Les manuels Stata sont maintenant disponibles dans le logiciel Stata 15 au format PDF (Ne sont plus vendus séparement).
Stata Press eBook details
LIVRES & JOURNAL STATA (en anglais sauf indication contraire)

Stata par la pratique : statistiques, graphiques; et éléments de programmation (Français)
Probabilité & Statistique pour les Sciences de la Santé : apprentissage au moyen du logiciel Stata (Français)
A Gentle Introduction to Stata, Fifth Edition  ^{(disponible également en ebook)}
A Visual Guide to Stata Graphics, Third Edition^{(disponible également en ebook)}
An Introduction to Modern Econometrics Using Stata ^{(disponible également en ebook)}
An Introduction to Stata for Health Researchers, Fourth Edition ^{(disponible également en ebook)}
An Introduction to Stata Programming, Second Edition ^{(disponible également en ebook)}
An Introduction to Survival Analysis Using Stata, Revised Third Edition ^{(disponible également en ebook)}
Bayesian Analysis with Stata
Data Analysis Using Stata, Third Edition
Data Management Using Stata : A Practical Handbook ^{(disponible également en ebook)}
Discovering Structural Equation Modeling Using Stata, Revised Edition ^{(disponible également en ebook)}
Flexible Parametric Survival Analysis Using Stata : Beyond the Cox Model
^{(disponible également en ebook)}
Financial Econometrics with Stata
(disponible également en ebook)
Health Econometrics using Stata

Generalized Linear Models and Extensions, Third Edition
Interpreting and Visualizing Regression Models Using Stata ^{(disponible également en ebook)}
Introduction to Time Series Using Stata ^{}^{(disponible également en ebook)}
Maximum Likelihood Estimation with Stata, Fourth Edition ^{(disponible également en ebook)}
METAANALYSIS IN STATA: AN UPDATED COLLECTION FROM THE STATA JOURNAL, Second Edition ^{(disponible également en ebook)}
Microeconometrics Using Stata, Revised Edition ^{(disponible également en ebook)}
Multilevel and Longitudinal Modeling Using Stata ( Volume 1 and 2), Third Edition ^{(disponible également en ebook)}
One Hundred Nineteen Stata Tips, Third Edition ^{(disponible également en ebook)}
Regression Models for Categorical Dependent Variables Using Stata, Third Edition ^{(disponible également en ebook)}
Stata for the Behavioral Sciences ^{(disponible également en ebook)}
Speaking Stata Graphics
The Workflow of Data Analysis Using Stata ^{(disponible également en ebook)}
Thirty years with Stata: A Retrospective ^{(disponible également en ebook) }
The Stata Journal
The Mata Book: A Book for serious Programmers and those who want to be.
Generelized Linear Models and Extensions, Fourth Edition

LIVRES & JOURNAL STATA
A Gentle Introduction to Stata, Sixth Edition (English)
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 not only be able to use Stata well but will also learn new aspects of Stata. Acock 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 portion of the book that deals with data management begins with a careful and detailed example of turning survey data on paper into a Stataready 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.
Acock is quite careful to teach the reader all aspects of using Stata. He covers data management, good work habits (including the use of basic dofiles), 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 structural equation modeling in a very approachable manner. Acock teaches Stata commands by using the menus and dialog boxes while still stressing the value of dofiles. 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.
The 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 and 2006, are used throughout the book.
The focus of the book is especially helpful for those in psychology and the 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.
The sixth edition has been updated to include new features in Stata 15. Effectsize computation is performed using the esize and estat esize commands. Power and samplesize analysis for twosample tests of means, as well as oneway, twoway, and repeated measures ANOVA models, is demonstrated using the power suite of commands. The multiple regression chapter includes a new section on modeling quadratic relationships. The chapter on logistic regression contains new material on examining effects of predictors using margins and marginsplot. A newly added chapter is devoted to Stata’s sem and gsem commands for structural equation modeling. This chapter focuses on fitting linear and logistic regression models, thinking of these models in terms of path diagrams, and expanding the capabilities of regress and logistic using sem and gsem. After covering models with one response variable, Acock extends these concepts to performing path analysis.
