Tenko Raykov and George A. Marcoulides
A Course in Item Response Theory
and Modeling with Stata, by Tenko Raykov and George A. Marcoulides, is a
comprehensive introduction to the concepts of item response theory (IRT) that
includes numerous examples using Stata's powerful suite of IRT commands. The
authors' unique development of IRT builds on the foundations of classical test
theory, nonlinear factor analysis, and generalized linear models. The examples
demonstrate how to fit many kinds of IRT models, including one-, two-, and
three-parameter logistic models for binary items as well as nominal, ordinal,
and hybrid models for polytomous items.
Chapters 1 and 2 define item response theory and
review the statistical concepts and functions that are used to build item
Chapters 3 and 4 discuss classical test theory,
factor analysis, and generalized linear models, which provide the conceptual
foundations of item response theory.
Chapters 5 and 6 introduce the fundamentals of item
response theory and provide examples to illustrate the concepts.
Chapters 7 and 8 cover model fitting, estimation using
maximum likelihood theory, item characteristic curves, and information
Chapters 9 and 10 provide a detailed introduction
to instrument construction and differential item functioning.
Chapters 11 and 12 introduce IRT models for nominal
and ordinal responses as well as multidimentional IRT models.
A Course in Item Response Theory and Modeling with
Stata is an outstanding text both for those who are new to IRT and for
those who are familiar with IRT but new to fitting these models in Stata. It is
a useful text for IRT courses and for researchers who use IRT.
Alan C. Acock's A Gentle
Introduction to Stata, Sixth Edition is aimed at new Stata users who
want to become proficient in Stata. After reading this introductory text, new
users will be able not only to use Stata well but also to learn new aspects of
assumes that the user is not familiar with any statistical software. This
assumption of a blank slate is central to the structure and contents of the
book. Acock starts with the basics; for example, the part of the book that
deals with data management begins with a careful and detailed example of
turning survey data on paper into a Stata-ready dataset on the computer. When
explaining how to go about basic exploratory statistical procedures, Acock
includes notes that will help the reader develop good work habits. This mixture
of explaining good Stata habits and good statistical habits continues
throughout the book.
is quite careful to teach the reader all aspects of using Stata. He covers data
management, good work habits (including the use of basic do-files), basic
exploratory statistics (including graphical displays), and analyses using the
standard array of basic statistical tools (correlation, linear and logistic
regression, and parametric and nonparametric tests of location and dispersion).
He also successfully introduces some more advanced topics such as multiple
imputation and multilevel modeling in a very approachable manner. Acock teaches
Stata commands by using the menus and dialog boxes while still stressing the
value of do-files. In this way, he ensures that all types of users can build
good work habits. Each chapter has exercises that the motivated reader can use
to reinforce the material.
tone of the book is friendly and conversational without ever being glib or
condescending. Important asides and notes about terminology are set off in
boxes, which makes the text easy to read without any convoluted twists or
forward referencing. Rather than splitting topics by their Stata
implementation, Acock arranges the topics as they would appear in a basic
statistics textbook; graphics and postestimation are woven into the material in
a natural fashion. Real datasets, such as the General
Social Surveys from 2002, 2006, and 2016, are used throughout the
focus of the book is especially helpful for those in the behavioral and social
sciences because the presentation of basic statistical modeling is supplemented
with discussions of effect sizes and standardized coefficients. Various
selection criteria, such as semipartial correlations, are discussed for model
selection. Acock also covers a variety of commands available for evaluating
reliability and validity of measurements.
sixth edition incorporates new features of Stata 15. All menus, dialog boxes,
and instructions for using the point-and-click interface have been updated.
Power-and-sample-size calculations for linear regression are demonstrated using
Stata 15's new power rsquared command.
This edition also includes new sections that describe how to evaluate
convergent and discriminant validity, how to compute effect sizes for t tests and ANOVA models, how to use margins and marginsplot to
interpret results of linear and logistic regression models, and how to use
full-information maximum-likelihood (FIML) estimation with SEM to address
problems with missing data.
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Michael N. Mitchell
The third edition of A Visual Guide to Stata Graphics is a complete guide to Stata’s graph command and the associated Graph Editor. Whether you want to tame the Stata graph command, quickly find out how to produce a graphical effect, master the Stata Graph Editor, or learn approaches that can be used to construct custom graphs, this is the book to read.
