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Event history modeling: a guide for social scientists Box-Steffensmeier, Janet M.

By: Contributor(s): Material type: TextTextPublication details: New York Cambridge University Press 2004Description: xiii, 218 pISBN:
  • 0521546737
Subject(s): DDC classification:
  • 001.432 B6E9
Summary: Event History Modeling provides an accessible, up-to-date 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. Recent and 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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Item type Current library Collection Call number Status Date due Barcode Item holds
Book Book Ahmedabad General Stacks Non-fiction 001.432 B6E9 (Browse shelf(Opens below)) Available 181221
Total holds: 0

Analytical methods for social research--Cover

Event History Modeling provides an accessible, up-to-date 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. Recent and 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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