Data Analysis Using Stata, Third Edition is a comprehensive introduction to both statistical methods and Stata. Beginners will learn the logic of data analysis and interpretation and easily become self-sufficient data analysts. Readers already familiar with Stata will find it an enjoyable resource for picking up new tips and tricks. The book is written as a self-study tutorial and organized around examples. It interactively introduces statistical techniques such as data exploration, description, and regression techniques for continuous and binary dependent variables. Step by step, readers move through the entire process of data analysis and in doing so learn the principles of Stata, data manipulation, graphical representation, and programs to automate repetitive tasks. This third edition includes advanced topics, such as factor-variables notation, average marginal effects, standard errors in complex survey, and multiple imputation in a way, that beginners of both data analysis and Stata can understand. Using data from a longitudinal study of private households, the authors provide examples from the social sciences that are relatable to researchers from all disciplines. The examples emphasize good statistical practice and reproducible research. Readers are encouraged to download the companion package of datasets to replicate the examples as they work through the book. Each chapter ends with exercises to consolidate acquired skills.
An Introduction to Survival Analysis Using Stata, Third Edition provides the foundation to understand various approaches for analyzing time-to-event data. It is not only a tutorial for learning survival analysis but also a valuable reference for using Stata to analyze survival data. Although the book assumes knowledge of statistical principles, simple probability, and basic Stata, it takes a practical, rather than mathematical, approach to the subject. This updated third edition highlights new features of Stata 11, including competing-risks analysis and the treatment of missing values via multiple imputation. Other additions include new diagnostic measures after Cox regression, Stata’s new treatment of categorical variables and interactions, and a new syntax for obtaining prediction and diagnostics after Cox regression. After reading this book, you will understand the formulas and gain intuition about how various survival analysis estimators work and what information they exploit. You will also acquire deeper, more comprehensive knowledge of the syntax, features, and underpinnings of Stata’s survival analysis routines.
New users can get a head start and experienced users a quick reference in this edition which describes the features of version 8 of Stata in a concise, convenient format. Each chapter also identifies the appropriate analysis for a particular set of data and includes a brief account of statistical background.
The powerful statistical software Stata has streamlined data analysis, interpretation, and presentation for researchers and statisticians around the world. But because of its power and plethora of features, particularly in version 8, Stata manuals are usually quite extensive and detailed. The third edition of the Handbook of Statistical Analyses Using Stata describes the features of Stata version 8 in the same concise, convenient format that made the previous editions so popular. But the revisions updating the handbook to version 8 are not all this edition has to offer: the authors also added important material in three all-new chapters and focused more attention on Stata's improved graphical features. More Highlights of the Third Edition Ö Updates in all chapters that reflect the features of Stata 8 Ö A new chapter on random effects models Ö A new chapter on generalized estimating equations Ö A new chapter on cluster analysis Ö Increased emphasis on diagnostics Each chapter deals with a particular data set, identifies the appropriate analysis for it, and while it includes a brief account of the statistical background of the technique applied, the primary focus remains firmly on using Stata 8 and interpreting its results. Ideal for researchers, statisticians, and students alike, this handbook forms a perfect complement to the Stata manuals, by giving new users a head start on using the program and providing experienced users with a handy quick reference.
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. 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. This book provides statistical theory, step-by-step procedures for analyzing survival data, an in-depth 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. The first three chapters of the text cover basic theoretical concepts: hazard functions, cumulative hazard functions, and their interpretations; survivor functions; hazard models; and a comparison of nonparametric, semiparametric, and parametric methodologies. Chapter 4 deals with censoring and truncation. The next three chapters cover the formatting, manipulation, stsetting, and error checking involved in preparing survival data for analysis using Stata's st analysis commands. Chapter 8 covers nonparametric methods, including the Kaplan Meier and Nelson Aalen estimators and the various nonparametric tests for the equality of survival experience. Chapters 9 11 discuss Cox regression and include various examples of fitting a Cox model, obtaining predictions, interpreting results, building models, model diagnostics, and regression with survey data. The next four chapters cover parametric models, which are fit using Stata's streg command. These chapters include detailed derivations of all six parametric models currently supported in Stata and methods for determining which model is appropriate, as well as information on stratification, obtaining predictions, and advanced topics such as frailty models. Chapter 16 is devoted to power and sample-size calculations for survival studies. The final chapter covers survival analysis in the presence of competing risks."
