Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence

Judith D. Singer, John B. Willett

  • 出版商: Oxford University
  • 出版日期: 2003-03-27
  • 售價: $5,870
  • 貴賓價: 9.5$5,577
  • 語言: 英文
  • 頁數: 644
  • 裝訂: Hardcover
  • ISBN: 0195152964
  • ISBN-13: 9780195152968
  • 相關分類: Data Science
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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 for:

  • Downloadable data sets
  • Library of computer programs in SAS, SPSS, Stata, HLM, MLwiN, and more
  • Additional material for data analysis


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