Christopher F. Baum
An Introduction to Modern Econometrics Using Stata, by Christopher F. Baum, successfully bridges the gap between learning econometrics and learning how to use Stata. The book presents a contemporary approach to econometrics, emphasizing the role of method-of-moments estimators, hypothesis testing, and specification analysis while providing practical examples showing how the theory is applied to real datasets by using Stata.
The gap between learning econometrics and learning how to use Stata. The book presents a contemporary approach to econometrics, emphasizing the role of method-of-moments estimators, hypothesis testing, and specification analysis while providing practical examples showing how the theory is applied to real datasets by using Stata.
Svend Juul and Morten Frydenberg
Svend Juul and Morten Frydenberg’s An Introduction to Stata for Health Researchers, Fourth Edition is distinguished in its careful attention to detail. The reader will learn not only the skills for statistical analysis but also the skills to make the analysis reproducible. The authors use a friendly, down-to-earth tone and include tips gained from a lifetime of collaboration and consulting.
The fourth edition has been substantially revised based on new features in Stata 12 and Stata 13. The updated material has been streamlined while including new features in Stata.
Christopher F. Baum's An Introduction to Stata Programming, Second Edition, is a great reference for anyone that wants to learn Stata programming. For those learning, Baum assumes familiarity with Stata and gradually introduces more advanced programming tools. For the more advanced Stata programmer, the book introduces Stata's Mata programming language and optimization routines.
This new edition of the book reflects some of the most important statistical tools added since Stata 10, when the book was introduced. Of note are factor variables and operators, the computation of marginal effects, marginal means, and predictive margins using margins, the use of gmm to implement generalized method of moments estimation, and the use of suest for seemingly unrelated estimation.
Mario Cleves, William Gould, and Yulia V. Marchenko
An Introduction to Survival Analysis Using Stata, Revised Third Edition is the ideal tutorial for professional data analysts who want to learn survival analysis for the first time or who are well versed in survival analysis but are not as dexterous in using Stata to analyze survival data. This text also serves as a valuable reference to those readers who already have experience using Stata’s survival analysis routines.
The revised third edition has been updated for Stata 14, and it includes a new section on predictive margins and marginal effects, which demonstrates how to obtain and visualize marginal predictions and marginal effects using the margins and marginsplot commands after survival regression models.
Bayesian Analysis with
Stata is a compendium of Stata community-contributed commands for
Bayesian analysis. It contains just enough theoretical and foundational
material to be useful to all levels of users interested in Bayesian statistics,
from neophytes to aficionados.
The book is careful to
introduce concepts and coding tools incrementally so that there are no steep
patches or discontinuities in the learning curve. The content helps the user
see exactly what computations are done for simple standard models and shows the
user how those computations are implemented. Understanding these concepts is
important for users because Bayesian analysis lends itself to custom or very
complex models, and users must be able to code these themselves. Bayesian Analysis with Stata is wonderful because it goes through
the computational methods three times—first using Stata's ado-code, then using
Mata, and finally using Stata to run the MCMC chains with WinBUGS or OpenBUGS.
This reinforces the material while making all three methods accessible and
clear. Once the book explains the computations and underlying methods, it
satisfies the user's yearning for more complex models by providing examples and
advice on how to implement such models. The book covers advanced topics while
showing the basics of Bayesian analysis—which is quite an achievement.
Bayesian Analysis with Stata presents all the material using real datasets rather than
simulated datasets, and there are many exercises that also use real datasets.
There is also a chapter on validating code for users who like to learn by
simulating models and recovering the known models. This provides users with the
opportunity to gain experience in assessing and running Bayesian models and
teaches users to be careful when doing so.
The book starts by discussing
the principles of Bayesian analysis and by explaining the thought process
underlying it. It then builds from the ground up, showing users how to write
evaluators for posteriors in simple models and how to speed them up using
Of course, this type of
evaluation is useful only in very simple models, so the book then addresses the
MCMC methods used throughout the Bayesian world. Once again, this starts from
the fundamentals, beginning with the Metropolis–Hastings algorithm and moving
on to Gibbs samplers. Because the latter are much quicker to use but are often
intractable, the book thoroughly explains the specialty tools of Griddy
sampling, slice sampling, and adaptive rejection sampling.
After discussing the
computational tools, the book changes its focus to the MCMC assessment
techniques needed for a proper Bayesian analysis; these include assessing
convergence and avoiding problems that can arise from slowly mixing chains.
