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Event History Modeling, first published in 2004, provides an accessible guide to event history analysis for researchers and advanced students in the social sciences. The substantive focus of many social science research problems leads directly to the consideration of duration models, and many problems would be better analyzed by using these longitudinal methods to take into account not only whether the event happened, but when. The foundational principles of event history analysis are discussed and ample examples are estimated and interpreted using standard statistical packages, such as STATA and S-Plus. Critical innovations in diagnostics are discussed, including testing the proportional hazards assumption, identifying outliers, and assessing model fit. The treatment of complicated events includes coverage of unobserved heterogeneity, repeated events, and competing risks models. The authors point out common problems in the analysis of time-to-event data in the social sciences and make recommendations regarding the implementation of duration modeling methods.

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Through the use of an original data set of bill initiation activity in six presidential democracies, we advance scholarly understanding of how the institutional incentives faced by legislative candidates influence representation. We extend and adapt theory, derived primarily from the experience of the U.S. Congress, demonstrating its viability, once assumed constants from the U.S. case are explicitly modeled, in quite distinct institutional contexts. In particular, we find the focus of individual legislators on national versus parochial concerns responds to the incentives provided by the candidate selection process, general election rules, legislator career patterns, and interbranch relations.

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Since 1990, the standard statistical approach for studying state policy adoption has been an event history analysis using binary link models, such as logit or probit. In this article, we evaluate this logit-probit approach and consider some alternative strategies for state policy adoption research. In particular, we discuss the Cox model, which avoids the need to parameterize the baseline hazard function and, therefore, is often preferable to the logit-probit approach. Furthermore, we demonstrate how the Cox model can be modified to deal effectively with repeatable and competing events, events that the logit-probit approach cannot be used to model.

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