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

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

Object Details

• Location: Deutsche Nationalbibliothek Frankfurt am Main
• Extent: Online-Ressource
• Language: Englisch
• Notes: Veröffentlichungsversion ; begutachtet (peer reviewed) ; In: Sociological Methodology ; 48 (2018) 1 ; 103-135

Classification and Topics

• Classification: Wirtschaft

Contributors, Places and Time

• Event: Veröffentlichung
• (where): Mannheim
• (who): SSOAR, GESIS – Leibniz-Institut für Sozialwissenschaften e.V.
• (when): 2018
• Creator: Studer, Matthias ; Fasang, Anette E. ; Struffolino, Emanuela

Further information

• DOI: 10.1177/0081175017747122
• URN: urn:nbn:de:101:1-2021100709172855885503
• Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Last update: 15.08.2025, 7:21 AM CEST