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Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence

Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence

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Authors: Judith D. Singer, John B. Willett
Publisher: Oxford University Press, USA
Category: Book

List Price: $69.50
Buy New: $53.70
You Save: $15.80 (23%)



New (27) Used (13) from $53.70

Avg. Customer Rating: 5.0 out of 5 stars 10 reviews
Sales Rank: 70428

Media: Hardcover
Edition: 1
Number Of Items: 1
Pages: 672
Shipping Weight (lbs): 1.7
Dimensions (in): 9.3 x 6.5 x 1.5

ISBN: 0195152964
Dewey Decimal Number: 001.42
EAN: 9780195152968
ASIN: 0195152964

Publication Date: March 27, 2003
Availability: Usually ships in 1-2 business days
Shipping: Expedited shipping available
Shipping: International shipping available
Condition: Hardcover. Brand new in excellent condition. Ready to ship. Receive within 4 days. Satisfaction guaranteed. US edition.

Editorial Reviews:

Product Description
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



Customer Reviews:   Read 5 more reviews...

5 out of 5 stars A Wonderful Work   July 15, 2007
 2 out of 2 found this review helpful

I find Professor Singer's Book to be a most informative and useful tool for anyone who wishes to better understand Multilevel Modeling.


5 out of 5 stars Applied Longitudinal Data Analysis by Singer,et al   March 13, 2007
 3 out of 3 found this review helpful

Clearly written text... and usefull for researchers.
I would recommend it to anyone starting to learn about the subject!



5 out of 5 stars The Clearest and Most Useful Book on HLM for Longitudinal Studies   July 27, 2006
 18 out of 18 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.



5 out of 5 stars Breaking down complex analyses   March 18, 2006
 2 out of 3 found this review helpful

This is an excellent book. Multilevel modeling and survival analysis are becoming increasingly important in psychological studies, but are pretty complicated procedures. Singer & Willet offer both a conceptual background and practical ways to do the analyses in a clear, understandable manner. The book is very readable and will be an important reference for future analyses!


5 out of 5 stars very clear and thorough   March 16, 2006
 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.


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