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A Visual Guide to Stata Graphics, Third Edition (English)
In its third edition, Michael Mitchell’s Visual Guide to Stata Graphics remains the essential introduction and reference for Stata graphics. The third edition retains all the features that made the first two editions so useful:
 A complete guide to Stata’s graph command and Graph Editor
 Exhaustive examples of customizing graphs using both command options and the Graph Editor
 Visual indexing of features—just look for a picture that matches what you want to do
New in this edition are treatments of contour plots, margins plots, and font handling. Mitchell dedicates a new subsection to contour plots—showing you how to control the number of levels, the colors used, and how to produce effective legends. Over 30 graphs are used to demonstrate what you can accomplish with the new marginsplot command—graphs of estimated means and marginal means (with confidence intervals), interaction graphs, comparisons of groups, and more. Mitchell also adds a section showing you how to get bold text, italic text, subscripts, superscripts, and Greek letters into your titles, axis, labels, and other text.
The book retains its visual style, presenting the reader with a colorcoded, visual table of contents that runs along the right edge of every page and shows readers exactly where they are in the book. You can see the colorcoded chapter tabs without opening the book, providing quick visual access to each chapter.
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An Introduction to Modern Econometrics Using Stata (English)
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 methodofmoments estimators, hypothesis testing, and specification analysis while providing practical examples showing how the theory is applied to real datasets using Stata.
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An Introduction to Stata for Health Researchers, Fourth Edition (English)
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, downtoearth tone and include tips gained from a lifetime of collaboration and consulting.
The book is based on the assumption that the reader has some basic knowledge of statistics but no knowledge of Stata. The authors build the reader's abilities as a builder would build a house: laying a firm foundation in Stata, framing a general structure in which good work can be accomplished, adding the details that are particular to various types of statistical analyses, and, finally, trimming with a thorough treatment of graphics and special topics such as power and samplesize computations.
Juul and Frydenberg start not only by teaching the reader how to communicate with Stata through its unified syntax but also by demonstrating how Stata thinks about its basic building blocks. The authors show how Stata views data, thus allowing the reader to see the variety of possible data structures. They also show how to manipulate data to create a dataset that is well documented. When demonstrating analysis techniques, the authors show how to think of analysis in terms of estimation and postestimation. They make the book easy to use as a learning tool and easy to refer back to for useful techniques.
Once they introduce Stata to new users, Juul and Frydenberg fill in the details for performing analysis in Stata. As would be expected from a book addressing health researchers, the authors mostly demonstrate the statistical techniques that are common in biostatistics and epidemiology: case–control, matched case–control, and incidencerate data analysis; linear and generalized linear models, including logistic, Poisson, and binomial regression; survival analysis with proportional hazards; and classification using receiver operating characteristic curves. While presenting general estimation techniques, the authors also spend time with interactions and techniques for checking model assumptions.
While teaching Stata implementation, Juul and Frydenberg reinforce habits that allow reproducible research and graceful backtracking in case of errors. Early in the book, they introduce how to use dofiles for creating sequences and log files for tracking work. At the end of the book, they introduce some useful programming techniques, such as loops and branching, that simplify repetitive tasks.
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.
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An Introduction to Stata Programming, Second Edition (English)
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.
As in the previous edition of the book, Baum steps the reader through the three levels of Stata programming. He starts with dofiles. Dofiles are powerful batch files that support loops and conditional statements and are ideal to automate your workflow as well as to guarantee reproducibility of your work. While giving examples of dofile programming, Baum introduces useful programming tips and advice.
He then delves into adofiles, which are used to extend Stata by creating new commands that share the syntax and behavior of official commands. Baum gives an example of how to write a simple additional command for Stata, complete with documentation and certification. After writing the simple command, users can then learn how to write their own custom estimation commands by using both Stata's builtin numerical maximumlikelihood estimation routine, ml, its builtin nonlinear leastsquares routines, nl and nlsur, and its builtin generalized method of moments estimation routine.
