TY - BOOK ID - 1489300 TI - Event history modeling : a guide for social scientists AU - Box-Steffensmeier, Janet M. AU - Jones, Bradford S. PY - 2004 SN - 0521837677 0521546737 0511194056 9780511194054 9780521837675 9780521546737 0511196091 9780511196096 051119479X 9780511194795 0511195435 9780511195433 9780511790874 0511790872 9786610477517 6610477515 1107150426 1280477512 0511314418 PB - Cambridge ; New York : Cambridge University Press, DB - UniCat KW - Quantitative methods in social research KW - Artificial intelligence. Robotics. Simulation. Graphics KW - Event history analysis KW - Social sciences KW - History KW - Evénement KW - Sciences sociales KW - Histoire KW - Computer simulation. KW - Methodology. KW - Simulation par ordinateur KW - Méthodologie KW - Computer simulation KW - Methodology KW - -Social sciences KW - -History KW - -#SBIB:303H12 KW - #SBIB:303H4 KW - Annals KW - Auxiliary sciences of history KW - Behavioral sciences KW - Human sciences KW - Sciences, Social KW - Social science KW - Social studies KW - Civilization KW - Methoden en technieken: sociale wetenschappen KW - Informatica in de sociale wetenschappen KW - Evénement KW - Méthodologie KW - #SBIB:303H12 KW - Historiography KW - Social Sciences KW - Political Science KW - Event history analysis - Computer simulation KW - Social sciences - Methodology KW - History - Methodology UR - https://www.unicat.be/uniCat?func=search&query=sysid:1489300 AB - 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. ER -