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
Alan C. Acock's A Gentle Introduction to Stata, Fifth 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 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 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.
The fifth edition of the book includes two new chapters that cover multilevel modeling and item response theory (IRT) models. The multilevel modeling chapter demonstrates how to fit linear multilevel models using the mixed command. Acock discusses models with both random intercepts and random coefficients, and he provides a variety of examples that apply these models to longitudinal data. The IRT chapter introduces the use of IRT models for evaluating a set of items designed to measure a specific trait such as an attitude, a value, or a belief. Acock shows how to use the irt suite of commands, which are new in Stata 14, to fit IRT models and to graph the results. In addition, he presents a measure of reliability that can be computed when using IRT.
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.
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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 complete guide to using Stata for Bayesian analysis. It contains just enoughtheoretical 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.
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.
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.
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.