| |
Multilevel and Longitudinal Modeling Using Stata, Second Edition - by Sophia Rabe-Hesketh and Anders Skrondal 
Multilevel and Longitudinal Modeling Using Stata, Second Edition
by Sophia Rabe-Hesketh and Anders Skrondal, looks specifically at
Stata’s treatment of generalized linear mixed models, also known as
multilevel or hierarchical models. These models are “mixed” because
they allow fixed and random effects, and they are “generalized” because
they are appropriate for continuous Gaussian responses as well as
binary, count, and other types of limited dependent variables.
The second edition has much to offer for readers of
the first edition, reading more like a sequel than an update. The text
has almost doubled in length from the original, coming in at 562 pages.
This second edition incorporates three new chapters: a chapter on
standard linear regression, a chapter on discrete-time survival
analysis, and a chapter on longitudinal and panel data containing an
expanded discussion of random-coefficient and growth-curve models. The
authors have updated this edition for Stata 10, expanding on
discussions in the original edition and adding new in-text examples and
end-of-chapter exercises. In particular, the authors have thoroughly
covered the new Stata commands xtmelogit and xtmepoisson.
The first chapter provides a review of the methods
of linear regression. Rabe-Hesketh and Skrondal then begin with the
comparatively simple random-intercept linear model without covariates,
developing the mixed model from principles and thereby familiarizing
the reader with terminology, summarizing and relating the widely used
estimating strategies, and providing historical perspective. .
Once the authors have established the mixed-model
foundation, they smoothly generalize to random-intercept models with
covariates and then to a discussion of the various estimators (between,
within, and random-effects). The authors then discuss models with
random coefficients, followed by models for growth curves. The middle
chapters of the book apply the concepts for Gaussian models to models
for binary responses (e.g., logit and probit), ordinal responses (e.g.,
ordered logit and ordered probit), and count responses (e.g., Poisson).
The text continues with a discussion of how to use
multilevel methods in discrete-time survival analysis, for example,
using complimentary log-log regression to fit the proportional hazards
model. The authors then consider models with multiple levels of random
variation and models with crossed (nonnested) random effects. In its
examples and end-of-chapter exercises, the book contains real datasets
and data from the medical, social, and behavioral sciences literature.
The book has several applications of generalized
mixed models performed in Stata. Rabe-Hesketh and Skrondal developed
gllamm, a Stata program that can fit many latent-variable models, of
which the generalized linear mixed model is a special case. As of
version 10, Stata contains the xtmixed, xtmelogit, and xtmepoisson
commands for fitting multilevel models, in addition to other xt
commands for fitting standard random-intercept models. The type of
models fit by these commands sometimes overlap; when this happens, the
authors highlight the differences in syntax, data organization, and
output for the two (or more) commands that can be used to fit the same
model. The authors also point out the relative strengths and weaknesses
of each command when used to fit the same model, based on
considerations such as computational speed, accuracy, available
predictions, and available postestimation statistics.
In reference to the first edition, a reviewer for
American Statistician commends Rabe-Hesketh and Skrondal for promoting
the appropriate use of multilevel and longitudinal modeling. The
reviewer writes in the August 2006 issue, “All too often computer
manuals leave off ... important aspects of an analysis, but the authors
have been careful to provide a well-rounded and complete approach to
model fitting and interpretation.”
In summary, this book is the most complete,
up-to-date depiction of Stata's capacity for fitting generalized linear
mixed models. The authors provide an ideal introduction for Stata users
wishing to learn about this powerful data-analysis tool.
Table of contents
|
|
