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Data Analysis Using Stata

Authors: Ulrich Kohler and Frauke Kreuter
Publisher: Stata Press
Copyright: 2005
ISBN-13: 978-1-59718-007-8
Pages: 378; paperback
Third edition now available

Comment from the Stata technical group

Data Analysis Using Stata provides a comprehensive introduction to Stata that will be useful to those who are just learning statistics and Stata, as well as to users of other statistical packages making the switch to Stata. 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. All key concepts are illustrated with multiple examples.

The remainder of the book includes chapters on reading text files, writing programs and ado-files, and using Internet resources, such as the search command and the SSC archive.

Overall, Kohler and Kreuter's book 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. The book also makes a handy reference guide for current Stata users.


Table of contents

Preface (pdf)
0 About the book
0.1 Structure
0.2 Using this book: Materials and hints
0.3 Teaching with this manual
1 "The first time"
1.1 Starting Stata
1.2 Setting up your screen
1.3 Your first analysis
1.4 Do-files
1.5 Exiting Stata
2 Working with do-files
2.1 From interactive work to working with a do-file
2.1.1 Alternative 1
2.1.2 Alternative 2
2.2 Designing do-files
2.2.1 Comments
2.2.2 Line breaks
2.2.3 Some crucial commands
2.3 Organizing your work
2.4 Summary
3 The grammar of Stata
3.1 The elements of Stata commands
3.1.1 Stata commands
3.1.2 The variable list
List of variables: required or optional
Abbreviation rules
Special listings
3.1.3 Options
3.1.4 The in qualifier
3.1.5 The if qualifier
3.1.6 Expressions
Operators
Functions
3.1.7 Lists of numbers
3.1.8 Using filenames
3.2 Repeating similar commands
3.2.1 The by prefix
3.2.2 The foreach loop
3.2.3 The forvalues loop
3.3 Weights
4 Some general comments on the statistical commands
5 Creating and changing variables
5.1 The commands generate and replace
5.1.1 Variable names
5.1.2 Some examples
5.1.3 Changing codes with by, _n, and _N
5.1.4 Subscripts
5.2 Specialized recoding commands
5.2.1 The recode command
5.2.2 The egen command
5.3 Additional tools for recording data
5.3.1 String functions
5.3.2 Date functions
5.4 Commands for dealing with missing values
5.5 Labels
5.6 Storage types, or, the ghost in the machine
6 Creating and changing graphs
6.1 A primer on graph syntax
6.2 Graph types
6.2.1 Examples
6.2.2 Specialized graphs
6.3 Graph elements
6.3.1 Appearance of data
Choice of marker
Marker colors
Marker size
Lines
6.3.2 Graph and plot regions
Graph size
Plot region
Scaling the axes
6.3.3 Information inside the plot region
Reference lines
Labeling inside the plot region
6.3.4 Information outside the plot region
Labeling the axes
Tick lines
Axis titles
The legend
Graph titles
6.4 Multiple graphs
6.4.1 Overlaying numerous twoway graphs
6.4.2 Option by()
6.4.3 Combining graphs
6.5 Saving and printing graphs
7 Describing and comparing distributions
7.1 Categories: Few or many?
7.2 Variables with few categories
7.2.1 Tables
Frequency tables
More than one frequency table
Comparing distributions
Summary statistics
7.2.2 Graphs
Histograms
Bar charts
Dot chart
7.3 Variables with many categories
7.3.1 Frequencies of grouped data
Some remarks on grouping data
Special techniques for grouping data
7.3.2 Describing data using statistics
Important summary statistics
The summarize command
The tabstat command
Comparing distributions using statistics
7.3.3 Graphs
Box plots
Histograms
Kernel density estimation
Quantile plot
7.3.4 Summary
7.4 Summary
8 Introduction to linear regression
8.1 Simple linear regression
8.1.1 The basic principle
8.1.2 Linear regression using Stata
The table of coefficients
Standard errors
The table of ANOVA results
The model fit table
8.2 Multiple regression
8.2.1 Multiple regression using Stata
8.2.2 Additional computations
8.2.3 What does "under control" mean?
8.3 Regression diagnostics
8.3.1 Violation of E(εi) = 0
Linearity
Influential cases
Omitted variables
8.3.2 Violation of Var(εi) = σ2
8.3.3 Violation of Cov(εi, εj) = 0, i ≠ j
8.4 Model extensions
8.4.1 Categorical independent variables
8.4.2 Interaction terms
8.4.3 Regression models using transformed variables
Nonlinear relations
Eliminating heteroskedasticity
8.5 More on standard errors
8.5.1 Bootstrap techniques
8.5.2 Confidence intervals in cluster samples
8.6 Advanced techniques
8.6.1 Median regression
8.6.2 Regression models for panel data
From wide to long format
Fixed-effects models
8.6.3 Error-component models
8.7 Summary
9 Regression models for categorical dependent variables
9.1 The linear probability model
9.2 Basic concepts
9.2.1 Odds, log odds, and odds ratios
9.2.2 Excursion: The maximum likelihood principle
9.3 Logistic regression with Stata
9.3.1 The coefficients block
Sign interpretation
Interpretation with odds ratios
Probability interpretation
9.3.2 The iteration block
9.3.3 The model fit block
Classification tables
Pearson chi-squared
9.4 Logistic regression diagnostics
9.4.1 Linearity
9.4.2 Influential cases
9.5 Likelihood-ratio test
9.6 Refined models
9.7 Advanced techniques
9.7.1 Probit models
9.7.2 Multinomial logistic regression
9.7.3 Models for ordinal data
9.8 Summary
10 Reading and writing data
10.1 The goal: The data matrix
10.2 Importing machine-readable data
10.2.1 Reading system files from other packages
10.2.2 Reading ASCII text files
Reading data in spreadsheet format
Reading data in free format
Reading data in fixed format
10.3 Inputting data
10.3.1 Input data using the editor
10.3.2 The input command
10.4 Combining data
10.4.1 The GSOEP database
10.4.2 The merge command
The merge procedure
Keeping track of observations
Merging more than two files
Merging data on different levels
10.4.3 The append command
10.5 Saving and exporting data
10.6 Handling big datasets
10.6.1 Rules for handling the working memory
10.6.2 Using oversized datasets
10.7 Summary
11 Do-files for advanced users and user-written programs
11.1 Two examples of usage
11.2 Four programming tools
11.2.1 Local macros
11.2.2 Do-files
11.2.3 Programs
11.2.4 Programs in do-files and ado-files
11.3 User-written Stata commands
11.3.1 Parsing variable lists
11.3.2 Parsing options
11.3.3 Parsing if and in qualifiers
11.3.4 Generating an unknown number of variables
11.3.5 Default values
11.3.6 Extended macro functions
11.3.7 Avoiding changes in the dataset
11.3.8 Help files
11.4 Summary
12 Around Stata
12.1 Resources and information
12.2 Taking care of Stata
12.3 Additional procedures
12.3.1 SJ and STB ado-files
12.3.2 SSC ado-files
12.3.3 Other ado-files
12.4 Summary
References

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