1 Getting started

• 1.1 Conventions
• 1.2 Introduction
• 1.3 The Stata screen
• 1.4 Using an existing dataset
• 1.5 An example of a short Stata session
• 1.6 Summary
• 1.7 Exercises

2 Entering data

• 2.1 Creating a dataset
• 2.2 An example questionnaire
• 2.3 Develop a coding system
• 2.4 Entering data using the Data Editor
  • 2.4.1 Value labels
• 2.5 The Variables Manager
• 2.6 The Data Editor (Browse) view
• 2.7 Saving your dataset
• 2.8 Checking the data
• 2.9 Summary
• 2.10 Exercises

3 Preparing data for analysis

• 3.1 Introduction
• 3.2 Planning your work
• 3.3 Creating value labels
• 3.4 Reverse-code variables
• 3.5 Creating and modifying variables
• 3.6 Creating scales
• 3.7 Save some of your data
• 3.8 Summary
• 3.9 Exercises

4 Working with commands, do-files, and results

• 4.1 Introduction
• 4.2 How Stata commands are constructed
• 4.3 Creating a do-file
• 4.4 Copying your results to a word processor
• 4.5 Logging your command file
• 4.6 Summary
• 4.7 Exercises

5 Descriptive statistics and graphs for one variable

• 5.1 Descriptive statistics and graphs
• 5.2 Where is the center of a distribution?
• 5.3 How dispersed is the distribution?
• 5.4 Statistics and graphs—unordered categories
• 5.5 Statistics and graphs—ordered categories and variables
• 5.6 Statistics and graphs—quantitative variables
• 5.7 Summary
• 5.8 Exercises

6 Statistics and graphs for two categorical variables

• 6.1 Relationship between categorical variables
• 6.2 Cross-tabulation
• 6.3 Chi-squared test
  • 6.3.1 Degrees of freedom
  • 6.3.2 Probability tables
• 6.4 Percentages and measures of association
• 6.5 Odds ratios when dependent variable has two categories
• 6.6 Ordered categorical variables
• 6.7 Interactive tables
• 6.8 Tables—linking categorical and quantitative variables
• 6.9 Power analysis when using a chi-squared test of significance
• 6.10 Summary
• 6.11 Exercises

7 Tests for one or two means

• 7.1 Introduction to tests for one or two means
• 7.2 Randomization
• 7.3 Random sampling
• 7.4 Hypotheses
• 7.5 One-sample test of a proportion
• 7.6 Two-sample test of a proportion
• 7.7 One-sample test of means
• 7.8 Two-sample test of group means
  • 7.8.1 Testing for unequal variances
• 7.9 Repeated-measures t test
• 7.10 Power analysis
• 7.11 Nonparametric alternatives
  • 7.11.1 Mann–Whitney two-sample rank-sum test
  • 7.11.2 Nonparametric alternative: Median test
• 7.12 Summary
• 7.13 Exercises

8 Bivariate correlation and regression

• 8.1 Introduction to bivariate correlation and regression
• 8.2 Scattergrams
• 8.3 Plotting the regression line
• 8.4 Correlation
• 8.5 Regression
• 8.6 Spearman’s rho: Rank-order correlation for ordinal data
• 8.7 Summary
• 8.8 Exercises

9 Analysis of variance

• 9.1 The logic of one-way analysis of variance
• 9.2 ANOVA example
• 9.3 ANOVA example using survey data
• 9.4 A nonparametric alternative to ANOVA
• 9.5 Analysis of covariance
• 9.6 Two-way ANOVA
• 9.7 Repeated-measures design
• 9.8 Intraclass correlation—measuring agreement
• 9.9 Summary
• 9.10 Exercises

10 Multiple regression

• 10.1 Introduction to multiple regression
• 10.2 What is multiple regression
• 10.3 The basic multiple regression command
• 10.4 Increment in R-squared: Semipartial correlations
• 10.5 Is the dependent variable normally distributed?
• 10.6 Are the residuals normally distributed?
• 10.7 Regression diagnostic statistics
  • 10.7.1 Outliers and influential cases
  • 10.7.2 Influential observations: DFbeta
  • 10.7.3 Combinations of variables may cause problems
• 10.8 Weighted data
• 10.9 Categorical predictors and hierarchical regression
• 10.10 A shortcut for working with a categorical variable
• 10.11 Fundamentals of interaction
• 10.12 Power analysis in multiple regression
• 10.13 Summary
• 10.14 Exercises

11 Logistic regression

• 11.1 Introduction to logistic regression
• 11.2 An example
• 11.3 What is an odds ratio and a logit?
  • 11.3.1 The odds ratio
  • 11.3.2 The logit transformation
• 11.4 Data used in rest of chapter
• 11.5 Logistic regression
• 11.6 Hypothesis testing
  • 11.6.1 Testing individual coefficients
  • 11.6.2 Testing sets of coefficients
• 11.7 Nested logistic regressions
• 11.8 Power analysis when doing logistic regression
• 11.9 Summary
• 11.10 Exercises

12 Measurement, reliability, and validity

• 12.1 Overview of reliability and validity
• 12.2 Constructing a scale
  • 12.2.1 Generating a mean score for each person
• 12.3 Reliability
  • 12.3.1 Stability and test–retest reliability
  • 12.3.2 Equivalence
  • 12.3.3 Split-half and alpha reliability—internal consistency
  • 12.3.4 Kuder–Richardson reliability for dichotomous items
  • 12.3.5 Rater agreement—kappa (K)
• 12.4 Validity
  • 12.4.1 Expert judgment
  • 12.4.2 Criterion-related validity
  • 12.4.3 Construct validity
• 12.5 Factor analysis
• 12.6 PCF analysis
  • 12.6.1 Orthogonal rotation: Varimax
  • 12.6.2 Oblique rotation: Promax
• 12.7 But we wanted one scale, not four scales
  • 12.7.1 Scoring our variable
• 12.8 Summary
• 12.9 Exercises

13 Working with missing values—multiple imputation

• 13.1 The nature of the problem
• 13.2 Multiple imputation and its assumptions about the mechanism for missingness
• 13.3 What variables do we include when doing imputations?
• 13.4 Multiple imputation
• 13.5 A detailed example
  • 13.5.1 Preliminary analysis
  • 13.5.2 Setup and multiple-imputation stage
  • 13.5.3 The analysis stage
  • 13.5.4 For those who want an R2 and standardized βs
  • 13.5.5 When impossible values are imputed
• 13.6 Summary
• 13.7 Exercises

A What’s next?

• A.1 Introduction to the appendix
• A.2 Resources
  • A.2.1 Web resources
  • A.2.2 Books about Stata
  • A.2.3 Short courses
  • A.2.4 Acquiring data
• A.3 Summary

References
Author index (pdf)
Subject index(pdf)