A gentle introduction to Stata

Getting started

Entering data

Preparing data for analysis

Working with commands, do-files, and results

Descriptive statistics and graphs for one variable

Statistics and graphs for two categorical variables

Tests for one or two means

Bivariate correlation and regression

Analysis of variance

Multiple regression

Logistic regression

Measurement, reliability, and validity

Structural equation and generalized structural equation modeling

Working with missing values - mulitple imputation

An introduction to multilevel analysis

Item response theory (IRT)

What's next?

1. Getting started

2. Entering data

3. Preparing data for analysis

4. Working with commands, do-files, and results

5. Descriptive statistics and graphs for one variable

6. Statistics and graphs for two categorical variables

7. Tests for one or two means

8. Bivariate correlation and regression

9. Analysis of variance

10. Multiple regression

11. Logistic regression

12. Measurement, reliability, and validity

13. Working with missing values

14. The sem and gsem commands

A. What's next?

List of tables

List of figures

Preface

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. Developing 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. Saving 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 the rest of the 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 (κ)

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 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

Subject index