# 简介

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 who are switching to Stata from other packages. Throughout the book, Kohler and Kreuter show examples using data from the German Socio-Economic Panel, a large survey of households containing demographic, income, employment, and other key information. Kohler and Kreuter take a hands-on approach, first showing how to use Stata’s graphical interface and then describing Stata’s syntax. The core of the book covers all aspects of social science research, including data manipulation, production of tables and graphs, linear regression analysis, and logistic modeling. The authors describe Stata’s handling of categorical covariates and show how the new margins and marginsplot commands greatly simplify the interpretation of regression and logistic results. An entirely new chapter discusses aspects of statistical inference, including random samples, complex survey samples, nonresponse, and causal inference. The rest of the book includes chapters on reading text files into Stata, writing programs and do-files, and using Internet resources such as the search command and the SSC archive. 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.

# 目录

```1 The first time
1.1 Starting Stata

1.3.1 Inputting commands
1.3.2 Files and the working memory
1.3.4 Variables and observations
1.3.5 Looking at data
1.3.6 Interrupting a command and repeating a command
1.3.7 The variable list
1.3.8 The in qualifier
1.3.9 Summary statistics
1.3.10 The if qualifier
1.3.11 Defining missing values
1.3.12 The by prefix
1.3.13 Command options
1.3.14 Frequency tables
1.3.15 Graphs
1.3.16 Getting help
1.3.17 Recoding variables
1.3.18 Variable labels and value labels
1.3.19 Linear regression

1.4 Do-files
1.5 Exiting Stata
1.6 Exercises

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

2.2.2 Line breaks
2.2.3 Some crucial commands

2.4 Exercises

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

The types of foreach lists
Several commands within a foreach loop

3.2.3 The forvalues loop

3.3 Weights

Frequency weights
Analytic weights
Sampling weights

3.4 Exercises

4 General comments on the statistical commands
4.1 Regular statistical commands
4.2 Estimation commands
4.3 Exercises

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 Useful functions
5.1.4 Changing codes with by, n, and N
5.1.5 Subscripts

5.2 Specialized recoding commands

5.2.1 The recode command
5.2.2 The egen command

5.3 Recoding string variables
5.4 Recoding date and time

5.4.1 Dates
5.4.2 Time

5.5 Setting missing values
5.6 Labels
5.7 Storage types, or the ghost in the machine
5.8 Exercises

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 many twoway graphs
6.4.2 Option by()
6.4.3 Combining graphs

6.5 Saving and printing graphs
6.6 Exercises

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
More than one contingency table

7.2.2 Graphs

Histograms
Bar charts
Pie charts
Dot charts

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
Comparing distributions with Q–Q plots

7.4 Exercises

8 Statistical inference
8.1 Random samples and sampling distributions

8.1.1 Random numbers
8.1.2 Creating fictitious datasets
8.1.3 Drawing random samples
8.1.4 The sampling distribution

8.2 Descriptive inference

8.2.1 Standard errors for simple random samples
8.2.2 Standard errors for complex samples

Typical forms of complex samples
Sampling distributions for complex samples
Using Stata’s svy commands

8.2.3 Standard errors with nonresponse

Unit nonresponse and poststratification weights
Item nonresponse and multiple imputation

8.2.4 Uses of standard errors

Confidence intervals
Significance tests
Two-group mean comparison test

8.3 Causal inference

8.3.1 Basic concepts

Data-generating processes
Counterfactual concept of causality

8.3.2 The effect of third-class tickets
8.3.3 Some problems of causal inference

8.4 Exercises

9 Introduction to linear regression
9.1 Simple linear regression

9.1.1 The basic principle
9.1.2 Linear regression using Stata

The table of coefficients
The table of ANOVA results
The model fit table

9.2 Multiple regression

9.2.1 Multiple regression using Stata
9.2.2 More computations

Standardized regression coefficients

9.2.3 What does “under control” mean?

9.3 Regression diagnostics

9.3.1 Violation of E(εi) = 0

Linearity
Influential cases
Omitted variables
Multicollinearity

9.3.2 Violation of Var(εi) = σ2
9.3.3 Violation of Cov(εi, εj) = 0, i ≠ j

9.4 Model extensions

9.4.1 Categorical independent variables
9.4.2 Interaction terms
9.4.3 Regression models using transformed variables

Nonlinear relationships
Eliminating heteroskedasticity

9.5 Reporting regression results

9.5.1 Tables of similar regression models
9.5.2 Plots of coefficients
9.5.3 Conditional-effects plots

9.6.1 Median regression
9.6.2 Regression models for panel data

From wide to long format
Fixed-effects models

9.6.3 Error-components models

9.7 Exercises

10 Regression models for categorical dependent variables
10.1 The linear probability model
10.2 Basic concepts

10.2.1 Odds, log odds, and odds ratios
10.2.2 Excursion: The maximum likelihood principle

10.3 Logistic regression with Stata

10.3.1 The coefficient table

Interpretation with odds ratios
Probability interpretation
Average marginal effects

10.3.2 The iteration block
10.3.3 The model fit block

Classification tables
Pearson chi-squared

10.4 Logistic regression diagnostics

10.4.1 Linearity
10.4.2 Influential cases

10.5 Likelihood-ratio test
10.6 Refined models

10.6.1 Nonlinear relationships
10.6.2 Interaction effects

10.7.1 Probit models
10.7.2 Multinomial logistic regression
10.7.3 Models for ordinal data

10.8 Exercises

11.1 The goal: The data matrix

11.2.1 Reading system files from other packages

11.3 Inputting data

11.3.1 Input data using the Data Editor
11.3.2 The input command

11.4 Combining data

11.4.1 The GSOEP database
11.4.2 The merge command

Merge 1:1 matches with rectangular data
Merge 1:1 matches with nonrectangular data
Merging more than two files
Merging m:1 and 1:m matches

11.4.3 The append command

11.5 Saving and exporting data
11.6 Handling large datasets

11.6.1 Rules for handling the working memory
11.6.2 Using oversized datasets

11.7 Exercises

12 Do-files for advanced users and user-written programs
12.1 Two examples of usage
12.2 Four programming tools

12.2.1 Local macros

Calculating with local macros
Combining local macros
Changing local macros

12.2.2 Do-files
12.2.3 Programs

The problem of redefinition
The problem of naming
The problem of error checking

12.2.4 Programs in do-files and ado-files

12.3 User-written Stata commands

12.3.1 Sketch of the syntax
12.3.3 Parsing variable lists
12.3.4 Parsing options
12.3.5 Parsing if and in qualifiers
12.3.6 Generating an unknown number of variables
12.3.7 Default values
12.3.8 Extended macro functions
12.3.9 Avoiding changes in the dataset
12.3.10 Help files

12.4 Exercises

13 Around Stata
13.1 Resources and information
13.2 Taking care of Stata