort study, a panel survey that started in autumn 2013 in Switzerland. Analyses show that it is not only the occurrence of an event that increases the risk of experiencing another event, but also the order in which various states occurred. What is more, it seems that two features have a significant influence on departure from the parental home: the co-residence structures and the arrival or departure of siblings from the parental home.

One reason to use sequence analysis in social research is to identify systematic differences in the structuring of ordered social phenomena across groups. This is often done via optimal matching analysis, discrepancy analysis, event-history analysis, or related approaches. These approaches can be supplemented with network-analytic approaches when sequential phenomena combine to form a larger structure of intersecting pathways, or “sequence-networks.” In these cases, analysts can employ network-analytic methods to supplement existing sequence methods in order to gain additional insight into the structure of intersecting sequences. This chapter identifies several useful network techniques, shows how they correspond to sequence-related concerns, and describes how to compare multiple sequence-network structures to each other. To demonstrate, I use time-diary data from the 2015 American Time Use Survey (ATUS) to compare the complex of daily activity pathways that were formed by two groups: Younger adults and older adults. The sequence-networks of these groups reveal key differences in the structure of these groups’ everyday lives. The chapter closes by discussing some of the theoretical and methodological implications of using network methods to supplement conventional sequence methods.

This paper uses relational sequence networks to discern gendered patterns in migration biographies. Starting from an integrated bimodal network model of kinship and mobility relations, sequence networks are constructed by classifying mobility events according to the social (kinship or other) relation between the individuals they link together as migrants and hosts. Itineraries thus are conceived of as walks in a space of relational positions. Drawing on 60 migration itineraries from rural South-east Togo (embedded in a larger network of 509 itineraries), we show that male and female trajectories do not so much differ in their degree of mobility as in the topology of the social spaces they traverse and in the structure of the social sequences they trace. Rather than just confirming the macro-tendencies for male and female mobility patterns stated in the demographic literature, sequence network analysis yields insight into the relational logics that bring these tendencies about. Data have been analyzed with the open source software Puck, which implements the model presented in the paper.

This chapter brings together methodological tools helping to compare sets of multiphase sequences, i.e., sequences structured into successive phases. First, the notions of phase and multiphase sequences are presented. Phases are defined by two properties—internal consistency and processual location—that imply two crucial methodological assumptions: phases are regarded both as sites of narratives and as dissociated incommensurable episodes. Three parameters of the division into phases are distinguished and exemplified: the reference frame of the division, the alphabet(s) of the dissociated phases, and the phase-structure of the sequences. Two ways of rendering multiphase sequences are considered: event-aligned representations and sliced representations. We then introduce multiphase optimal matching, a measure of pairwise distances between multiphase sequences the logic of which can be extended to other dissimilarity measures. Throughout the chapter, an example of two-phase sequences drawn from a study of the careers of participants in professional competitions in France is developed.