Multilevel and Longitudinal Modeling Using Stata, Third 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 material in the third edition consists of two volumes, a result
of the substantial expansion of material from the second edition,
and has much to offer readers of the earlier editions. The text has almost
doubled in length from the second edition and almost quadrupled in length
from the original version to almost 1,000 pages across the two volumes.
for Stata 12, the book has 5 new chapters and many new exercises
The two volumes comprise 16 chapters organized into eight parts.
Volume I is devoted to continuous Gaussian linear mixed models and has nine
chapters organized into four parts. The first part reviews the methods of
linear regression. The second part provides in-depth coverage of
two-level models, the simplest extensions of a linear regression model.
Rabe-Hesketh and Skrondal 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
The third part of volume I describes models for longitudinal and panel data,
including dynamic models, marginal models (a new chapter), and growth-curve
models (a new chapter). The fourth and final part covers models with
nested and crossed random effects, including a new chapter describing
in more detail higher-level nested models for continuous outcomes.
The mixed-model foundation and the in-depth coverage of the mixed-model
principles provided in volume I for continuous outcomes make it
straightforward to transition to generalized linear mixed models for
noncontinuous outcomes, which are described in volume II.
Volume II is devoted to generalized linear mixed models for binary,
categorical, count, and survival outcomes. The second volume has seven
chapters also organized into 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 fourth and final part in volume II describes models with
nested and crossed-random effects with an emphasis on binary outcomes.
The book has extensive 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 types 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 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.
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