Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence, Hardcover, 1 Edition by Singer, Judith D.

Hardcover: 1 Edition
9780195152968
0195152964

Publication Date: 2003-03-27
Publisher: Oxford University Press
Hardcover : 644 pages
Edition: 1 Edition
Author: Singer, Judith D.
ISBN-10: 0195152964
ISBN-13: 9780195152968

Product Description Change is constant in everyday life. Infants crawl and then walk, children learn to read and write, teenagers mature in myriad ways, the elderly become frail and forgetful. Beyond these natural processes and events, external forces and interventions instigate and disrupt change: test scores may rise after a coaching course, drug abusers may remain abstinent after residential treatment. By charting changes over time and investigating whether and when events occur, researchers reveal the temporal rhythms of our lives. Applied Longitudinal Data Analysis is a much-needed professional book for empirical researchers and graduate students in the behavioral, social, and biomedical sciences. It offers the first accessible in-depth presentation of two of today's most popular statistical methods: multilevel models for individual change and hazard/survival models for event occurrence (in both discrete- and continuous-time). Using clear, concise prose and real data sets from published studies, the authors take you step by step through complete analyses, from simple exploratory displays that reveal underlying patterns through sophisticated specifications of complex statistical models. Applied Longitudinal Data Analysis offers readers a private consultation session with internationally recognized experts and represents a unique contribution to the literature on quantitative empirical methods. Visit http://www.ats.ucla.edu/stat/examples/alda.htm for: � Downloadable data sets � Library of computer programs in SAS, SPSS, Stata, HLM, MLwiN, and more � Additional material for data analysis Review "The book begins with an excellent introduction to the types of questions that might be answered by a longitudinal study...After a chapter with sensible suggestions for exploratory analysis... -- Statistics in Medicine Review "It will come as no surprise to those familiar with Judith Singer and John Willett's didactic journal articles to learn that they have written a terrific textbook on longitudinal data analysis." -- Social Methods and Research "Anyone teaching courses on the analysis of repeated measures data or on the analysis of survival data in the social sciences will find this book extremely helpful. It is thorough, well written and the associated web site (www.oup-usa.org/alda) provides useful back-up material in the form of datasets used in the book..." -- Centre for Multilevel Modelling "This book...will certainly have a substantial impact on the analyses of longitudinal data carried out in many fields." -- International Epidemiological Association "Longitudinal data are often essential for understanding the dynamics of social and other systems. Recent methodological developments in multilevel and event history data modeling have made it possible to handle such data efficiently and informatively. This book provides a valuable exploration of the application of this methodology, within a likelihood framework, to real data using careful and clear descriptions of procedures. Particularly important is the attention given by the authors to the assumptions built into their statistical models. This book will provide a useful resource for the applied researcher who wishes to gain insight into the analysis of longitudinal data and to be guided through the various stages of an analysis."-Harvey Goldstein, Professor of Statistical Methods, University of London, Institute of Education "This book will be of great use to many behavioral and social researchers who use quantitative methods to analyze longitudinal data. Its defining contribution is that it teaches researchers to analyze data wisely. Through many examples, it helps people look at their data using a variety of graphical and tabular techniques. It encourages people to formulate sensible models in light of their research questions. It teaches people to view such models as tentative representations, subject to criticism and revision based on data. It wages a much-needed struggle