A compendium of studies drawn from an international conference, this volume includes the newest and most substantive work on event history analysis. Researchers at four institutions convened, shared models of analysis, and collected their findings for the first time. The studies included represent work done in the following organizations: The Max Planck Institute in West Germany; The U.S. Social Science Research Council's Committee on Comparative Stratification; The Special Research Unit of the Deutsche Forschungsgemeinschaft; and MASO, and informal German working group on mathematical sociology. This book is one of twelve in the University of Wisconsin Press's Life Course Studies series, arranged by the series editors David L. Featherman and David I. Kertzer.

"In a manner similar to many other titles within the Applied Social Research Methods Series, this 182-page book thoroughly covers many of the specific methodological hurdles encountered in implementing event history analysis (EHA). The Applied Social Research Methods Series' ... is the result of careful subject selection. ... Consistent with the practical orientation of the book, each of the application sections provides useful insights into data structure problems and programming notes. ... Kazuo Yamaguchi's insightful review of problems in structuring EHA models is useful for those contemplating life-course research. ... We strongly recommend its inclusion in the libraries of marketing researchers and its inclusion on suggested reading lists of graduate research method seminars."--Journal of Marketing Research "This book, which is part of Sage Publications' Applied Social Research Methods Series, is a practical guide for those interested in using event history analysis. ... The book's strength is that it is well written and easy to understand. Even those with limited statistical backgrounds can follow the discussion and the systematic progression from the simpler to the more complex models (although the author provides ample references for those wanting a more rigorous discussion). ... Upon finishing the book, I found myself wondering about specific accounting questions that might be addressed using event history analysis. There are many, and in fact, most issues can be recast in an events framework. ... In sum, I recommend this book to anyone wanting to use event history analysis whether to apply to new research questions or to provide a fresh look at old questions." --The Accounting Review "A significant introduction to the event-history literature that provides the background to implement this difficult methodology successfully and that can be supplemented with other, more advanced texts. It will undoubtedly become a prized text among students and a valuable reference for the research community." --Contemporary Sociology As a research tool event history analysis has recently become a key technique for researchers, professionals and students in a wide range of disciplines. However, despite this increasing interest, few resources exist which clearly examine this technique. Now, Event History Analysis provides a systematic introduction to models, methods and applications of event history analysis. Kazuo Yamaguchi emphasizes "hands on" information, including the use and misuse of samples, models, and covariates in applications, the structural arrangement of input data, the specification of various models in such computer programs as SAS-LOGIST and SPSS-LOGLINEAR, and the interpretation of parameters estimated from models. This timely book also offers such significant topics as missing data, hazard rate, Cox's partial likelihood model, survivor function, and discrete-time logit models for both one-way and two-way transitions. Event History Analysis is essential for researchers, professionals and students of public health, sociology, labor economics, political science, and organization studies.-Provided by published.

Drawing on recent "event history" analytical methods from biostatistics, engineering, and sociology, this clear and comprehensive monograph explains how longitudinal data can be used to study the causes of deaths, crimes, wars, and many other human events. Allison shows why ordinary multiple regression is not suited to analyze event history data, and demonstrates how innovative regression - like methods can overcome this problem. He then discusses the particular new methods that social scientists should find useful.

The aim of this book is to bridge the gap between standard textbook models and a range of models where the dynamic structure of the data manifests itself fully. The common denominator of such models is stochastic processes. The authors show how counting processes, martingales, and stochastic integrals fit very nicely with censored data. Beginning with standard analyses such as Kaplan-Meier plots and Cox regression, the presentation progresses to the additive hazard model and recurrent event data. Stochastic processes are also used as natural models for individual frailty; they allow sensible interpretations of a number of surprising artifacts seen in population data. The stochastic process framework is naturally connected to causality. The authors show how dynamic path analyses can incorporate many modern causality ideas in a framework that takes the time aspect seriously. To make the material accessible to the reader, a large number of practical examples, mainly from medicine, are developed in detail. Stochastic processes are introduced in an intuitive and non-technical manner. The book is aimed at investigators who use event history methods and want a better understanding of the statistical concepts. It is suitable as a textbook for graduate courses in statistics and biostatistics.

With an emphasis on social science applications, Event History Analysis with R presents an introduction to survival and event history analysis using real-life examples. Keeping mathematical details to a minimum, the book covers key topics, including both discrete and continuous time data, parametric proportional hazards, and accelerated failure times. Features Introduces parametric proportional hazards models with baseline distributions like the Weibull, Gompertz, Lognormal, and Piecewise constant hazard distributions, in addition to traditional Cox regression Presents mathematical details as well as technical material in an appendix Includes real examples with applications in demography, econometrics, and epidemiology Provides a dedicated R package, eha, containing special treatments, including making cuts in the Lexis diagram, creating communal covariates, and creating period statistics A much-needed primer, Event History Analysis with R is a didactically excellent resource for students and practitioners of applied event history and survival analysis.

Serving as both a student textbook and a professional reference/handbook, this volume explores the statistical methods of examining time intervals between successive state transitions or events. Examples include: survival rates of patients in medical studies, unemployment periods in economic studies, or the period of time it takes a criminal to break the law after his release in a criminological study. The authors illustrate the entire research path required in the application of event-history analysis, from the initial problems of recording event-oriented data to the specific questions of data organization, to the concrete application of available program packages and the interpretation of the obtained results. Event History Analysis: * makes didactically accessible the inclusion of covariates in semi-parametric and parametric regression models based upon concrete examples * presents the unabbreviated close relationship underlying statistical theory * details parameter-free methods of analysis of event-history data and the possibilities of their graphical presentation * discusses specific problems of multi-state and multi-episode models * introduces time-varying covariates and the question of unobserved population heterogeneity * demonstrates, through examples, how to implement hypotheses tests and how to choose the right model.

This open access book provides innovative methods and original applications of sequence analysis (SA) and related methods for analysing longitudinal data describing life trajectories such as professional careers, family paths, the succession of health statuses, or the time use. The applications as well as the methodological contributions proposed in this book pay special attention to the combined use of SA and other methods for longitudinal data such as event history analysis, Markov modelling, and sequence network. The methodological contributions in this book include among others original propositions for measuring the precarity of work trajectories, Markov-based methods for clustering sequences, fuzzy and monothetic clustering of sequences, network-based SA, joint use of SA and hidden Markov models, and of SA and survival models. The applications cover the comparison of gendered occupational trajectories in Germany, the study of the changes in women market participation in Denmark, the study of typical day of dual-earner couples in Italy, of mobility patterns in Togo, of internet addiction in Switzerland, and of the quality of employment career after a first unemployment spell. As such this book provides a wealth of information for social scientists interested in quantitative life course analysis, and all those working in sociology, demography, economics, health, psychology, social policy, and statistics.; Provides new perspectives and methods for sequence analysis Focusses on the link between sequence analysis and other methods for longitudinal data, especially event history analysis and Markov models Stresses the complementarity of sequence analysis and other models for longitudinal data Applications of sequence analysis in a whole range of different domains This work was published by Saint Philip Street Press pursuant to a Creative Commons license permitting commercial use. All rights not granted by the work's license are retained by the author or authors.