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Multilevel and Longitudinal Modeling Using Stata, Volume II: Categorical Responses, Counts, and Survival, Third Edition., by Sophia Rabe-H
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Volume II is devoted to generalized linear mixed models for binary, categorical, count, and survival outcomes. The second volume has seven chapters also organized in four parts. The first three parts in volume II cover models for categorical responses, including binary, ordinal, and nominal (a new chapter); models for count data; and models for survival data, including discrete-time and continuous-time (a new chapter) survival responses. The final part in volume II describes models with nested and crossed-random effects with an emphasis on binary outcomes.
- Sales Rank: #986009 in Books
- Brand: Brand: Stata Press
- Published on: 2012-04-02
- Original language: English
- Number of items: 1
- Dimensions: 9.50" h x 2.50" w x 7.30" l, 4.55 pounds
- Binding: Paperback
- 484 pages
- Used Book in Good Condition
Most helpful customer reviews
19 of 19 people found the following review helpful.
Top text on multilevel models available
By Joseph Hilbe
The first edition of Rabe-Hesketh and Skrondal's "Multilevel and Longitudinal Modeling
Using Stata" was published in 2005. The second edition was released in 2008, and now this
third edition in 2012. With each edition the scope of the model's discussed in the text
has increased. This release is in fact a 2-volume work, with the first volume devoted to
panel models having a continuous response (or dependent variable), and the second to
discrete response panel models.
Nearly every panel model in the literature is addressed in the text. Discussion begins
with a review of basic linear regression and provides the basics upon which more complex
models will be developed. Then variance-components are addressed, explaining concepts such
as between-subject heterogeneity and within-subject dependence. Following this the authors
introduce fixed and random effects models, and then delve into the details of both random
intercept and random coefficient models. Mixed effects models is given a thorough examination.
Following a discussion of subject-specific models, the authors turn to population-averaged
or marginal models, as well as growth curve models. The first volume concludes with chapters
devoted to higher-level models with nested random effects and crossed random effects mdoels.
Throughout the volume each model, where appropriate, is approached as one, two, and higher
level models.
Volume two addresses one, two, and higher level categorical response models, count models,
and survival or duration models. The emphasis, of course, is on understanding data that
is structured as panels - whether clustered or longitudinal. Zero-inflated count models,
for example, are not discussed, nor are generalized binomial, generalized Poisson, or
generalized negative binomial models. But near every type of categorical response panel model
is discussed -- in full.
Volume one is over 500 pages in length; volume two is a bit shorter. Together the two volumes
consist of 974 pages plus nearly 40 Roman numeral pages. Stata statistical software is used
throughout the text, which is dually published by Stata Press and Chapman & Hall/CRC. Stata
and Limdep econometric software are in my opinion the two most compehensive panel-modeling
statistical packages available, with SAS the next best in this regard. Stata, as a general
purpose stat package has a much wider range of capabilities, as does SAS. It is therefore a
very good choice of software to use for examining this class of models. It is also a comparatively
easy programming language. The authors have written 'gllamm', a Stata command that allows estimation
of many of the more complex models disussed in the text, including, for example, a
three-level random coefficient logistic regression model. Most examples though rely on Stata's
built-in commands, plus it's Mata matrix programming facility.
This two-volume work is in my opinion the foremost text on multilevel models.
It uses Stata for examples, but any text that uses examples to explain difficult statistical
concepts and methods needs to use some type of statistical software. Stata is ideal for this
type of modeling, so has been used in this text. Researchers who use other software for
modeling; eg SAS, R, SPSS, etc, can use the methods taught in this volume with their preferred
package, insofar as it has the capability to estimate a particular type of model.
I highly recommend this two-volume set of books to anyone with an interest in modeling
multilevel and longitudial models, regardless of their preferred statistical software. It is
the most comprehensive work available on applied multilevel modeling. It is also very well
written, with each model examined in a very clear manner. Data sets and author-written code is
provided on the book's web site. Readers therefore are able to replicate the exmaples in the book,
or to adapt them for their own projects.
7 of 7 people found the following review helpful.
The best single source
By N. Mozahem
Usually, to master a modeling technique, the student has to consult more than one source. However, if you only had to use one book to do multilevel analysis then this is the book you should go for. The authors start from the simple model which assumed independence between the different observations and slowly build more complicated models while explaining both the strengths and weaknesses of the added complexity. This book is perfect or Stata users because along with the theory the authors also explain the Stata output. I find it frustrating that there are several commands that you can use for this type of modeling, each with its advantages, but the authors do a very good job of explaining the differences so the user doesn't get mixed up. I have used this book to study multilevel models for my PhD thesis, but it was not the only book that I used. If I were to be very picky and target one item which was better presented in another book, it would definitely be the the building up of models, taking special care about the time variable, in longitudinal datasets. I used Applied Longitudinal Data Analysis by Singer and Willett for this particular issue. Unlike the present book, the book by Singer and Willett is mainly concerned with longitudinal datasets and hence they devote a lot of time to explaining how time should be treated. However, the book by Rabe-Hesketh and Skrondal is much more comprehensive and has the added value of including detailed commands and output from Stata. This sort of book makes me glad that I decided to learn Stata and not another statistical package.
2 of 3 people found the following review helpful.
Not Just a Cookbook
By Kevin S. Gray
This is a very impressive and useful book. It strikes an excellent balance between theory and application and is very well-written. Even non-users of Stata will find it an excellent, detailed overview of a set of important and complex topics.
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