Event History Analysis: Statistical Theory and Application in the Social Sciences

Event History Analysis: Statistical Theory and Application in the Social Sciences

Event History Analysis: Statistical Theory and Application in the Social Sciences

Event History Analysis: Statistical Theory and Application in the Social Sciences

Synopsis

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.

Excerpt

In the social sciences, especially in economics and sociology, there is an increasing interest in the analysis of event histories. Compared to traditional panel or time-series data, event histories are often better suited to the dynamic nature of empirical phenomena. For each unit of analysis event histories provide information about the exact duration until a state transition as well as the occurrence and sequence of events. Examples of event histories include the survival rates of patients in medical studies; periods of unemployment in economic studies; the "lifetime" of political systems in the field of political science; the time span in which a technical apparatus works without defect in engineering science; required learning time in psychological research; periods of stability in migration and mobility analyses; recidivism in criminological studies; the length of time in which children remain living in their parent's household in youth and family sociological studies, and so on.

The statistical theory and practical examples of event history analysis presented in this book are thus of interest to readers in a large circle of disciplines. However, the examples presented in this book are especially designed for the needs of modern economic and social science research.

The book is written for students and scientists who want to learn how to analyze event history data. It also may be used as a handbook and reference guide for users in practical research. We have tried to present the statistical foundations of event history analysis and we have especially attempted to illustrate the entire research path required in applications of event history analysis: (1) the problems of recording event oriented data; (2) specific questions of data organization; (3) the application of statistical programs; and (4) interpretation of the obtained results.

Compared with other textbooks in this field of applied statistics, it was our special intention in writing this book to provide many examples of studies in which covariates are included in semiparametric and parametric regression models. We have also sought to complement practical examples with concise explanations of the underlying statistical theory. Parameter-free methods of analysis of event history data and the possibilities for their graphical presentation are also discussed in detail. Much space is devoted to the specific problems of multistate and multiepisode models, the introduction of time- depending covariates, and the question of unobserved population heterogeneity. Detailed examples demonstrate how to check the assumptions of the models, how to test hypotheses, and how to choose the right model.

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