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Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence, by Judith D. Singer, John B. Willett
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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 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
- Sales Rank: #121061 in Books
- Brand: Singer, Judith D./ Willett, John B.
- Published on: 2003-03-27
- Original language: English
- Number of items: 1
- Dimensions: 6.20" h x 1.50" w x 9.30" l, 2.29 pounds
- Binding: Hardcover
- 644 pages
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
About the Author
Judith D. Singeris an Associate Professor at the Graduate School of Education, Harvard University.
John B. Willett is an Associate Professor at the Graduate School of Education, Harvard University.
Most helpful customer reviews
45 of 47 people found the following review helpful.
The Bible for longitudinal analysis
By sleepy_bn
This book is, bar none, the best book on longitudinal analysis in social sciences.
The book has three outstanding features that make it the must-have for researchers who conduct longitudinal studies. First, the book has numerous examples that use data from real studies, collected by prominent scholars in this area. With the help of the accompanying website at UCLA, you will learn how to set up data files, which is crucial in longitudinal analysis. The sample codes and data files in SAS, SPSS, Stata, MLwiN, Mplus, HLM, and Splus will allow you to replicate the analyses. The authors use every effort to explain the results in plain, understandable language. They use a lot of graphs and tables to compare different nested models and help you to choose the one that best describes your data. It feels like you have an excellent tutor by your side when you are reading this book.
Second, the coverage of this book is comprehensive. Part I covers the regular growth curve modeling and multilevel modeling, with a few chapters dealing with time-varying covariates, discontinuous and nonlinear change. Part II covers discrete-time and continuous-time survival analysis. If you are conducting a longitudinal study, chances are you will find a technique in this book that suits you just right.
Third, the book is quite deep. Although it gears toward applications of different longitudinal analyses, it is no cakewalk. You need at least some background in multiple regression and multivariate statistics. I think the treatment of mathematics (both concepts and formulas) is just right. In some sections you may need to revisit them often in order to fully understand the subject.
18 of 18 people found the following review helpful.
The Clearest and Most Useful Book on HLM for Longitudinal Studies
By M. Allen Greenbaum
This is simply the best book for those analyzing longitudinal data (data measured at more than one time point). Singer's coverage of Hierarchical Linear MOdeling (HLM) is clear, well-written (sprinkled with humor, it's like a lecture by the most popular prof. at your school), and geared towards researchers who need their programs to run, not just learn the mathematical underpinnings. Singer and Willett (the coauthor, not listed above!) set the standard for presenting math/statistics book examples.
THe authors accomplish the latter by keying her examples to data located at a UCLA website; you can run the same programs on the same datasets used in the book (wow!), and compare your output, troubleshooting any problems you may have. Singer and Willett (her coauthor, not listed here!) provide outputs and programs correspoing to several of the most popular statistical programs, including SAS and SPSS.
SInger and Willet also explain the rationale for using HLM over more traditional techniques such as regression. Simply stated, regression aggregates at a level that cause one to lose information (and hence the power to detect differences.) HLM allows one to look at overall differences due to time, but also the trajectories of individual differences who are "nested" within those time points. It's the (relatively) new thing, and is increasing used by investigators, and desired by peer reviewers.
As supplements, I suggest using the UCLA website mentioned above, subscribing to an e-mail LISTSERV for interesting (though sometimes compicated discussions of "multilevel modeling" (MULTILEVEL@JISCMAIL.AC.UK), and searching for Judith Singer's website through Google or A9 (if you use A9--"Alexa"--enough you'll get a small discount at Amazon.com). Also, compare Amazon's and Judith Singer's (through her website) current prices on this book.
15 of 16 people found the following review helpful.
Absolutely wonderful!
By Dennis Hanseman
Singer and Willett is an absolutely wonderful book on longitudinal data analysis. It is divided into two main sections -- one on longitudinal analysis per se, and another on time-to-event, or survival analysis, models. The former is especially good on the basic setup and interpretation of multi-level statistical models.
This is a book for beginners in the sense that it emphasizes data analysis, rather than theory. But every statistician, and every user of statistics, can find something of value.
When I was only halfway through reading this book, I recommended it to my friends. Several of them have purchased a copy and are glad they did.
This is probably the most well-written statistics text I have ever read.
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