Event History Modeling: A Guide for Social Scientists (Analytical Methods for Social Research) [Paperback]

Janet M. Box-Steffensmeier (Author), Bradford S. Jones (Author)

Book Description

Publication Date: March 29, 2004 | ISBN-10: 0521546737 | ISBN-13: 978-0521546737

Here is an accessible, up-to-date guide to event history analysis for researchers and advanced students in the social sciences. 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. Recent and 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.

Editorial Reviews

Review

"Box-Steffensmeier and Jones have written a highly accessible and incredibly thoughtful introduction to survival analysis for the social scientist. I find Event History Modeling to be well suited for adoption both as a graduate and self study test. Highly recommended." --Tze Kwang Teo, University of Illinois at Urbana-Champaign, The Political Methodologist

Book Description

Event History Modeling provides an accessible, up-to-date guide to event history analysis for researchers and advanced students in the social sciences. The authors explain the foundational principles of event-history analysis, and analyse numerous examples which they estimate and interpret using standard statistical packages, such as STATA and S-Plus. They review recent and critical innovations in diagnostics, including testing the proportional hazards assumption, identifying outliers, and assessing model fit. They also discuss common problems encountered with time-to-event data, and make recommendations regarding the implementation of duration modeling methods.

See all Editorial Reviews

Product Details

• Paperback: 234 pages
• Publisher: Cambridge University Press (March 29, 2004)
• Language: English
• ISBN-10: 0521546737
• ISBN-13: 978-0521546737
• Product Dimensions: 8.9 x 5.9 x 0.7 inches
• Shipping Weight: 10.4 ounces (View shipping rates and policies)
• Average Customer Review: 2.7 out of 5 stars  See all reviews (3 customer reviews)
• Amazon Best Sellers Rank: #602,030 in Books (See Top 100 in Books)

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Most Helpful Customer Reviews

6 of 7 people found the following review helpful:

5.0 out of 5 stars Fills a big void, August 14, 2007

By 

F. Boehmke (Iowa City, IA USA) - See all my reviews
(REAL NAME)   

This review is from: Event History Modeling: A Guide for Social Scientists (Analytical Methods for Social Research) (Paperback)

This is excerpted and slightly modified from a published review (Perspectives on Politics Volume 3, June 2005) I wrote of this book, which I like quite a bit and regularly recommend and assign to my political science graduate students.

Event History Modeling: A Guide for Social Scientists provides a broad and in-depth introduction to duration analysis for political scientists and for social scientists in general. This book will instantly become the go-to guide for most political scientists interested in event history analysis and should become a staple on syllabi for graduate courses for years to come. The authors cover a broad range of important topics, employing a combination of mathematical detail and verbal discussion; important concepts are illustrated with examples using political science data that readers can download. For a book on statistical methods, Event History Modeling is quite readable and the authors do a commendable job of presenting a great variety of issues and making clear recommendations.

2 of 3 people found the following review helpful:

1.0 out of 5 stars This book does not suffice in any way., May 24, 2009

By 

Jay Nash (San Diego, CA) - See all my reviews

This review is from: Event History Modeling: A Guide for Social Scientists (Analytical Methods for Social Research) (Kindle Edition)

I totally agree with Loch's comments on this book. I had to read this book for a political science phd class (as I am a poli sci phd student in a highly quantitative program). We were assigned this book for self-learning without any lectures. It turns out this book omits NUMEROUS mathematical inferences and it is very difficult for a self learner to follow how each equation was obtained. The descriptions of some methods are vague and many things were written in an illusive way such as comparing models, tests, etc. In addition, the organization is not clear enough. They have sub-topics within each chapter but these sub-topics are not listed in the contents page so if you want to find a certain topic concerning an issue it is very difficult to locate them or use this book as an instrument book. If you're a self-learner, this book will definitely NOT suffice. If you're preparing a lecture based on this book, your students will have a diastrous time. In sum you cannot read this book alone; you'd better check out survival analysis books published in the medical literature. Those real "scientists" do a better job explaining the bigger picture, the train of thoughts, and concrete techniques behind these models.

8 of 18 people found the following review helpful:

2.0 out of 5 stars Don't read this book., October 4, 2006

By 

Loch (Seoul, Korea) - See all my reviews

This review is from: Event History Modeling: A Guide for Social Scientists (Analytical Methods for Social Research) (Paperback)

This book can be thought of as reflecting prejudice that those non-math or non-stat people can benefit only from the books without mathematical gimmichks. Without basic mathematical expressions and statistical explanations, this book seems to fail in providing proper knowledge on the methods. Try Lawless and Lee's books, which employ some mathematics and statistics. Even though we are not math or statistics majors, we can select and pick up what we need for application.