3rd Edition
Data Analysis Using Stata, Third Edition
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.
The First Time
Starting Stata
Setting up your screen
Your first analysis
Do-files
Exiting Stata
Working with Do-Files
From interactive work to working with a do-file
Designing do-files
Organizing your work
The Grammar of Stata
The elements of Stata commands
Repeating similar commands
Weights
General Comments on the Statistical Commands
Regular statistical commands
Estimation commands
Creating and Changing Variables
The commands generate and replace
Specialized recoding commands
Recoding string variables
Recoding date and time
Setting missing values
Labels
Storage types, or the ghost in the machine
Creating and Changing Graphs
A primer on graph syntax
Graph types
Graph elements
Multiple graphs
Saving and printing graphs
Describing and Comparing Distributions
Categories: Few or many?
Variables with few categories
Variables with many categories
Statistical Inference
Random samples and sampling distributions
Descriptive inference
Causal inference
Introduction to Linear Regression
Simple linear regression
Multiple regression
Regression diagnostics
Model extensions
Reporting regression results
Advanced techniques
Regression Models for Categorical Dependent Variables
The linear probability model
Basic concepts
Logistic regression with Stata
Logistic regression diagnostics
Likelihood-ratio test
Refined models
Advanced techniques
Reading and Writing Data
The goal: The data matrix
Importing machine-readable data
Inputting data
Combining data
Saving and exporting data
Handling big datasets
Do-Files for Advanced Users and User-Written Programs
Two examples of usage
Four programming tools
User-written Stata commands
Around Stata
Resources and information
Taking care of Stata
Additional procedures
References
Author Index
Subject Index
Exercises appear at the end of each chapter.
Biography
Ulrich Kohler is a sociologist at the Social Science Research Center Berlin (WZB). Dr. Kohler is an organizer of the German Stata Users Group meetings. Frauke Kreuter is an associate professor in the Joint Program in Survey Methodology (JPSM) at the University of Maryland–College Park, professor in the Statistics Department at the Ludwig-Maximilians-University of Munich, and head of the Statistical Methods group at the Institute for Employment Research (IAB) in Nuremberg, Germany. Both authors are associate editors of the Stata Journal. They coauthored a German textbook, Datenanalyse mit Stata, which was the predecessor of this book. They used Data Analysis Using Stata to teach several classes and short courses at the University of Mannheim, the University of Konstanz, the Free University of Berlin, and the University of California–Los Angeles.
