Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence / Edition 1

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Overview


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

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Editorial Reviews

From the Publisher

"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 against overly formulaic thinking that is all too common in the every day practice of statistical analysis in social science."-Stephen W. Raudenbush, Professor of Education and Statistics, Senior Research Scientist, Survey Research Center, School of Education, University of Michigan

"This is a clearly written book on longitudinal analysis, multilevel models, and survival analysis by two outstanding classroom teachers. Building systematically from elementary ideas to advanced data analysis, it will be a great resource for students and investigators in the social and biomedical sciences."-James H. Ware, Frederick Mosteller Professor of Biostatistics, Harvard School of Public Health

"...provides readers with a solid, thorough, andaccurate understanding of concepts and procedures.[S[ubstantive researchers may have been introduced to multilevel models or methods for categorical data analysisbut they have difficulty seeing how these methods can be applied to longitudinal data. The authors make this connection, and also comprehensively introduce the methods to those completely unfamiliar with either multilevel models or survival analysis."--Journal of the American Statistical Association, March 2005, vol. 100, No. 469, 352-353

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Product Details

  • ISBN-13: 9780195152968
  • Publisher: Oxford University Press
  • Publication date: 3/27/2003
  • Edition description: New Edition
  • Edition number: 1
  • Pages: 672
  • Sales rank: 500,091
  • Product dimensions: 9.30 (w) x 6.20 (h) x 1.50 (d)

Meet the Author

Harvard University, Graduate School of Education
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Table of Contents

Part I
1. A Framework for Investigating Change over Time
2. Exploring Longitudinal Data on Change
3. Introducing the Multilevel Model for Change
4. Doing Data Analysis with the Multilevel Model for Change
5. Treating TIME More Flexibly
6. Modeling Discontinuous and Nonlinear Change
7. Examining the Multilevel Model's Error Covariance Structure
8. Modeling Change using Covariance Structure Analysis
Part II
9. A Framework for Investigating Event Occurrence
10. Describing Discrete-Time Event Occurrence Data
11. Fitting Basic Discrete-Time Hazard Models
12. Extending the Discrete-Time Hazard Model
13. Describing Continuous-Time Event Occurrence Data
14. Fitting Cox Regression Models
15. Extending the Cox Regression Model
Notes
References
Index

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