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Data Analysis Using Stata, Third Edition - By Ulrich Kohler and Frauke Kreuter

Comment from the Stata Technical group

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.

Table of Contents

List of Tables

List of Figures

Preface

  1. "The first time"
    1.1 Starting Stata
    1.2 Setting up your screen
    1.3 Your first analysis
    1.3.1 Inputting commands
    1.3.2 Files and the working memory
    1.3.3 Loading data
    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 Define 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 of 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
  1. 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 Exercises
  1. 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 optionals
    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
    Probability weights
    3.4 Exercises
  1. General comments on the statistical commands
    4.1 Regular statistical commands
    4.2 Estimation commands
              4.3 Exercises
  1. 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 Recording string variables
    5.4 Recording 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
  1. 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 Graphs 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
    6.6 Exercises
  1. 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
    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
    Comparing distributions with Q–Q plots
    7.4 Exercises

  2. 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
  1.             9.1 Simple linear regression
    9.1.1 The basic principle
    9.1.2 Linear regression using Stata
    The table of coefficients
    Standard errors
    The table of ANOVA results
    The model fit table
    9.2 Multiple regression
    9.2.1 Multiple regression using Stata
    9.2.2 Additional components
    Adjusted R2
    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 relations
    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 Advanced techniques
    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-component models
    9.7 Exercises
  1. Regression models for Categorical Dependent Variables
    10.1 The linear probability model
    10.2 Basic concepts
    9.2.1 Odds, log odds, and odds ratios
    9.2.2 Excursion: The maximum likelihood principle
    10.3 Logistic regression with Stata
    10.3.1 The coefficients table
    Sign interpretation
    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
    9.4.1 Linearity
    9.4.2 Influential cases
    10.5 Likelihood-ratio test
    10.6 Refined models
    10.6.1 Nonlinear relationships
    10.6.2 Interaction effects
    10.7 Advanced techniques
    10.7.1 Probit models
    10.7.2 Multinomial logistic regression
    10.7.3 Models for ordinal data
    10.8 Exercises
  1. Reading and writing data
    11.1 The goal: The data matrix
    11.2 Importing machine-readable data
    11.2.1 Reading system files from other packages
                          Reading Excel files
                          Reading SAS transport files
                          Reading other system files
    11.2.2 Reading ASCII text files 
    Reading data in spreadsheet format
    Reading data in free format
    Reading data in fixed format
    11.3 Inputting data
    11.3.1 Input data using the 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 lage datasets
    10.6.1 Rules for handling the working memory
    10.6.2 Using oversized datasets
    11.7 Exercises
  1. 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 12.3.1 Sketch of the syntax
    12.3.2 Create a first ado-file
    12.3.3 Parsing variable lists
    11.3.4 Parsing options
    11.3.5 Parsing if and in qualifiers
    11.3.6 Generating an unknown number of variables
    11.3.7 Default values
    11.3.8 Extended macro functions
    11.3.9 Avoiding changes in the dataset
    11.3.10 Help files
    12.4 Exercises
  1. Around Stata
    13.1 Resources and information
    13.2 Taking care of Stata
    13.3 Additional procedures
    13.3.1 Stata Journal ado-files 
    13.3.2 SSC ado-files
    13.3.3 Other ado-files
    13.4 Exercises

References

Authors Index

Subject Index

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