In this book we want to give a comprehensive introductory account of event history modeling techniques and their usefulness for causal analysis in the social sciences. In the literature a distinction between discrete-time and continuous- time models is often made (Allison 1982, 1984; Blossfeld, Hamerle, and Mayer 1989; Hamerle and Tutz 1989; Yamaguchi 1991). This volume is intended to introduce the reader to the application of continuous-time models. It is both a student textbook and a reference book for research scientists.
There were three main goals in writing this book. The first was to demonstrate that event history models are an extremely useful approach to uncover causal relationships or to map out a system of causal relations. Event history models are linked very naturally to a causal understanding of social processes because they relate change in future outcomes to conditions in the past.
The second objective of this book was to introduce the reader to the computer program Transition Data Analysis (TDA). This program, written by Götz Rohwer, estimates the sorts of models most frequently used with longitudinal data, in particular, event history data. The guiding principle in constructing TDA was the desire to make a broad range of event history analysis techniques as simple and convenient to apply as possible. TDA is now widely used in many research and university centers that analyze longitudinal data in Europe and the United States. It can be run on DOS-based personal computers and UNIX workstations. Attached to this book is a disk with an executable version of the TDA program for DOS-based machines, a file with the data used in the examples throughout the book, and a series of files containing the TDA set ups for the examples. Thus, the reader is offered the unique opportunity to easily run and modify all the application examples on a computer. In fact, we advise the event history analysis beginner to go through the application examples of the book on his or her own computer step by step. Based on our teaching experience from many workshops and introductory classes, this seems to be the most efficient and straightforward way to get familiar with these complex analysis techniques.
We have tried to emphasize the strengths and weaknesses of event history modeling techniques in each example. In particular, we have tried to complement each practical application with a short exposition of the underlying statistical concepts. The examples start with an introduction of the substantive background for the specific model. Then we demonstrate how to organize the input data and use the commands to control the TDA operation. Finally, a substantive interpretation of the obtained results is given.
The third goal was to supplement and update the textbook Event History Analysis . . .