Finally, he introduces Mata, Stata's matrix programming language. Mata programs are integrated into adofiles to build a custom estimation routine that is optimized for speed and numerical stability. While discussing Mata, Baum presents useful topics for advanced programming such as structures and pointers and likelihoodfunction evaluators using Mata.
Baum introduces concepts by providing the background and importance for the topic, presents common uses and examples, and then concludes with larger, more applied examples he refers to as "cookbook recipes". Many of the examples in the book are of particular interest because they arose from frequently asked questions from Stata users.
If you want to understand basic Stata programming or want to write your own routines and commands using advanced Stata tools, Baum's book is a great reference.
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An Introduction to Survival Analysis Using Stata,
Revised Third Edition (English)
An Introduction to Survival Analysis Using Stata, 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 third edition has been updated for Stata 11, and it includes a new chapter on competingrisks analysis. This chapter describes the problems posed by competing events (events that impede the failure event of interest), and covers estimation of causespecific hazards and cumulative incidence functions.
Other enhancements include the handling of missing values by multiple imputation in Cox regression, a newtoStata11 system for specifying categorical (factor) variables and their interactions, three additional diagnostic measures for Cox regression, and a more efficient syntax for obtaining predictions and diagnostics after Cox regression. Survival analysis is a field of its own that requires specialized data management and analysis procedures. To meet this requirement, Stata provides the st family of commands for organizing and summarizing survival data. The authors of this text are also the authors of Stata’s st commands.
This book provides statistical theory, stepbystep procedures for analyzing survival data, an indepth usage guide for Stata’s most widely used st commands, and a collection of tips for using Stata to analyze survival data and to present the results. This book develops from first principles the statistical concepts unique to survival data and assumes only a knowledge of basic probability and statistics and a working knowledge of Stata.
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Bayesian Analysis with Stata (English)
Bayesian Analysis with Stata is a compendium of Stata communitycontributed 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 adocode, 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.
The Stata commands in this book demonstate how to perform Bayesian analysis using Stata 13 and earlier versions. Nevertheless, Thompson's approach is invaluable because it teaches readers to think from a Bayesian perspective, regardless of the version of Stata they use.
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 algebraic simplification.
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 burnin gets treated, and thinning and centering are used for performance gains.
The book then returns its focus to computation. First, it shows users how to use Mata in place of Stata's adocode; 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 selfcontained introduction to Bayesian analysis while providing additional information on models that are normally beyond a basic introduction.
Data Analysis Using Stata, Third Edition (English)
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 who are switching to Stata from other packages. 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.
Kohler and Kreuter take a handson approach, first showing how to use Stata’s graphical interface and then describing Stata’s syntax. The core of the book covers all aspects of social science research, including data manipulation, production of tables and graphs, linear regression analysis, and logistic modeling. The authors describe Stata’s handling of categorical covariates and show how the new margins and marginsplot commands greatly simplify the interpretation of regression and logistic results. An entirely new chapter discusses aspects of statistical inference, including random samples, complex survey samples, nonresponse, and causal inference.
The rest of the book includes chapters on reading text files into Stata, writing programs and dofiles, and using Internet resources such as the search command and the SSC archive.
Data Analysis Using Stata, Third Edition has been structured so that it can be used as a selfstudy 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.
Data Management Using Stata : A Practical Handbook (English)
Michael N. Mitchell’s Data Management Using Stata comprehensively covers datamanagement tasks, from those a beginning statistician would need to those hardtoverbalize tasks that can confound an experienced user. Mitchell does this all in simple language with illustrative examples.
The book is modular in structure, with modules based on datamanagement tasks rather than on clusters of commands. This format is helpful because it allows readers to find and read just what they need to solve a problem at hand.
To complement this format, the book is in a style that will teach even sporadic readers good habits in data management, even if the reader chooses to read chapters out of order.
