Models for Categorical Dependent Variables Using Stata, Third
Edition, by J. Scott Long and Jeremy Freese, is an
essential reference for those who use Stata to fit and interpret
regression models for categorical data. Although regression
mode ls for categorical dependent variables are common, few
texts explain how to interpret such models; this text decisively
fills the void. |
Begins shipping Sept. 8
History and Survival Analysis, Second Edition, by Paul
D. Allison, is a concise yet substantive book that discusses the
main techniques currently used for modeling survival analysis.
Mathematical formulas have been kept to a minimum throughout
the book and mostly relegated to an appendix. Instead, the book
focuses on the fundamental concepts; for example, you will find
a discussion on the different kinds of censoring and on how to
perform sensitivity analysis for the noninformative assumption.
The second edition of Propensity Score
Analysis, by Shenyang Guo and Mark W. Fraser, is an
excellent book on estimating treatment effects from
observational data. New to the second edition are sections on
multivalued treatments, generalized propensity-score estimators,
and enhanced sections on propensity-score weighting estimators.
Most of the examples in this book use Stata, and many of the
estimators discussed in this new edition are implemented in the
teffects command available in Stata 13.
in Regression: Detection and Correction, by Robert L.
Kaufman, is an ideal reference for applied researchers who want
to understand the challenges posed by heteroskedasticity and
the ways to detect and address it. The discussions highlight
the advantages and warnings of the methods presented and
complement rigorous technical discussions using Stata examples
with do-files available online.
Data, by Joseph M. Hilbe, provides an introduction to
analyzing count data. It is targeted at researchers who are new
to modeling count data or who are trying to determine the best
type of count-data model to use for a specific research problem.
The second edition of Linear Mixed Models: A
Practical Guide Using Statistical Software, provides an
excellent first course in the theory and methods of linear mixed
models. Each chapter highlights a different software package and
teaches you the basics of fitting mixed models therein. If you
wish to fit linear mixed models, whether in Stata or elsewhere,
we recommend this text.
Every installation of Stata includes all the documentation in PDF
format. Stata’s documentation consists of over 11,000 pages detailing
each feature in Stata including the methods and formulas and fully