List of figures

List of tables

List of boxed tips

Support materials for the book

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 An alternative to producing a scattergram, binscatter

8.5 Correlation

8.6 Regression

8.7 Spearman’s rho: Rank-order correlation for ordinal data

8.8 Summary

8.9 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 Power analysis with ANOVA

9.9.1 One-way ANOVA

Power analysis for two-way ANOVA

9.9.2 Power analysis for repeated-measures ANOVA

9.9.3 Summary of power analysis for ANOVA

9.10 Summary

9.11 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 Nonlinear relations

10.12.1 Fitting a quadratic model

10.12.2 Centering when using a quadratic term

10.12.3 Do we need to add a quadratic component?

10.13 Power analysis in multiple regression

10.14 Summary

10.15 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 More on interpreting results from logistic regression

11.8 Nested logistic regressions

11.9 Power analysis when doing logistic regression

11.10 Summary

11.11 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 *R*^{2} and standardized *β*s

13.5.5 When impossible values are imputed

13.6 Summary

13.7 Exercises

14 The sem and gsem commands

14.1 Ordinary least-squares regression models using sem

14.1.1 Using the SEM Builder to fit a basic regression model

14.2 A quick way to draw a regression model and a fresh start

14.2.1 Using sem without the SEM Builder

14.3 WThe gsem command for logistic regression

14.3.1 Fitting the model using the logit command

14.3.2 Fitting the model using the gsem command

14.4 Path analysis and mediation

14.5 Conclusions and what is next for the sem command

14.6 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