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101 0 $aeng
135   $aur|||||||||||
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aEvent history modeling $ea guide for social scientists /$fJanet M. Box-Steffensmeier, Bradford S. Jones$b[electronic resource]
210  1$aCambridge :$cCambridge University Press,$d2004.
215   $a1 online resource (xiii, 218 pages) $cdigital, PDF file(s)
225 1 $aAnalytical methods for social research
300   $aTitle from publisher's bibliographic system (viewed on 05 Oct 2015).
311   $a0-521-54673-7 
311   $a0-521-83767-7 
320   $aIncludes bibliographical references (p. 201-211) and index.
327   $aCover; Half-title; Title; Copyright; Dedication; Contents; Figures; Tables; Preface; CHAPTER 1 Event History and Social Science; CHAPTER 2 The Logic of Event History Analysis; CHAPTER 3 Parametric Models for Single-Spell Duration Data; CHAPTER 4 The Cox Proportional Hazards Model; CHAPTER 5 Models for Discrete Data; CHAPTER 6 Issues in Model Selection; CHAPTER 7 Inclusion of Time-Varying Covariates; CHAPTER 8 Diagnostic Methods for the Event History Model; CHAPTER 9 Some Modeling Strategies for Unobserved Heterogeneity; CHAPTER 10 Models for Multiple Events
327   $aCHAPTER 11 The Social Sciences and Event HistoryAppendix Software for Event History Analysis; References; Index
330   $aEvent History Modeling, first published in 2004, provides an accessible guide to event history analysis for researchers and advanced students in the social sciences. The substantive focus of many social science research problems leads directly to the consideration of duration models, and many problems would be better analyzed by using these longitudinal methods to take into account not only whether the event happened, but when. The foundational principles of event history analysis are discussed and ample examples are estimated and interpreted using standard statistical packages, such as STATA and S-Plus. Critical innovations in diagnostics are discussed, including testing the proportional hazards assumption, identifying outliers, and assessing model fit. The treatment of complicated events includes coverage of unobserved heterogeneity, repeated events, and competing risks models. The authors point out common problems in the analysis of time-to-event data in the social sciences and make recommendations regarding the implementation of duration modeling methods.
410  0$aAnalytical methods for social research.
606   $aEvent history analysis$xComputer simulation
606   $aSocial sciences$xMethodology
606   $aHistory$xMethodology
615  0$aEvent history analysis$xComputer simulation.
615  0$aSocial sciences$xMethodology.
615  0$aHistory$xMethodology.
676   $a001.4/32
700   $aBox-Steffensmeier$b Janet M.$f1965-$0786601
702   $aJones$b Bradford S.$f1965-
801  0$bUkCbUP
801  1$bUkCbUP
906   $aBOOK
912   $a9910457685003321
996   $aEvent history modeling$92479797
997   $aUNINA