A Gentle Introduction to Stata, Fifth Edition
5th Edition
Published on 19. April 2016
Book
Paperback/Softback
546 pages
978-1-59718-185-3 (ISBN)
Description
Alan C. Acock's A Gentle Introduction to Stata, Fifth Edition,
is aimed at new Stata users who want to become proficient in Stata.
After reading this introductory text, new users will be able not only
to use Stata well but also to 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 portion 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 on the computer. 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 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
structural equation modeling in a very approachable manner. Acock
teaches Stata commands by using the menus and dialog boxes while still
stressing the value of 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 in a natural fashion. Real datasets, such as
the General Social Surveys from 2002 and 2006, 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 fifth edition of the book includes two new chapters that cover
multilevel modeling and item response theory (IRT) models. The
multilevel modeling chapter demonstrates how to fit linear multilevel
models using the mixed command. Acock discusses models with
both random intercepts and random coefficients, and he provides a
variety of examples that apply these models to longitudinal data. The
IRT chapter introduces the use of IRT models for evaluating a set of
items designed to measure a specific trait such as an attitude, value,
or a belief. Acock shows how to use the irt suite of commands,
which are new in Stata 14, to fit IRT models and to graph the results.
In addition, he presents a measure of reliability that can be computed
when using IRT.
is aimed at new Stata users who want to become proficient in Stata.
After reading this introductory text, new users will be able not only
to use Stata well but also to 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 portion 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 on the computer. 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 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
structural equation modeling in a very approachable manner. Acock
teaches Stata commands by using the menus and dialog boxes while still
stressing the value of 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 in a natural fashion. Real datasets, such as
the General Social Surveys from 2002 and 2006, 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 fifth edition of the book includes two new chapters that cover
multilevel modeling and item response theory (IRT) models. The
multilevel modeling chapter demonstrates how to fit linear multilevel
models using the mixed command. Acock discusses models with
both random intercepts and random coefficients, and he provides a
variety of examples that apply these models to longitudinal data. The
IRT chapter introduces the use of IRT models for evaluating a set of
items designed to measure a specific trait such as an attitude, value,
or a belief. Acock shows how to use the irt suite of commands,
which are new in Stata 14, to fit IRT models and to graph the results.
In addition, he presents a measure of reliability that can be computed
when using IRT.
More details
Edition
5th New edition
Language
English
Place of publication
College Station
United States
Target group
College/higher education
Edition type
New edition
Weight
1134 gr
ISBN-13
978-1-59718-185-3 (9781597181853)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
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Alan C. Acock
A Gentle Introduction to Stata, Revised Sixth Edition
Book
01/2023
6th Edition
Stata Press
€74.50
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A Gentle Introduction to Stata, Sixth Edition
Book
04/2018
6th Edition
Stata Press
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Alan C. Acock
A Gentle Introduction to Stata, Fourth Edition
Book
04/2014
4th Edition
Stata Press
€85.60
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Person
Content
Getting started
Conventions
Introduction
The Stata screen
Using an existing dataset
An example of a short Stata session
Video aids to learning Stata
Summary
Exercises
Entering data
Creating a dataset
An example questionnaire
Developing a coding system
Entering data using the Data Editor
Value labels
The Variables Manager
The Data Editor (Browse) view
Saving your dataset
Checking the data
Summary
Exercises
Preparing data for analysis
Introduction
Planning your work
Creating value labels
Reverse-code variables
Creating and modifying variables
Creating scales
Save some of your data
Summary
Exercises
Working with commands, do-files, and results
Introduction
How Stata commands are constructed
Creating a do-file
Copying your results to a word processor
Logging your command file
Summary
Exercises
Descriptive statistics and graphs for one variable
Descriptive statistics and graphs
Where is the center of a distribution?
How dispersed is the distribution?
Statistics and graphs-unordered categories
Statistics and graphs-ordered categories and variables
Statistics and graphs-quantitative variables
Summary
Exercises
Statistics and graphs for two categorical variables
Relationship between categorical variables
Cross-tabulation
Chi-squared test
Degrees of freedom
Probability tables
Percentages and measures of association
Odds ratios when dependent variable has two categories
Ordered categorical variables
Interactive tables
Tables--linking categorical and quantitative variables
Power analysis when using a chi-squared test of significance
Summary
Exercises
Tests for one or two means
Introduction to tests for one or two means
Randomization
Random sampling
Hypotheses
One-sample test of a proportion
Two-sample test of a proportion
One-sample test of means
Two-sample test of group means
Testing for unequal variances
Repeated-measures t test
Power analysis
Nonparametric alternatives
Mann--Whitney two-sample rank-sum test
Nonparametric alternative: Median test
Video tutorial related to this chapter
Summary
Exercises
Bivariate correlation and regression
Introduction to bivariate correlation and regression
Scattergrams
Plotting the regression line
An alternative to producing a scattergram, binscatter
Correlation
Regression
Spearman's rho: Rank-order correlation for ordinal data
Power analysis with correlation
Summary
Exercises
Analysis of variance
The logic of one-way analysis of variance
ANOVA example
ANOVA example with nonexperimental data
Power analysis for one-way ANOVA
A nonparametric alternative to ANOVA
Analysis of covariance
Two-way ANOVA
Repeated-measures design
Intraclass correlation<-measuring agreement
Power analysis with ANOVA
Power analysis for one-way ANOVA
Power analysis for two-way ANOVA
Power analysis for repeated-measures ANOVA
Summary of power analysis for ANOVA
Summary
Exercises
Multiple regression
Introduction to multiple regression
What is multiple regression?
