Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence | 
 enlarge | Authors: Judith D. Singer, John B. Willett
Publisher: Oxford University Press, USA
Category: Book
List Price: $72.95
Buy New: $54.99
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Rating:  11 reviews
Sales Rank: 16837
Media: Hardcover
Edition: 1
Pages: 672
Number Of Items: 1
Shipping Weight (lbs): 1.7
Dimensions (in): 9.3 x 6.5 x 1.5
ISBN: 0195152964
Dewey Decimal Number: 001.42
EAN: 9780195152968
Publication Date: March 27, 2003
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Product Description
Here 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. This book explains how to select an appropriate method given a research question, including how to use both individual growth modeling 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. Using Longitudinal Data is a unique contribution to the literature on research methods and will be useful to a wide range of behavioral and social science researchers.
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| Customer Reviews: Read 6 more reviews...
The Bible for longitudinal analysis August 26, 2004
sleepy_bn
20 out of 22 found this review helpful
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.
The Clearest and Most Useful Book on HLM for Longitudinal Studies July 27, 2006
M. Allen Greenbaum (California)
12 out of 12 found this review helpful
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.
Absolutely wonderful! October 4, 2004
Dennis Hanseman (Cincinnati, OH United States)
11 out of 12 found this review helpful
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.
very clear and thorough March 16, 2006
R. Rogge
6 out of 6 found this review helpful
This book does a particularly good job of explaining the substantive meaning of the equations involved in multilevel modeling analyses. It spends a lot of extra time explaining what the equations mean in real world terms using examples from actual data sets. I teach a graduate level course on HLM and I much prefer this book to the Raudenbush & Byrk book because it not only does a better job of explaining the math (for graduate students less comfortable with statistics) but the chapters are also sprinkled with incredibly useful advice on actually running the analyses (getting them to converge, interpreting them, etc.) The Raudenbush & Bryk book probably does a slightly better job of presenting the equations, but it falls short on explanation and practical advice. If you were only going to buy one HLM book, I would start with this one.
Great if you really want to do longitudinal data analysis July 25, 2003
7 out of 8 found this review helpful
One of the great features of this book is that it is addressed to the empirical researcher and it really tells you how to conduct good data-analysis with longitudinal data. It doesn't push one particular piece of software, either, but uses a variety of different software packages. The book is really easy to read, and clearly explained -- and, there's so much in it!
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