Alan C. Acock’s *A Gentle Introduction to Stata, Revised Sixth Edition* is aimed at new Stata users who want to become proficient in Stata. After reading this introductory text, new users will be able to not only use Stata well but also learn new aspects of Stata.

Acock assumes that the user is not familiar with any statistical software. This assumption of a blank slate is central to the structure and contents of the book. Acock starts with the basics; for example, the part of the book that deals with data management begins with a careful and detailed example of turning survey data on paper into a Stata-ready dataset. When explaining how to go about basic exploratory statistical procedures, Acock includes notes that will help the reader develop good work habits. This mixture of explaining good Stata habits and explaining good statistical habits continues throughout the book.

Acock is quite careful to teach the reader all aspects of using Stata. He covers data management, good work habits (including the use of basic do-files), basic exploratory statistics (including graphical displays), and analyses using the standard array of basic statistical tools (correlation, linear and logistic regression, and parametric and nonparametric tests of location and dispersion). He also successfully introduces some more advanced topics such as multiple imputation and multilevel modeling in a very approachable manner. Acock teaches Stata commands by using the menus and dialog boxes while still stressing the value of Stata commands and do-files. In this way, he ensures that all types of users can build good work habits. Each chapter has exercises that the motivated reader can use to reinforce the material.

The tone of the book is friendly and conversational without ever being glib or condescending. Important asides and notes about terminology are set off in boxes, which makes the text easy to read without any convoluted twists or forward referencing. Rather than splitting topics by their Stata implementation, Acock arranges the topics as they would appear in a basic statistics textbook; graphics and postestimation are woven into the material naturally. Real datasets, such as the *General Social Surveys* from 2002, 2006, and 2016, are used throughout the book.

The focus of the book is especially helpful for those in the behavioral and social sciences because the presentation of basic statistical modeling is supplemented with discussions of effect sizes and standardized coefficients. Various selection criteria, such as semipartial correlations, are discussed for model selection. Acock also covers a variety of commands available for evaluating reliability and validity of measurements.

The revised sixth edition is fully up to date for Stata 17, including updated discussion and images of Stata’s interface and modern command syntax. In addition, examples include new features such as the **table** command and **collect suite** for creating and exporting customized tables as well as the option for creating graphs with transparency.

© Copyright 1996–2023 StataCorp LLC

**List of figures**

** List of tables**

** List of boxed tips**

**Preface** (PDF)

** Support materials for the book
**

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 Video aids to learning Stata

1.7 Summary

1.8 Exercises

2.2 An example questionnaire

2.3 Developing a coding system

2.4 Entering data using the Data Editor

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

3.2 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

4.2 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

5.2 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

6.2 Cross-tabulation

6.3 Chi-squared test

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

7.2 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

7.9 Repeated-measures t test

7.10 Power analysis

7.11 Nonparametric alternatives

7.11.2 Nonparametric alternative: Median test

7.12 Video tutorial related to this chapter

7.13 Summary

7.14 Exercises

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 Power analysis with correlation

8.9 Summary

8.10 Exercises

9.2 ANOVA example

9.3 ANOVA example with nonexperimental data

9.4 Power analysis for one-way ANOVA

9.5 A nonparametric alternative to ANOVA

9.6 Analysis of covariance

9.7 Two-way ANOVA

9.8 Repeated-measures design

9.9 Intraclass correlation—measuring agreement

9.10 Power analysis with ANOVA

9.10.2 Power analysis for two-way ANOVA

9.10.3 Power analysis for repeated-measures ANOVA

9.10.4 Summary of power analysis for ANOVA

9.11 Summary

9.12 Exercises

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

10.7.2 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

10.12.2 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

11.2 An example

11.3 What is an odds ratio and a logit?

11.3.2 The logit transformation

11.4 Data used in the rest of the chapter

11.5 Logistic regression

11.6 Hypothesis testing

11.6.2 Testing sets of coefficients

11.7 Margins: More on interpreting results from logistic regression

11.8 Nested logistic regressions

11.9 Power analysis when doing logistic regression

11.10 Next steps for using logistic regression and its extensions

11.11 Summary

11.12 Exercises

12.2 Constructing a scale

12.3 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

12.4.2 Criterion-related validity

12.4.3 Construct validity

12.5 Factor analysis

12.6 PCF analysis

12.6.2 Oblique rotation: Promax

12.7 But we wanted one scale, not four scales

12.8 Summary

12.9 Exercises

13.1 Linear regression using sem

13.1.2 SEM and working with missing values

13.1.3 Exploring missing values and auxiliary variables

13.1.4 Getting auxiliary variables into your SEM command

13.2 A quick way to draw a regression model

13.3 The gsem command for logistic regression

13.3.2 Fitting the model using the gsem command

13.4 Path analysis and mediation

13.5 Conclusions and what is next for the sem command

13.6 Exercises

14.2 What variables do we include when doing imputations?

14.3 The nature of the problem

14.4 Multiple imputation and its assumptions about the mechanism for missingness

14.5 Multiple imputation

14.6 A detailed example

14.6.2 Setup and multiple-imputation stage

14.6.3 The analysis stage

14.6.4 For those who want an

*R*

^{2}and standardized

*β*s

14.6.5 When impossible values are imputed

14.7 Summary

14.8 Exercises

15.2 Questions and data for a longitudinal multilevel application

15.3 Fixed-effects regression models

15.4 Random-effects regression models

15.5 An applied example

15.5.2 Reshaping data to do multilevel analysis

15.6 A quick visualization of our data

15.7 Random-intercept model

15.7.2 Random-intercept model—quadratic term

15.7.3 Treating time as a categorical variable

15.8 Random-coefficients model

15.9 Including a time-invariant covariate

15.10 Summary

15.11 Exercises

16.2 Overview of three IRT models for dichotomous items

16.2.2 The two-parameter logistic (2PL) model

16.2.3 The three-parameter logistic (3PL) model

16.3 Fitting the 1PL model using Stata

16.3.2 How important is each of the items?

16.3.3 An overall evaluation of our scale

16.3.4 Estimating the latent score

16.4 Fitting a 2PL IRT model

16.5 The graded response model—IRT for Likert-type items

16.5.2 Fitting our graded response model

16.5.3 Estimating a person’s score

16.6 Reliability of the fitted IRT model

16.7 Using the Stata menu system

16.8 Extensions of IRT

16.9 Exercises

A.2 Resources

A.2.2 Books about Stata

A.2.3 Short courses

A.2.4 Acquiring data

A.2.5 Learning from the postestimation methods

A.3 Summary

**Glossary of acronyms**

**Glossary of mathematical and statistical symbols**

**References**

© Copyright 1996–2023 StataCorp LLC