Throughout the book, Mitchell subtly emphasizes the absolute necessity of reproducibility and an audit trail. Instead of stressing programming esoterica, Mitchell reinforces simple habits and points out the timesavings gained by being careful. Mitchell’s experience in UCLA’s Academic Technology Services clearly drives much of his advice.
Mitchell includes advice for those who would like to learn to write their own datamanagement Stata commands. Even experienced users will learn new tricks and new ways to approach datamanagement problems.
This is a great book  thoroughly recommended for anyone interested in data management using Stata.
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Discovering Structural Equation Modeling Using Stata, Revised Edition (English)
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 applicationbased 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.
In Stata, structural equation models can be fit using the command language or the graphical user interface (GUI) for SEM, known as the SEM Builder. The book demonstrates both of these approaches. Throughout the text, the examples use the sem command. Each chapter also includes brief discussions on drawing the appropriate path diagram and performing estimation from within the SEM Builder. A more indepth coverage of the SEM Builder is given in one of the book’s appendixes.
The Revised Edition includes output, syntax, and instructions for fitting models with the SEM Builder that have been updated for Stata 13.
Discovering Structural Equation Modeling Using Stata, Revised Edition is an excellent resource both for those who are new to SEM and for those who are familiar with SEM but new to fitting these models in Stata. It is useful as a text for courses covering SEM as well as for researchers performing SEM.
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Flexible Parametric Survival Analysis Using Stata :
Beyond the Cox Model (English)
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 stcox command, and parametric models are fit using streg, 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 text is concerned with obtaining a compromise between Cox and parametric models that retains the desired features of both types of models. The book is aimed at researchers who are familiar with the basic concepts of survival analysis and with the stcox and streg commands in Stata. As such, it is an excellent complement to An Introduction to Survival Analysis Using Stata by Cleves et al. (2010).
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Financial Econometrics Using Stata (English)
Financial Econometrics Using Stata by Simona Boffelli and Giovanni Urga provides an excellent introduction to timeseries 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.
After providing an intuitive introduction to timeseries analysis and the ubiquitous autoregressive movingaverage (ARMA) model, the authors carefully cover univariate and multivariate models for volatilities. Chapters on risk management and analyzing contagion show how to define, estimate, interpret, and perform inference on essential measures of risk and contagion.
The authors illustrate every topic with easily replicable Stata examples and explain how to interpret the results from these examples.
The authors have a unique blend of academic and industry training and experience. This training produced a practical and thorough approach to each of the addressed topics.
Generalized Linear Models and Extensions, Third Edition (English)
Generalized linear models (GLMs) extend linear regression to models with a nonGaussian, 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 distributions. 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.
This 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, general properties of this family of distributions, and the derivation of maximum likelihood (ML) estimators and standard errors.
The book shows how iteratively reweighted least squares, another method of parameter estimation, is a consequence of ML estimation using Fisher scoring. Hardin and Hilbe 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 R^{2}type measures of explained variability
Interpreting and Visualizing Regression Models Using Stata (English)
Michael Mitchell’s Interpreting and Visualizing Regression Models Using Stata is a clear treatment of how to carefully present results from modelfitting 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.
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Introduction to Time Series Using Stata (English)
Introduction to Time Series Using Stata, by Sean Becketti, provides a practical guide to working with timeseries 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 timeseries 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 timeseries analysis.
Sean Becketti is a financial industry veteran with three decades of experience in academics, government, and private industry. He was a developer of Stata in its infancy, and he was Editor of the Stata Technical Bulletin, the precursor to the Stata Journal, between 1993 and 1996. He has been a regular Stata user since its inception, and he wrote many of the first timeseries commands in Stata.
Introduction to Time Series Using Stata, by Sean Becketti, is a firstrate, examplebased guide to timeseries analysis and forecasting using Stata. It can serve as both a reference for practitioners and a supplemental textbook for students in applied statistics courses.
