Comment from the Stata technical group
Alan C. Acock’s A Gentle Introduction to Stata is an ideal book for
students and for those with experience with other software packages but new to Stata.
For the reader who is just learning social statistics and has
never used a statistics package before, Acock leads the way, first
explaining how to use the Stata GUI, use dialog boxes, and load Stata
datasets before moving on to statistical analysis.
Users who are familiar with other statistical software will be able to move
quickly through the book and become fluent in Stata with ease.
Acock emphasizes the use of dialog boxes when starting out, since using
them makes finding options easier and they allow you to see the Stata
commands that will accomplish the same thing. This approach is
particularly useful when learning the graphics system.
The book relies on real datasets like the General Social Survey for 2002
and the National Survey of Youth, 1997, to illustrate the
real-world application of statistics instead of using small,
artificially created ones. Exercises at the end of each chapter use
these datasets. The examples and exercises will appeal to anyone in the
social sciences, including economists, sociologists, and psychologists.
The first four chapters are geared toward the mechanics of using Stata:
navigating the user interface, loading data into Stata, creating value
and variable labels, and saving results and logging commands. The
rest of the book follows the outline of a typical introductory
statistics course, beginning with descriptive statistics and graphs for
a single variable, such as pie graphs and histograms, and then moving to
bivariate analysis of two categorical variables, including tables and
chi-squared tests. One- and two-sample t-tests are covered, as well as
nonparametric alternatives like the rank-sum test and the median test.
A chapter is devoted to bivariate analysis of continuous variables,
including simple correlation, regression, Spearman’s rho, Cronbach’s
alpha, and the kappa statistic of interrater agreement. One- and
two-way ANOVA and repeated-measure designs are then covered, followed by
a chapter on multiple regression and diagnostics. The final chapter on
statistics is devoted to logistic regression.
Acock’s A Gentle Introduction to Stata is an excellent first book for
people new to Stata. The book would be particularly
useful as a supplementary textbook for an introductory statistics
course, allowing students to learn Stata on their own while course time
is spent on statistics.
Table of contents
Preface (pdf)
Support materials for the book
1 Getting started
- 1.1 Introduction
- 1.2 The Stata screen
- 1.3 Using an existing dataset
- 1.4 An example of a short Stata session
- 1.5 Conventions
- 1.6 Chapter 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
- 2.4.1 Labeling values
- 2.5 Saving your dataset
- 2.6 Checking the data
- 2.7 Chapter summary
- 2.8 Exercises
3 Preparing data for analysis
- 3.1 Introduction
- 3.2 Plan your work
- 3.3 Create value labels
- 3.4 Reverse-code variables
- 3.5 Create and modify variables
- 3.6 Create 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 Getting the command from the menu system
- 4.4 Saving your results
- 4.5 Logging your command file
- 4.6 Summary
- 4.7 Exercises
5 Descriptive statistics and graphs for a single 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
- 6.3.1 Degrees of freedom—optional
- 6.3.2 Probability tables—optional
- 6.4 Percentages and measures of association
- 6.5 Ordered categorical variables
- 6.6 Interactive tables
- 6.7 Tables—linking categorical and quantitative variables
- 6.8 Summary
- 6.9 Exercises
7 Tests for one or two means
- 7.1 Tests for one or two means
- 7.2 Randomization
- 7.3 Hypotheses
- 7.4 One-sample test of a proportion
- 7.5 Two-sample test of a proportion
- 7.6 One-sample test of means
- 7.7 Two-sample test of group means
- 7.7.1 Testing for unequal variances
- 7.8 Repeated-measures t test
- 7.9 Power analysis
- 7.10 Nonparametric alternatives
- 7.10.1 Mann–Whitney two-sample rank-sum test
- 7.10.2 Nonparametric alternative: median test
- 7.11 Summary
- 7.12 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 Alpha reliability
- 8.8 Kappa as a measure of agreement for categorical data
- 8.9 Summary
- 8.10 Exercises
9 Analysis of variance (ANOVA)
- 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
- 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 Fundamentals of interaction
- 10.11 Summary
- 10.12 Exercises
11 Logistic regression
- 11.1 Introduction
- 11.2 An example
- 11.3 What are 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 Summary
- 11.9 Exercises
12 What's next?
- 12.1 Introduction
- 12.2 Resources
- 12.2.1 Web resources
- 12.2.2 Books on Stata
- 12.2.3 Short courses
- 12.2.4 Acquiring data
- 12.3 Summary
References
Author index (pdf)
Subject index (pdf)
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