Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence [Kindle Edition] Author: Judith D. Singer | Language: English | ISBN:
B005253FN2 | Format: PDF, EPUB
Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence
Posts about Download The Book Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence [Kindle Edition] for everyone book 4shared, mediafire, hotfile, and mirror link 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.
Direct download links available for Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence
- File Size: 7008 KB
- Print Length: 672 pages
- Publisher: Oxford University Press, USA; 1 edition (March 19, 2003)
- Sold by: Amazon Digital Services, Inc.
- Language: English
- ASIN: B005253FN2
- Text-to-Speech: Enabled
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- Lending: Enabled
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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.
By sleepy_bn
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.
By M. Allen Greenbaum
HALL OF FAME VINE VOICE
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