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Maximum Likelihood Estimation with Stata, Fourth Edition (English)
Maximum Likelihood Estimation with Stata, Fourth Edition, is the essential reference and guide for researchers in all disciplines who wish to write maximum likelihood (ML) estimators in Stata. Beyond providing comprehensive coverage of Stata’s ml command for writing ML estimators, the book presents an overview of the underpinnings of maximum likelihood and how to think about ML estimation.
The book shows you how to take full advantage of the ml command’s noteworthy features :
 linear constraints
 four optimization algorithms (Newton–Raphson, DFP, BFGS, and BHHH)
 observed information matrix (OIM) variance estimator
 outer product of gradients (OPG) variance estimator
 Huber/White/sandwich robust variance estimator
 cluster–robust variance estimator
 complete and automatic support for survey data analysis
 direct support of evaluator functions written in Mata
MetaAnalysis in Stata : An Updated Collection from the Stata Journal, Second Edition (English)
Metaanalysis allows researchers to combine results of several studies into a unified analysis that provides an overall estimate of the effect of interest and to quantify the uncertainty of that estimate. Stata has some of the best statistical tools available for doing metaanalysis. The unusual thing about these tools is that none of them are part of official Stata. They are all created by and documented by experts in the broader research community who also happen to be proficient Stata developers.
Editors Tom Palmer and Jonathan Sterne show how each of the articles in this collection relates to others and how each fits in the overall literature of metaanalysis. For the first edition, Sterne convinced over half the authors to update their software and articles for the collection. In this new edition, Palmer and Sterne have substantially expanded the scope of the collection to cover in more depth many contemporary advances that will help keep the reader up to date.
The second edition retains its original topicspecific sections devoted to the fundamentals of metaanalysis: fitting models, metaregression, and graphical and analytic tools for detecting bias. It also retains a section devoted to advanced methods. Readers of the first edition will find new articles in these sections, in particular ones that take advantage of major changes that occurred in Stata since the first edition, such as the introduction of the gsem command.
This edition also adds three new topicspecific sections for multivariate or multiple outcomes metaanalysis, individual participant data (IPD) metaanalysis, and network metaanalysis. The addition of these sections gives readers access to new commands that address recent methodological developments in the field.
The new edition adds 11 articles to the original collection of 16 articles. The articles cover topics ranging from standard and cumulative metaanalysis and forest plots to contourenhanced funnel plots and nonparametric analysis of publication bias. In their articles, the authors present conceptual overviews of the techniques, thorough explanations, and detailed descriptions and syntax of new commands. They also provide examples using realworld data. In short, this collection is a complete introduction and reference for performing metaanalyses in Stata.
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Microeconometrics Using Stata, Revised Edition (English)
Microeconometrics Using Stata, Revised Edition, by A. Colin Cameron and Pravin K. Trivedi, is an outstanding introduction to microeconometrics and how to do microeconometric research using Stata. Aimed at students and researchers, this book covers topics left out of microeconometrics textbooks and omitted from basic introductions to Stata. Cameron and Trivedi provide the most complete and uptodate survey of microeconometric methods available in Stata.
The revised edition has been updated to reflect the new features available in Stata 11 that are germane to microeconomists. Instead of using mfx and the userwritten margeff commands, the revised edition uses the new margins command, emphasizing both marginal effects at the means and average marginal effects.
Factor variables, which allow you to specify indicator variables and interaction effects, replace the xi command. The new gmm command for generalized method of moments and nonlinear instrumentalvariables estimation is presented, along with several examples. Finally, the chapter on maximum likelihood estimation incorporates the enhancements made to ml in Stata 11.
Early in the book, Cameron and Trivedi introduce simulation methods and then use them to illustrate features of the estimators and tests described in the rest of the book. While simulation methods are important tools for econometricians, they are not covered in standard textbooks. By introducing simulation methods, the authors arm students and researchers with techniques they can use in future work. Cameron and Trivedi address each topic with an indepth Stata example, and they reference their 2005 textbook, Microeconometrics: Methods and Applications, where appropriate.
