Release Date: 01 June, 2003
Hardcover
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Book 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.
Book Info
Text offers an 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). A companion website presents data sets and computer code in a wide variety of software packages. DLC: Longitudinal methods. Rating 5.0
Absolutely wonderful!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.The Bible for longitudinal analysisThis 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.
Great if you really want to do longitudinal data analysisOne 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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