A Gentle Introduction to Stata
Author: 
Alan C. Acock 
Publisher: 
Stata Press 
Copyright: 
2006 
ISBN13: 
9781597180092 
Pages: 
289; paperback 
Price: 
$42.00 



Comment from the Stata technical group
Alan C. Acock’s A Gentle Introduction to Stata is an ideal book for
students and for those who have experienced other statistical software
packages but are new to Stata. Acock leads the way for the reader who is
just learning social statistics and has never before used a statistics
software package. He first explains 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 packages can use this
book to quickly become fluent in Stata.
Acock emphasizes using dialog boxes when beginning in Stata because dialog
boxes make finding options easier and show the Stata commands that will
accomplish what the reader is after. Using dialog boxes is particularly
helpful when learning the graphics system.
Instead of creating small, artificial datasets, Acock relies on real
datasets—like the General Social Survey for 2002 and the National
Survey of Youth (1997)—to illustrate the realworld application of
statistics. Each chapter uses the datasets in examples and endofchapter
exercises that will appeal to anyone in the social sciences, including
economists, sociologists, and psychologists.
The first four chapters are geared toward the basics of using Stata:
navigating the user interface, loading data into Stata, creating value and
variable labels, saving results, and logging commands. The remainder of the
book follows the outline of a typical introductory statistics course,
beginning with singlevariable descriptive statistics and graphs, such as
pie graphs and histograms, and moving on to bivariate analysis of two
categorical variables, including tables and chisquared tests. Acock covers
one and twosample t tests, as well as nonparametric alternatives
like the ranksum 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. Acock then covers one and twoway ANOVA and
repeatedmeasure designs, followed by a chapter on multiple regression and
diagnostics. The final statistics chapter is devoted to logistic regression.
A Gentle Introduction to Stata is an excellent beginner’s guide
for people new to Stata. The book would be particularly useful as a
supplementary text for an introductory statistics course, allowing students
to learn statistics in the classroom and Stata at home.
Note for Stata 10 users: The commands discussed in this book work the same
in Stata 10 as they did in Stata 9, but some of the menus and dialog boxes
may differ slightly.
Table of contents
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 Reversecode 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, dofiles, 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 Crosstabulation
6.3 Chisquared
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 Onesample test of a proportion
7.5 Twosample test of a proportion
7.6 Onesample test of means
7.7 Twosample test of group means
7.7.1 Testing for unequal variances
7.8 Repeatedmeasures t test
7.9 Power analysis
7.10 Nonparametric alternatives
7.10.1 Mann–Whitney twosample ranksum 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: rankorder 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 oneway 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 Twoway ANOVA
9.7 Repeatedmeasures 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 Rsquared: 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