This is where burn-in gets treated, and thinning and centering are used for
The book then returns its focus
to computation. First, it shows users how to use Mata in place of Stata's
ado-code; second, it demonstrates how to pass data and models to WinBUGS or
OpenBUGS and retrieve its output. Using Mata speeds up evaluation time.
However, using WinBUGS or OpenBUGS further speeds evaluation time, and each one
opens a toolbox, which reduces the amount of custom Stata programming needed
for complex models. This material is easy for the book to introduce and explain
because it has already laid the conceptual and computational groundwork.
The book finishes with detailed
chapters on model checking and selection, followed by a series of case studies
that introduce extra modeling techniques and give advice on specialized Stata
code. These chapters are very useful because they allow the book to be a
self-contained introduction to Bayesian analysis while providing additional
information on models that are normally beyond a basic introduction.
Ulrich Kohler and Frauke Kreuter
Data Analysis Using Stata, Third Edition, has been completely revamped to reflect the capabilities of Stata 12. This book will appeal to those just learning statistics and Stata as well as to the many users of other packages switching to Stata. Throughout the book, Kohler and Kreuter show examples using data from the German Socioeconomic Panel, a large survey of households containing demographic, income, employment, and other key information.
Data Analysis Using Stata, Third Edition has been structured so that it can be used as a self-study course or as a textbook in an introductory data analysis or statistics course. It will appeal to students and academic researchers in all the social sciences.
Michael N. Mitchell
Michael N. Mitchell’s Data Management Using Stata comprehensively covers data-management tasks, from those a beginning statistician would need to those hard-to-verbalize tasks that can confound an experienced user. Mitchell does this all in simple language with illustrative examples.
Alan C. Acock
Discovering Structural Equation Modeling Using Stata, Revised Edition, by Alan Acock, successfully introduces both the statistical principles involved in structural equation modeling (SEM) and the use of Stata to fit these models. The book uses an application-based approach to teaching SEM. Acock demonstrates how to fit a wide variety of models that fall within the SEM framework and provides datasets that enable the reader to follow along with each example. As each type of model is discussed, concepts such as identification, handling of missing data, model evaluation, and interpretation are covered in detail.
Simona Boffelli and Giovanni Urga
Financial Econometrics Using Stata by Simona Boffelli and Giovanni Urga provides an excellent introduction to time-series analysis and how to do it in Stata for financial economists. Aimed at researchers, graduate students, and industry practitioners, this book introduces readers to widely used methods, shows them how to perform these methods in Stata, and illustrates how to interpret the results.
Patrick Royston and Paul C. Lambert
Researchers wishing to fit regression models to survival data have long faced the difficult task of choosing between the Cox model and a parametric survival model such as Weibull. Cox models can be fit using Stata’s command, and parametric models are fit using which offers five parametric forms in addition to Weibull. While the Cox model makes minimal assumptions about the form of the baseline hazard function, prediction of hazards and other related functions for a given set of covariates is hindered by this lack of assumptions; the resulting estimated curves are not smooth and do not possess information about what occurs between the observed failure times. Parametric models offer nice, smooth predictions by assuming a functional form of the hazard, but often the assumed form is too structured for use with real data, especially if there exist significant changes in the shape of the hazard over time.
This book is written for Stata 12, but is fully compatible with Stata 11 as well.
James W. Hardin and Joseph M. Hilbe
Generalized linear models (GLMs) extend linear regression to
models with a non-Gaussian or even discrete response. GLM theory is predicated
on the exponential family of distributions—a class so rich that it includes the
commonly used logit, probit, and Poisson models. Although one can fit these
models in Stata by using specialized commands (for example, logit for logit models), fitting them
as GLMs with Stata’s glm command
offers some advantages. For example, model diagnostics may be calculated and
interpreted similarly regardless of the assumed distribution.
text thoroughly covers GLMs, both theoretically and computationally, with an
emphasis on Stata. The theory consists of showing how the various GLMs are
special cases of the exponential family, showing general properties of this
family of distributions, and showing the derivation of maximum likelihood (ML)
estimators and standard errors. Hardin and Hilbe show how iteratively
reweighted least squares, another method of parameter estimation, is a consequence
of ML estimation using Fisher scoring. The authors also discuss different
methods of estimating standard errors, including robust methods, robust methods
with clustering, Newey–West, outer product of the gradient, bootstrap, and
jackknife. The thorough coverage of model diagnostics includes measures of
influence such as Cook’s distance, several forms of residuals, the Akaike and
Bayesian information criteria, and various R2-type measures of explained variability.
presenting general theory, Hardin and Hilbe then break down each distribution.