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Multilevel and Longitudinal Modeling Using Stata, Third Edition (Vol. 1 & 2  English)
Multilevel and Longitudinal Modeling Using Stata, Third Edition, by Sophia RabeHesketh 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. Fully updated for Stata 12, the book has 5 new chapters, and many new exercises and datasets.
The two volumes comprise 16 chapters organized into eight parts.
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One Hundred Nineteen Stata Tips, Third Edition (English)
One Hundred Nineteen Stata Tips provides concise and insightful notes about commands, features, and tricks that will help you obtain a deeper understanding of Stata. The book comprises the contributions of the Stata community that have appeared in the Stata Journal since 2003.
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Probabilité & Statistique pour les Sciences de la Santé :
apprentissage au moyen du logiciel Stata (ouvrage en Français)
Probabilité et Statistique pour les Sciences de la Santé : apprentissage au moyen du logiciel Stata, par Patrick Taffé, se veut un livre différent de nombreux ouvrages théoriques traitant des probabilités et de la statistique. Cet ouvrage (en français) non seulement présente, de façon rigoureuse, les concepts et méthodes statistiques, mais aussi utilise des exemples concrets pour illustrer chaque concept théorique nouvellement introduit. Le lecteur va apprendre à réaliser des analyses au moyen de Stata, basé sur des vraies données. De nombreuses illustrations et nombreux exemples d'applications sont donnés pour apprendre au lecteur à mettre en pratique les techniques d'analyse. Enfin, des exercices à réaliser avec Stata et impliquant le plus souvent un petit jeu de données, sont proposés à la fin de chaque section afin de mettre en oeuvre les connaissances nouvellement acquises.
L'ouvrage s'adresse en premier lieu au chercheur dans le domaine des sciences de la santé (médecin, infirmière et infirmier, épidémiologue, biologiste, biostatisticien, etc.), qu'il soit débutant ou qu'il maitrise déjà les concepts de base de la statistique, mais aussi aux chercheurs d'autres domaines (économie, psychologie, démographie, géographie, etc.) qui désirent acquérir les fondements de la statistique.
Ce livre présente de façon méticuleuse les notions fondamentales de la théorie des probabilités et de la statistique: bref rappel de l'histoire de la statistique, la statistique descriptive, les distributions discrètes et continues, estimation, tests d'hypothèses, l'analyse de corrélation, l'analyse de régression linéaire simple et multiple, et le modèle d'analyse de variance. Au moyen des exemples et exercices, le lecteur est guidé tout au long de la réalisation du problème. En même temps, l'apprentissage de l'utilisation de Stata se fait progressivement au fil des chapitres. La dernière partie de l'ouvrage propose une introduction à l'utilisation de St ata. Les corrections des exercices figurent à la fin de l'ouvrage, permettant au lecteur de vérifier le niveau de compréhension atteint après chaque étape.
Ce livre ne se limite pas à une présentation de la théorie que l'on trouve dans des ouvrages d'introduction de la statistique. En tant que biostatisticien, Patrick Taffé a plusieurs années d'expérience dans l'application de la statistique à la recherche clinique. Dans ce livre, l'auteur partage son expérience et montre comment utiliser la théorie statistique sur des vraies données, au moyen d'un logiciel statistique. Le lecteur apprendra à choisir la méthode statistique la plus simple et adéquate, et à apprécier si les hypothèses sur lesquelles reposent ces méthodes sont validées dans un contexte donné, afin de justifier leur utilisation. Ce livre propose, donc, une méthode pédagogique originale d'enseignement dont l'objectif est de faciliter le passage de la théorie à la pratique.
Regression Models for Categorical Dependent Variables Using Stata,
Third Edition (English)
Regression 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 models for categorical dependent variables are common, few texts explain how to interpret such models; this text decisively fills the void.
