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| Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence | 
 enlarge | Authors: Judith D. Singer, John B. Willett
Publisher: Oxford University Press, USA
Discount Category: Book
Selling Price: $72.95
Buy New: $58.36
Potential Savings: $14.59 (20%)
New (28) Used (10) from $54.99
Customer Ratings:  11 comments
Media: Hardcover
Edition: 1
Number Of Items: 1
Pages: 672
Shipping Weight (pounds): 1.7
Dimensions (inch): 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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| Customer Comments:
| Showing comments 1-5 of 11 | | NEXT » |
It is THAT good December 16, 2008
1 out of 1 found this comment useful.
Others have have already said that this book is superb and I completely agree. If you have had a class that covers applied statistics (basic correlation and regression) you should be able to pick this up and read it with no trouble. There is math here but is is *well* explained and the algebra is always presented with a worked example.
The code at UCLA (sorry they will not let me post the link here) makes the incredibly good writing even more valuable because, not only will you understand the concepts behind Mixed Effects/Hierarchical Linear Models, you will be able to implement the ideas. If you already have some experience with Mixed Effects/HLM browse the code and you will quickly see this book covers a wide scope. I have worked with the SAS code a lot and even though the book is a bit old (by a programmer's standards) the code still works just fine.
While the book is written to be clear for non-mathemeticians, there are many "intermediate to advanced" statistical topics covered here. These are importantly areas which are typically unintelligible to non-statisticians or are glossed over or ignored by other authors. Here are some noteworthy examples. This book could/should be used as a text on data exploration and visualization. There are many case-studies throughout the book and they all begin with great visualizations (with the all important code supplements showing the novice how to make the plots in the book). Topics like fitting lines, splines, curves are covered clearly and are shown beautifully. The discussion on choosing between sets of models using deviance (-2log likelihood) and AIC has the best coverage of any book. The general discussion of likelihood estimation (maximum likelihood and restricted maximum likelihood) is superb. The coverage of data transformation for model fitting is explained well and is presented with wonderful plots. These "bonus" topics are interwoven into the great explanations of longitudinal data analyzes.
There is so much to like in this book and nothing to criticize (except perhaps the price). It makes the rest of the books in the field look bad.
A Wonderful Work July 15, 2007
2 out of 2 found this comment useful.
I find Professor Singer's Book to be a most informative and useful tool for anyone who wishes to better understand Multilevel Modeling.
Applied Longitudinal Data Analysis by Singer,et al March 13, 2007
3 out of 3 found this comment useful.
Clearly written text... and usefull for researchers.
I would recommend it to anyone starting to learn about the subject!
The Clearest and Most Useful Book on HLM for Longitudinal Studies July 27, 2006
12 out of 12 found this comment useful.
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.
Breaking down complex analyses March 18, 2006
2 out of 3 found this comment useful.
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!
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