Each distribution has its own chapter that explains the computational details
of applying the general theory to that particular distribution. Pseudocode
plays a valuable role here because it lets the authors describe computational
algorithms relatively simply. Devoting an entire chapter to each distribution
(or family, in GLM terms) also allows for the inclusion of real-data examples
showing how Stata fits such models, as well as the presentation of certain diagnostics
and analytical strategies that are unique to that family. The chapters on
binary data and on count (Poisson) data are excellent in this regard. Hardin
and Hilbe give ample attention to the problems of overdispersion and zero
inflation in count-data models.
final part of the text concerns extensions of GLMs. First, the authors cover
multinomial responses, both ordered and unordered. Although multinomial
responses are not strictly a part of GLM, the theory is similar in that one can
think of a multinomial response as an extension of a binary response. The
examples presented in these chapters often use the authors’ own Stata programs,
augmenting official Stata’s capabilities. Second, GLMs may be extended to
clustered data through generalized estimating equations (GEEs), and one chapter
covers GEE theory and examples. GLMs may also be extended by programming one’s
own family and link functions for use with Stata’s official glm command, and the authors detail
this process. Finally, the authors describe extensions for multivariate models
and Bayesian analysis.
fourth edition includes two new chapters. The first introduces bivariate and
multivariate models for binary and count outcomes. The second covers Bayesian
analysis and demonstrates how to use the bayes: prefix
and the bayesmh command to fit
Bayesian models for many of the GLMs that were discussed in previous chapters.
Additionally, the authors added discussions of the generalized negative
binomial models of Waring and Famoye. New explanations of working with heaped
data, clustered data, and bias-corrected GLMs are included. The new edition
also incorporates more examples of creating synthetic data for models such as
Poisson, negative binomial, hurdle, and finite mixture models.
Partha Deb, Edward C. Norton, Willard G. Manning
Health Econometrics Using Stata by Partha Deb, Edward C. Norton, and Willard G. Manning provides an excellent overview of the methods used to analyze data on healthcare expenditure and use. Aimed at researchers, graduate students, and practitioners, this book introduces readers to widely used methods, shows them how toperform these methods in Stata, and illustrates how to interpret the results. Each method is discussed in the context of an example using an extract from the Medical Expenditure Panel Survey.
After the overview chapters, the book provides excellent introductions to a series of topics aimed specifically at those analyzing healthcare expenditure and use data. The basic topics of linear regression, the generalized linear model, and log and Box-Cox models are covered with a tight focus on the problems presented by these data. Using this foundation, the authors cover the more advanced topics of models for continuous outcome with mass points, count models, and models for heterogeneous effects. Finally, they discuss endogeneity and how to address inference questions using data from complex surveys.
The authors use their formidable experience to guide readers toward useful methods and away from less recommended ones. Their discussion of "health econometric myths" and the chapter presenting a framework for approaching health econometric estimation problems are especially useful for this aspect.
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Michael Mitchell’s Interpreting and Visualizing Regression Models Using Stata is a clear treatment of how to carefully present results from model-fitting in a wide variety of settings. It is a boon to anyone who has to present the tangible meaning of a complex model in a clear fashion, regardless of the audience. As an example, many experienced researchers start to squirm when asked to give a simple explanation of the practical meaning of interactions in nonlinear models such as logistic regression. The techniques presented in Mitchell's book make answering those questions easy. The overarching theme of the book is that graphs make interpreting even the most complicated models containing interaction terms, categorical variables, and other intricacies straightforward.
This book is a worthwhile addition to the library of anyone involved in statistical consulting, teaching, or collaborative applied statistical environments. Graphs greatly aid the interpretation of regression models, and Mitchell’s book shows you how.
Introduction to Time Series Using Stata, by Sean Becketti, provides a practical guide to working with time-series data using Stata and will appeal to a broad range of users. The many examples, concise explanations that focus on intuition, and useful tips based on the author’s decades of experience using time-series methods make the book insightful not just for academic users but also for practitioners in industry and government.
The book is appropriate both for new Stata users and for experienced users who are new to time-series analysis. Introduction to Time Series Using Stata, by Sean Becketti, is a first-rate, example-based guide to time-series analysis and forecasting using Stata. It can serve as both a reference for practitioners and a supplemental textbook for students in applied statistics courses.