About the Book
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. In addition to these natural changes, targeted interventions may cause change: cholesterol levels may decline as a result of a new medication, exam grades may rise following completion of a coaching class. By measuring and charting changes like these - both naturalistic and experimentally
induced - researchers uncover the temporal nature of development. The investigation of change has fascinated empirical researchers for generations, and to do it well, they must have longitudinal
data. Applied Longitudinal Data Analysis is a much-needed professional book that will instruct readers in the many new methodologies now at their disposal to make the best use of longitudinal data, including both individual growth modelling and survival analysis. Throughout the chapters, the authors employ many cases and examples from a variety of disciplines, covering multilevel models, curvilinear and discontinuous change, in addition to discrete-time hazard
models, continuous-time event occurrence, and Cox regression models. Applied Longitudinal Data Analysis is a unique contribution to the literature on research methods and will be useful to a wide range of
behavioural and social science researchers.
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 mode for change
5: Treating TIME more flexibly
6: Modelling discontinuous and nonlinear change
7: Examining the multilevel model's error covariance structure
8: Modelling 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
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 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
"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."--The Bulletin for Mathematics Books.
"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 book...will certainly have a substantial impact on the analyses of longitudinal data carried out in many fields." --International Epidemiological Association
"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
"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
"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, and accurate understanding of concepts and procedures. Substantive researchers may have been introduced to multilevel models or methods for categorical data analysis but 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