This book analyses Switzerland’s European policies using the concept of differentiated European integration, providing a new and original perspective on the country. This analytical approach focuses on the similarities between Switzerland's EU policies and the integration of EU member states. The latter have often been the focus of research as Switzerland is the last Western European country not to have become a member of the European Union (EU) or the European Economic Area (EEA). The book claims that Switzerland’s position on the European integration map is different in terms of degree from many EU member states, but not different in kind. The cornerstone of the book is new empirical data quantitatively measuring Switzerland’s differentiated integration during the period 1990 – 2010. The data rely on the sectoral agreements Switzerland concluded with the EU and the voluntary incorporation of EU law into domestic legislation. The book shows, among other findings, that over time Swiss European policies have begun to resemble integration policies and that the more they did so, the more dynamically they evolved.
An Introduction to Survival Analysis Using Stata, Third Edition provides the foundation to understand various approaches for analyzing time-to-event data. It is not only a tutorial for learning survival analysis but also a valuable reference for using Stata to analyze survival data. Although the book assumes knowledge of statistical principles, simple probability, and basic Stata, it takes a practical, rather than mathematical, approach to the subject. This updated third edition highlights new features of Stata 11, including competing-risks analysis and the treatment of missing values via multiple imputation. Other additions include new diagnostic measures after Cox regression, Stata's new treatment of categorical variables and interactions, and a new syntax for obtaining prediction and diagnostics after Cox regression. After reading this book, you will understand the formulas and gain intuition about how various survival analysis estimators work and what information they exploit. You will also acquire deeper, more comprehensive knowledge of the syntax, features, and underpinnings of Stata's survival analysis routines.
Maximum Likelihood Estimation with Stata, Fourth Edition is written for researchers in all disciplines who need to compute maximum likelihood estimators that are not available as prepackaged routines. Readers are presumed to be familiar with Stata, but no special programming skills are assumed except in the last few chapters, which detail how to add a new estimation command to Stata. The book begins with an introduction to the theory of maximum likelihood estimation with particular attention on the practical implications for applied work. Individual chapters then describe in detail each of the four types of likelihood evaluator programs and provide numerous examples, such as logit and probit regression, Weibull regression, random-effects linear regression, and the Cox proportional hazards model. Later chapters and appendixes provide additional details about the ml command, provide checklists to follow when writing evaluators, and show how to write your own estimation commands.
This book examines 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. Volume I covers continuous Gaussian linear mixed models and has nine chapters. The chapters are organized in four parts. Volume II discusses generalized linear mixed models for binary, categorical, count, and survival outcomes.
With each new release of Stata, a comprehensive resource is needed to highlight the improvements as well as discuss the fundamentals of the software. Fulfilling this need, A Handbook of Statistical Analyses Using Stata, Fourth Edition has been fully updated to provide an introduction to Stata version 9. This edition covers many new features of Stata, including a new command for mixed models and a new matrix language. Each chapter describes the analysis appropriate for a particular application, focusing on the medical, social, and behavioral fields. The authors begin each chapter with descriptions of the data and the statistical techniques to be used. The methods covered include descriptives, simple tests, variance analysis, multiple linear regression, logistic regression, generalized linear models, survival analysis, random effects models, and cluster analysis. The core of the book centers on how to use Stata to perform analyses and how to interpret the results. The chapters conclude with several exercises based on data sets from different disciplines. A concise guide to the latest version of Stata, A Handbook of Statistical Analyses Using Stata, Fourth Edition illustrates the benefits of using Stata to perform various statistical analyses for both data analysis courses and self-study.