Methodological Advances in Sequence Analysis

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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 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 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 Sequence Analysis Multistate Model (SAMM) procedure. The SAMM procedure allows studying 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: (i) SA-related methods are used to identify ideal-typical patterns of changes within trajectories obtained by considering sequence of states over a predefined time span; (ii) 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 the German reunification affected these trajectories in the two sub-societies.