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Event history analysisùa method for explaining why some people are more likely to experience a particular event, transition, or change than other peopleùhas been useful in the social sciences for studying the processes of social change. One of the main difficulties, however, in using this technique is that often information is (partially) missing on some of the relevant variables. Author Jeroen K. Vermunt presents a general approach to these missing data problems in event history analysis that is based on the similarities between log-linear, hazard, and event history models. The book begins with a discussion of log-linear, log-rate, and modified path models and methods for obtaining maximum likelihood estimates of the parameters of these models. Vermunt then shows how to incorporate variables with missing information in log-linear models for non-response. In addition, he covers such topics as the main types of hazard models; censoring; the use of time-varying covariates; models for competing risks; multivariate hazard models; and a general approach for dealing with missing data problems, including unobserved heterogeneity, measurement error in the dependent variable, measurement error in the covariate, partially missing information on the dependent variable, and partially observed covariate values.
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.
This book is an accessible, practical and comprehensive guide for researchers from multiple disciplines including biomedical, epidemiology, engineering and the social sciences. Written for accessibility, this book will appeal to students and researchers who want to understand the basics of survival and event history analysis and apply these methods without getting entangled in mathematical and theoretical technicalities. Inside, readers are offered a blueprint for their entire research project from data preparation to model selection and diagnostics. Engaging, easy to read, functional and packed with enlightening examples, ‘hands-on’ exercises, conversations with key scholars and resources for both students and instructors, this text allows researchers to quickly master advanced statistical techniques. It is written from the perspective of the ‘user’, making it suitable as both a self-learning tool and graduate-level textbook. Also included are up-to-date innovations in the field, including advancements in the assessment of model fit, unobserved heterogeneity, recurrent events and multilevel event history models. Practical instructions are also included for using the statistical programs of R, STATA and SPSS, enabling readers to replicate the examples described in the text.
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.
Social scientists are interested in events and their causes. Although event histories are ideal for studying the causes of events, they typically possess two features—censoring and time-varying explanatory variables—that create major problems for standard statistical procedures. Several innovative approaches have been developed to accommodate these two peculiarities of event history data. This volume surveys these methods, concentrating on the approaches that are most useful to the social sciences. In particular, Paul D. Allison focuses on regression methods in which the occurrence of events is dependent on one or more explanatory variables. He gives attention to the statistical models that form the basis of event history analysis, and also to practical concerns such as data management, cost, and useful computer software. The Second Edition is part of SAGE’s Quantitative Applications in the Social Sciences (QASS) series, which continues to serve countless students, instructors, and researchers in learning the most cutting-edge quantitative techniques.
"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.
First published in 1993, Analyzing Tabular Data is an accessible text introducing a powerful range of analytical methods. Empirical social research almost invariably requires the presentation and analysis of tables, and this book is for those who have little prior knowledge of quantitative analysis or statistics, but who have a practical need to extract the most from their data. The book begins with an introduction to the process of data analysis and the basic structure of cross-tabulations. At the core of the methods described in the text is the loglinear model. This and the logistic model, are explained and their application to causal modelling, to event history analysis, and to social mobility research are described in detail. Each chapter concludes with sample programs to show how analysis on typical datasets can be carried out using either the popular computer packages, SPSS, or the statistical programme, GLIM. The book is packed with examples which apply the methods to social science research. Sociologists, geographers, psychologists, economists, market researchers and those involved in survey research in the fields of planning, evaluation and policy will find the book to be a clear and thorough exposition of methods for the analysis of tabular data.
This study illustrates the use of new techniques for the analysis of individual life histories that contain dates of relevant demographic events such as births, marriages, marital dissolutions, and migrations. The book focuses on major methodological approaches and their differences.