The third edition is divided into two parts. Part I begins with an excellent introduction to Stata and follows with general treatments of the estimation, testing, fitting, and interpretation of models for categorical dependent variables. The book is thus accessible to new users of Stata and those who are new to categorical data analysis. Part II is devoted to a comprehensive treatment of estimation and interpretation for binary, ordinal, nominal, and count outcomes.
Readers familiar with previous editions will find many changes in the third edition. An entire chapter is now devoted to interpretation of regression models using predictions. This concept is explored in greater depth in Part II. The authors also discuss how many improvements made to Stata in recent years—factor variables, marginal effects with margins, plotting predictions using marginsplot—facilitate analysis of categorical data.
The authors advocate a variety of new methods that use predictions to interpret the effect of variables in regression models. Readers will find all discussion of statistical concepts firmly grounded in concrete examples. All the examples, datasets, and authorwritten commands are available on the authors' website, so readers can easily replicate the examples with Stata.
Examples in the new edition also illustrate changes to the authors' popular SPost commands after a recent rewrite inspired by the authors' evolving views on interpretation. Readers will note that SPost now takes full advantage of the power of the margins command and the flexibility of factorvariable notation. Long and Freese also provide a suite of new commands, including mchange, mtable, and mgen. These commands complement margins, aiding model interpretation, hypothesis testing, and model diagnostics. They offer the same syntactical convenience features that users of Stata expect, for example including powers or interactions of covariates in regression models and seamlessly working with complex survey data. The authors also discuss how to use these commands to estimate marginal effects, either averaged over the sample or evaluated at fixed values of the regressors.
The third edition of Regression Models for Categorical Dependent Variables Using Stata continues to provide the same highquality, practical tutorials of previous editions. It also offers significant improvements over previous editions—new content, updated information about Stata, and updates to the authors' own commands. This book should be on the bookshelf of every applied researcher analyzing categorical data and is an invaluable learning resource for students and others who are new to categorical data analysis.
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Stata for the Behavioral Sciences
Stata for the Behavioral Sciences, by Michael Mitchell, is the ideal reference for researchers using Stata to fit ANOVA models and other models commonly applied to behavioral science data. Drawing on his education in psychology and his experience in consulting, Mitchell uses terminology and examples familiar to the reader as he demonstrates how to fit a variety of models, how to interpret results, how to understand simple and interaction effects, and how to explore results graphically.
Although this book is not designed as an introduction to Stata, it is appealing even to Stata novices. Throughout the text, Mitchell thoughtfully addresses any features of Stata that are important to understand for the analysis at hand. He also is careful to point out additional resources such as related videos from Stata's YouTube channel.
This book is an easytofollow guide to analyzing data using Stata for researchers in the behavioral sciences and a valuable addition to the bookshelf of anyone interested in applying ANOVA methods to a variety of experimental designs.
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Stata par la pratique (ouvrage en Français)
Stata par la pratique propose une parfaite introduction à l’utilisation de Stata rédigée en français. Des exemples clairs, proposés dans un langage accessible, accompagnent l’utilisateur à travers l’ensemble des fonctionnalités de Stata 10. L’ensemble des outils nécessaires au traitement des données est inclus : gestion des données, statistiques sommaires, essai et modélisation d’hypothèses, postestimation, graphiques, et préparations des données pour publication.
En outre, l’ouvrage propose une approche des fondamentaux de la programmation sous Stata et de la résolution des problèmes usuels utilisant des macros prédéfinies. L’ensemble des informations apportées par cet ouvrage permet de transformer tout nouvel utilisateur de Stata de débutant à chevronné ; cet apprentissage se fait facilement, grâce à la clarté de la présentation de l’ensemble de l’ouvrage.
L’un des points forts de cet ouvrage est de présenter non seulement l’utilisation des commandes préprogrammées incluses dans Stata mais aussi d’illustrer l’usage de la programmation développée par l’utilisateur. Les exemples du livre ont principalement trait aux sciences sociales et économiques, mais la plupart des exemples présentent également un intérêt pour tout statisticien, quelle que soit sa spécialité.