The basic multiple regression command
Increment in R-squared: Semipartial correlations
Is the dependent variable normally distributed?
Are the residuals normally distributed?
Regression diagnostic statistics
Outliers and influential cases
Influential observations: DFbeta
Combinations of variables may cause problems
Weighted data
Categorical predictors and hierarchical regression
A shortcut for working with a categorical variable
Fundamentals of interaction
Nonlinear relations
Fitting a quadratic model
Centering when using a quadratic term
Do we need to add a quadratic component?
Power analysis in multiple regression
Summary
Exercises
Logistic regression
Introduction to logistic regression
An example
What is an odds ratio and a logit?
The odds ratio
The logit transformation
Data used in the rest of the chapter
Logistic regression
Hypothesis testing
Testing individual coefficients
Testing sets of coefficients
More on interpreting results from logistic regression
Nested logistic regressions
Power analysis when doing logistic regression
Next steps for using logistic regression and its extensions
Summary
Exercises
Measurement, reliability, and validity
Overview of reliability and validity
Constructing a scale
Generating a mean score for each person
Reliability
Stability and test-retest reliability
Equivalence
Split-half and alpha reliabilit--internal consistency
Kuder-Richardson reliability for dichotomous items
Rater agreement-kappa (K)
Validity
Expert judgment
Criterion-related validity
Construct validity
Factor analysis
PCF analysis
Orthogonal rotation: Varimax
Oblique rotation: Promax
But we wanted one scale, not four scales
Scoring our variable
Summary
Exercises
Working with missing values-multiple imputation
The nature of the problem
Multiple imputation and its assumptions about the mechanism for missingness
What variables do we include when doing imputations?
Multiple imputation
A detailed example
Preliminary analysis
Setup and multiple-imputation stage
The analysis stage
For those who want an R and standardized sss
When impossible values are imputed
Summary
Exercises
The sem and gsem commands
Linear regression using sem
Using the SEM Builder to fit a basic regression model
A quick way to draw a regression model and a fresh start
Using sem without the SEM Builder
The gsem command for logistic regression
Fitting the model using the logit command
Fitting the model using the gsem command
Path analysis and mediation
Conclusions and what is next for the sem command
Exercises
An introduction to multilevel analysis
Questions and data for groups of individuals
Questions and data for a longitudinal multilevel application
Fixed-effects regression models
Random-effects regression models
An applied example
Research questions
Reshaping data to do multilevel analysis
A quick visualization of our data
Random-intercept model
Random intercept-linear model
Random-intercept model-quadratic term
Treating time as a categorical variable
Random-coefficients model
Including a time-invariant covariate
Summary
Exercises
Item response theory (IRT)
How are IRT measures of variables different from summated scales?
Overview of three IRT models for dichotomous items
The one-parameter logistic (PL) model
The two-parameter logistic (PL) model
The three-parameter logistic (PL) model
Fitting the PL model using Stata
The estimation
How important is each of the items?
An overall evaluation of our scale
Estimating the latent score
Fitting a PL IRT model
Fitting the PL model
The graded response model-IRT for Likert-type items
The data
Fitting our graded response model
Estimating a person's score
Reliability of the fitted IRT model
Using the Stata menu system
Extensions of IRT
Exercises
What's next?
Introduction to the appendix
Resources
Web resources
Books about Stata
Short courses
Acquiring data
Learning from the postestimation methods
Summary
Conventions
Introduction
The Stata screen
Using an existing dataset
An example of a short Stata session
Video aids to learning Stata
Summary
Exercises
Entering data
Creating a dataset
An example questionnaire
Developing a coding system
Entering data using the Data Editor
Value labels
The Variables Manager
The Data Editor (Browse) view
Saving your dataset
Checking the data
Summary
Exercises
Preparing data for analysis
Introduction
Planning your work
Creating value labels
Reverse-code variables
Creating and modifying variables
Creating scales
Save some of your data
Summary
Exercises
Working with commands, do-files, and results
Introduction
How Stata commands are constructed
Creating a do-file
Copying your results to a word processor
Logging your command file
Summary
Exercises
Descriptive statistics and graphs for one variable
Descriptive statistics and graphs
Where is the center of a distribution?
How dispersed is the distribution?
