Programming Stata 9 (STATA RELEASE 9)

STATA

Stata Press

Multivariate Statistics Stata 9 (STATA RELEASE 9)

Stata 9 Survey Data Release 9

Stata Time Series (STATA RELEASE 9)

Data Analysis Using Stata provides a comprehensive introduction to Stata with an emphasis on data management, linear regression, logistic modeling, and using programs to automate repetitive tasks. Throughout the book, the authors make extensive use of examples using data from the German Socioeconomic Panel, a large survey of households containing demographic, income, employment, and other key information. The book begins with an introduction to the Stata interface and then proceeds with a discussion of Stata syntax and simple programming tools like foreach loops. The core of the book includes chapters on producing tables and graphs, performing linear regression, and using logistic regression. The remainder of the book includes chapters on reading text files, writing programs and ado-files, and Internet resources, such as the search command and the SSC archive. All key concepts are illustrated with multiple examples. Data Analysis Using Stata will serve as a valuable introduction to Stata, both for those who are new to statistics and statistical computing as well as for those new to Stata but familiar with other programs.

Data Analysis Using Stata

Science & Math

Kreuter, Frauke

Kohler, Ulrich

Mathematics

Presenting a thorough and accessible treatment of generalized linear mixed models, also known as multilevel or hierarchical models, Multilevel and Longitudinal Modeling Using Stata explains the models and their assumptions, applies methods to real data using Stata, and shows how to interpret the results. 
Beginning with the comparatively simple random-intercept linear model without covariates, the text develops the mixed model from first principles, familiarizing the reader with terminology, summarizing and relating the widely used estimating strategies, and providing historical perspective. Once this mixed-model foundation has been established, the text smoothly transitions to random-intercept models with covariates and then to random-coefficient models. The middle chapters apply the concepts defined earlier for Gaussian models to models for binary responses (e.g., logit and probit), ordinal responses (e.g., ordered logit and ordered probit), and count responses (e.g., Poisson). Models with multiple levels of random variation are then considered, as well as models with crossed (nonnested) random effects. 
The most complete and up-to-date depiction of Stata’s capacity for fitting generalized linear mixed models, Multilevel and Longitudinal Modeling Using Stata serves as an ideal introduction for Stata users wishing to learn about this powerful data-analysis tool.

Multilevel and Longitudinal Modeling Using Stata

Rabe-Hesketh, Sophia

Skrondal, Anders

Providing the basic collection of statistical procedures used by social scientists, A Gentle Introduction to Stata presents the fundamental tools to learn Stata. The book begins with showing how to enter and manage data as well as perform basic descriptive statistics and graphical analysis. It then examines standard statistical procedures from a t test, nonparametric tests, measures of association, multiple regression, and logical regression. The book ends with guidelines for future work and advanced topics. This learning source is an excellent introduction for those with little statistical software experience while also a useful reference for more knowledgeable statisticians by offering a detailed index of commands.

A Gentle Introduction to Stata

Acock, Alan C.

Designed to assist those working in health research, An Introduction to Stata for Health Researchers explains how to maximize the versatile Stata program for data management, statistical analysis, and graphics for research. The first nine chapters are devoted to becoming familiar with Stata and the essentials of effective data management. The text is also a valuable companion reference for more advanced users. It covers a host of useful applications for health researchers including the analysis of stratified data via epitab and regression models; linear, logistic, and Poisson regression; survival analysis including Cox regression, standardized rates, and correlation/ROC analysis of measurements.

An Introduction to Stata for Health Researchers

Juul, Svend

Frydenberg, Morten

Although regression models for categorical dependent variables are common, few texts explain how to interpret such models. Regression Models for Categorical Dependent Variables Using Stata, Second Edition, fills this void, showing how to fit and interpret regression models for categorical data with Stata. The authors also provide a suite of commands for hypothesis testing and model diagnostics to accompany the book. The book begins with an excellent introduction to Stata and then provides a general treatment of estimation, testing, fit, and interpretation in this class of models. It covers in detail binary, ordinal, nominal, and count outcomes in separate chapters. The final chapter discusses how to fit and interpret models with special characteristics, such as ordinal and nominal independent variables, interaction, and nonlinear terms. One appendix discusses the syntax of the author-written commands, and a second gives details of the datasets used by the authors in the book. Nearly 50% longer than the previous edition, the book covers new topics for fitting and interpreting models included in Stata 9, such as multinomial probit models, the stereotype logistic model, and zero-truncated count models. Many of the interpretation techniques have been updated to include interval as well as point estimates. New to the Second Edition: 
<li>Regression models, including the zero-truncated Poisson and the zero-truncated negative binomial models, the hurdle model for counts, the stereotype logistic regression model, the rank-ordered logit model, and the multinomial probit model 
</li>
<li>Stata commands, such as estat, which provides a uniform way to access statistics useful for postestimation interpretation. 
</li>
<li>Expanded suite of programs known as SPost 
</li>
<li>Inclusion of confidence intervals for predictions computed by prvalue and prgen Because all the examples, datasets, and author-written commands are available from the authors' Web site, readers can easily replicate the concrete examples using Stata, making it ideal for students or applied researchers who want to know how to fit and interpret models for categorical data.</li>

Regression Models for Categorical Dependent Variables Using Stata, Second Edition

Computers & Technology

Freese, Jeremy

Long, J. Scott

Software

Written by the creators of Stata's likelihood maximization features, Maximum Likelihood Estimation with Stata, Third Edition continues the pioneering work of the previous editions. Emphasizing practical implications for applied work, the first chapter provides an overview of maximum likelihood estimation theory and numerical optimization methods. With step-by-step instructions, the next several chapters detail the use of Stata to maximize user-written likelihood functions. Various examples include logit, probit, linear, Weibull, and random-effects linear regression as well as the Cox proportional hazards model. The final chapters describe how to add a new estimation command to Stata. Assuming a familiarity with Stata, this reference is ideal for researchers who need to maximize their own likelihood functions. New ml commands and their functions: 
<li>constraint: fits a model with linear constraints on the coefficient by defining your constraints; accepts a constraint matrix 
</li>
<li>ml model: picks up survey characteristics; accepts the subpop option for analyzing survey data 
</li>
<li>optimization algorithms: Berndt-Hall-Hall-Hausman (BHHH), Davidon-Fletcher-Powell (DFP), Broyden-Fletcher-Goldfarb-Shanno (BFGS) 
</li>
<li>ml: switches between optimization algorithms; computes variance estimates using the outer product of gradients (OPG)</li>

Maximum Likelihood Estimation with Stata, Third Edition

Gould, William

Pitblado, Jeffrey

Sribney, William

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