Le caractère didactique et exhaustif de cet ouvrage le rendent indispensable à tout utilisateur de Stata, du nouveau venu à l’utilisateur averti, de l’étudiant au chercheur spécialiste.
Speaking Stata Graphics (English)
Speaking Stata Graphics is ideal for researchers who want to produce effective, publicationquality graphs. A compilation of articles from the popular “Speaking Stata” column by Nicholas J. Cox, this book provides valuable insights about Stata's builtin and userwritten statisticalgraphics commands.
The Workflow of Data Analysis Using Stata (English)
The Workflow of Data Analysis Using Stata, by J. Scott Long, is an essential productivity tool for data analysts. Aimed at anyone who analyzes data, this book presents an effective strategy for designing and doing dataanalytic projects.
In this book, Long presents lessons gained from his experience with numerous academic publications, as a coauthor of the immensely popular Regression Models for Categorical Dependent Variables Using Stata, and as a coauthor of the SPOST routines, which are downloaded over 20,000 times a year.
A workflow of data analysis is a process for managing all aspects of data analysis. Planning, documenting, and organizing your work; cleaning the data; creating, renaming, and verifying variables; performing and presenting statistical analyses; producing replicable results; and archiving what you have done are all integral parts of your workflow.
Long shows how to design and implement efficient workflows for both oneperson projects and team projects. Long guides you toward streamlining your workflow, because a good workflow is essential for replicating your work, and replication is essential for good science.
An efficient workflow reduces the time you spend doing data management and lets you produce datasets that are easier to analyze. When you methodically clean your data and carefully choose names and effective labels for your variables, the time you spend doing statistical and graphical analyses will be more productive and more enjoyable.
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Thirty Years with stata: A Retrospective (English)
This volume is a sometimes serious and sometimes whimsical retrospective of Stata, its development, and its use over the last 30 years.
The view from the inside opens with an essay by Bill Gould, Stata's president and cofounder, that discusses the challenges and concepts that guided the design and implementation of Stata. This is followed by an interview of Bill by Joe Newton that discusses Bill's early interest in computing, his early work on a program for matching prom dates in the days when you had to purchase time on computers, and further exploration of the guiding principles behind Stata. Finally, Sean Becketti, Stata's first employee, delves into the nottobemissed culture of Stata in its infancy.
The view from the outside comprises 14 essays by prominent researchers and members of the Stata community. Most discuss Stata's use and evolution in disciplines such as behavioral sciences, business, economics, epidemiology, time series, political science, public health, public policy, veterinary epidemiology, and statistics. Some take a sweeping overview. Others are more intimate personal recollections.
Mostly, we simply wanted to celebrate the relationship between Stata users and Stata software. This volume holds something interesting for everyone.
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The Stata Journal (English)
The Stata Journal is a quarterly publication containing articles about statistics, data analysis, teaching methods, and effective use of Stata's language.
The Journal publishes reviewed papers together with shorter notes and comments, regular columns, book reviews, and other material of interest to Stata users.
Examples of the types of papers include :
 expository papers that link the use of Stata commands or programs to associated principles, such as those that will serve as tutorials for users first encountering a new field of statistics or a major new technique
 papers that go "beyond the Stata manual" in explaining key features or uses of Stata that are of interest to intermediate or advanced users of Stata
 papers that discuss new commands or Stata programs of interest either to a wide spectrum of users (e.g., in data management or graphics) or to some large segment of Stata users (e.g., in survey statistics, survival analysis, panel analysis, or limited dependent variable modeling)
 papers analyzing the statistical properties of new or existing estimators and tests in Stata
 papers that could be of interest or usefulness to researchers, especially in fields that are of practical importance but are not often included in texts or other journals, such as the use of Stata in managing datasets, especially large datasets, with advice from hardwon experience
 papers of interest to those who teach, including Stata with topics such as extended examples of techniques and interpretation of results, simulations of statistical concepts, and overviews of subject areas.
Health Econometrics Using Stata
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 to perform 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 BoxCox 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.