Statistics and graphs-unordered categories
Statistics and graphs-ordered categories and variables
Statistics and graphs-quantitative variables
Summary
Exercises
Statistics and graphs for two categorical variables
Relationship between categorical variables
Cross-tabulation
Chi-squared test
Degrees of freedom
Probability tables
Percentages and measures of association
Odds ratios when dependent variable has two categories
Ordered categorical variables
Interactive tables
Tables--linking categorical and quantitative variables
Power analysis when using a chi-squared test of significance
Summary
Exercises
Tests for one or two means
Introduction to tests for one or two means
Randomization
Random sampling
Hypotheses
One-sample test of a proportion
Two-sample test of a proportion
One-sample test of means
Two-sample test of group means
Testing for unequal variances
Repeated-measures t test
Power analysis
Nonparametric alternatives
Mann--Whitney two-sample rank-sum test
Nonparametric alternative: Median test
Video tutorial related to this chapter
Summary
Exercises
Bivariate correlation and regression
Introduction to bivariate correlation and regression
Scattergrams
Plotting the regression line
An alternative to producing a scattergram, binscatter
Correlation
Regression
Spearman's rho: Rank-order correlation for ordinal data
Power analysis with correlation
Summary
Exercises
Analysis of variance
The logic of one-way analysis of variance
ANOVA example
ANOVA example with nonexperimental data
Power analysis for one-way ANOVA
A nonparametric alternative to ANOVA
Analysis of covariance
Two-way ANOVA
Repeated-measures design
Intraclass correlation<-measuring agreement
Power analysis with ANOVA
Power analysis for one-way ANOVA
Power analysis for two-way ANOVA
Power analysis for repeated-measures ANOVA
Summary of power analysis for ANOVA
Summary
Exercises
Multiple regression
Introduction to multiple regression
What is multiple regression?
The basic multiple regression command
Increment in R-squared: Semipartial correlations
Is the dependent variable normally distributed?
Are the residuals normally distributed?
Regression diagnostic statistics
Outliers and influential cases
Influential observations: DFbeta
Combinations of variables may cause problems
Weighted data
Categorical predictors and hierarchical regression
A shortcut for working with a categorical variable
Fundamentals of interaction
Nonlinear relations
Fitting a quadratic model
Centering when using a quadratic term
Do we need to add a quadratic component?
Power analysis in multiple regression
Summary
Exercises
Logistic regression
Introduction to logistic regression
An example
What is an odds ratio and a logit?
The odds ratio
The logit transformation
Data used in the rest of the chapter
Logistic regression
Hypothesis testing
Testing individual coefficients
Testing sets of coefficients
More on interpreting results from logistic regression
Nested logistic regressions
Power analysis when doing logistic regression
Next steps for using logistic regression and its extensions
Summary
Exercises
Measurement, reliability, and validity
Overview of reliability and validity
Constructing a scale
Generating a mean score for each person
Reliability
Stability and test-retest reliability
Equivalence
Split-half and alpha reliabilit--internal consistency
Kuder-Richardson reliability for dichotomous items
Rater agreement-kappa (K)
Validity
Expert judgment
Criterion-related validity
Construct validity
Factor analysis
PCF analysis
Orthogonal rotation: Varimax
Oblique rotation: Promax
But we wanted one scale, not four scales
Scoring our variable
Summary
Exercises
Working with missing values-multiple imputation
The nature of the problem
Multiple imputation and its assumptions about the mechanism for missingness
What variables do we include when doing imputations?
Multiple imputation
A detailed example
Preliminary analysis
Setup and multiple-imputation stage
The analysis stage
For those who want an R and standardized sss
When impossible values are imputed
Summary
Exercises
The sem and gsem commands
Linear regression using sem
Using the SEM Builder to fit a basic regression model
A quick way to draw a regression model and a fresh start
Using sem without the SEM Builder
The gsem command for logistic regression
Fitting the model using the logit command
Fitting the model using the gsem command
Path analysis and mediation
Conclusions and what is next for the sem command
Exercises
An introduction to multilevel analysis
Questions and data for groups of individuals
Questions and data for a longitudinal multilevel application
Fixed-effects regression models
Random-effects regression models
An applied example
Research questions
Reshaping data to do multilevel analysis
A quick visualization of our data
Random-intercept model
Random intercept-linear model
Random-intercept model-quadratic term
Treating time as a categorical variable
Random-coefficients model
Including a time-invariant covariate
Summary
Exercises
Item response theory (IRT)
How are IRT measures of variables different from summated scales?
Overview of three IRT models for dichotomous items
The one-parameter logistic (PL) model
The two-parameter logistic (PL) model
The three-parameter logistic (PL) model
Fitting the PL model using Stata
The estimation
How important is each of the items?
An overall evaluation of our scale
Estimating the latent score
Fitting a PL IRT model
Fitting the PL model
The graded response model-IRT for Likert-type items
The data
Fitting our graded response model
Estimating a person's score
Reliability of the fitted IRT model
Using the Stata menu system
Extensions of IRT
Exercises
What's next?
Introduction to the appendix
Resources
Web resources
Books about Stata
Short courses
Acquiring data
Learning from the postestimation methods
Summary