A gentle introduction to stata /
Acock, Alan C.
A gentle introduction to stata / Alan C. Acock - 4th ed. - Stata press , 2014. - xxiii, 468 p. ill. ;
Includes index
Getting started
1.1 Conventions
1.2 Introduction
1.3 The Stata screen
1.4 Using an existing datiuset
1.5 An example of a short Stata session
1.6 Summary
1.7 Exercises .
Entering data
2.1 Creating a datiuset
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 tlu; data
2.9 Summary
2.10 Exercises
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 .
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 .
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.
Statistics and graphs for two categorical variables
6.1 Relationsliip between categorical variables
6.2 Cross-tabulation
6.3 Clii-squared tost
6.3.1 Degrees of freedom
6.3.2 Probability tal>les .
6.4 Percentages and measures of association
6.5 Odds ratios when dependent variable hiis 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.
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-samj)le 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 .
5 Bivaxiate correlation and regression
8.1 Introduction to bivfiiialc correlation and regression
8.2 Scattorgrains
8.3 Plotting the regression line
8.4 An alternative to producing a scattcrgrain. l^inscattcr
8.5 Correlation
8.6 R.cgiTssion .
8.7 Speannan's rho: Riiiik-order correlation for ordinal data .
8.8 Summary
8.9 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 noiiparainetric alternative to ANOVA
9.5 Analysis of covariancc
9.6 Two-way ANOVA
9.7 Repeated-measurers design
9.8 Intraclass correlation measuring agreement .
9.9 Power analysis witli ANOVA
9.9.1 One-way ANOVA
Power analysis for two-way ANOVA
9.9.2 Power analysis for rei)eated-m(ra.sure.s ANOVA .
9.9.3 Summary of power analysis for ANOVA
9.10 S\imniary
9.11 Exercises.
10 Multiple regression
10.1 Introduction to multiple regression
10.2 What is multiple r(?gressin?
10.3 The imsic mult iple regression eoiiimaiid
10.4 Incrcinont in Il-scinarcd: Soniipartial correlatioiivS
10.5 Is the (lepcncleiit variabk.' nonnally distributed? .
10.6 Are the residuals nonnally distributed? .
10.7 Regression diagnostic statistics
10.7.1 Outliers and influential Cciaes
10.7.2 Influent ial ob.servations: DFbcta
10.7.3 Combinations of variables may cause proPieius
10.8 Weighted data .
10.9 Categorical predictors and hierarchical regression
10.10 A sliortcut for working with a categorical variable
10.11 Fundamentals of interaction
10.12 NonliiK^ar relations
10.12.1 Fitting a quadratic model
10.12.2 Centering when using a quadratic term .
10.12.3 Do we iukhI to add a quadratic component? .
10.13 Power analysis in multii)]e regression
10.14 Summary
10.15 Exer(;ises
11 Logistic regression
11.1 Introduct ion to logistic regn^ssion
11.2 An exam})le
11.3 What is an odds ratio and a logit?
11.3.1 The odds ratio
11.3.2 Tlie logit transformation
11.4 Data u.sed in the rest of the chapter
11.5 Logist ic regression
ll.G Hypotiiesis testing
ll.G.l Testing individual coeflicients
11.6.2 Test ing sets of coeflicients
11.7 More on interpret ing results from logistic regres.sioii
11.8 Ni'.stc(l logistic rcgrc.ssioiis
11.9 Power analysis when doing logist ic regression
11.10 Snnnnary
11.11 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 Ecini valence
12.3.3 Split-half and alpha reliability— internal consistency
12.3.4 Ktider Richardson reliability for dichotomous items .
12.3.5 Rater agreement—kappa (k)
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 Oblicine rotation: Promax
12.7 But we wanted one .scale, not four scales
12.7.1 Scoring our variabl(>
12.8 Summary
12.9 Exercises.
13 Working with missing values—multiple imputation
1.1.1 The nature of the problem
13.2 Multiple imputation and its a.s.sumptions about the mechani.sm 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 B? and standardized /3s
13.5.5 When impossible values are imputed
13.6 Summary
13.7 Exercises .
14 The sem and gsem commands
14.1 Ordinary least-squares regression models using sem
14.1.1 Using the SEM Builder to fit a basic regression model
14.2 A quick way to draw a regression model and a fresh start
14.2.1 Using sem without the SEM Builder
14.3 The gsem command for logistic regression
14.3.1 Fitting the model using the logit command
14.3.2 Fitting the model using the gsem command
14.4 Path analysis and mediation
14.5 Conclusions and what is next for the sem command .
14.6 Exercises
A What's next?
A.l 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
9781597181426 (pb)
Computer Programming
005.55 / ACO/G
A gentle introduction to stata / Alan C. Acock - 4th ed. - Stata press , 2014. - xxiii, 468 p. ill. ;
Includes index
Getting started
1.1 Conventions
1.2 Introduction
1.3 The Stata screen
1.4 Using an existing datiuset
1.5 An example of a short Stata session
1.6 Summary
1.7 Exercises .
Entering data
2.1 Creating a datiuset
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 tlu; data
2.9 Summary
2.10 Exercises
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 .
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 .
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.
Statistics and graphs for two categorical variables
6.1 Relationsliip between categorical variables
6.2 Cross-tabulation
6.3 Clii-squared tost
6.3.1 Degrees of freedom
6.3.2 Probability tal>les .
6.4 Percentages and measures of association
6.5 Odds ratios when dependent variable hiis 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.
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-samj)le 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 .
5 Bivaxiate correlation and regression
8.1 Introduction to bivfiiialc correlation and regression
8.2 Scattorgrains
8.3 Plotting the regression line
8.4 An alternative to producing a scattcrgrain. l^inscattcr
8.5 Correlation
8.6 R.cgiTssion .
8.7 Speannan's rho: Riiiik-order correlation for ordinal data .
8.8 Summary
8.9 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 noiiparainetric alternative to ANOVA
9.5 Analysis of covariancc
9.6 Two-way ANOVA
9.7 Repeated-measurers design
9.8 Intraclass correlation measuring agreement .
9.9 Power analysis witli ANOVA
9.9.1 One-way ANOVA
Power analysis for two-way ANOVA
9.9.2 Power analysis for rei)eated-m(ra.sure.s ANOVA .
9.9.3 Summary of power analysis for ANOVA
9.10 S\imniary
9.11 Exercises.
10 Multiple regression
10.1 Introduction to multiple regression
10.2 What is multiple r(?gressin?
10.3 The imsic mult iple regression eoiiimaiid
10.4 Incrcinont in Il-scinarcd: Soniipartial correlatioiivS
10.5 Is the (lepcncleiit variabk.' nonnally distributed? .
10.6 Are the residuals nonnally distributed? .
10.7 Regression diagnostic statistics
10.7.1 Outliers and influential Cciaes
10.7.2 Influent ial ob.servations: DFbcta
10.7.3 Combinations of variables may cause proPieius
10.8 Weighted data .
10.9 Categorical predictors and hierarchical regression
10.10 A sliortcut for working with a categorical variable
10.11 Fundamentals of interaction
10.12 NonliiK^ar relations
10.12.1 Fitting a quadratic model
10.12.2 Centering when using a quadratic term .
10.12.3 Do we iukhI to add a quadratic component? .
10.13 Power analysis in multii)]e regression
10.14 Summary
10.15 Exer(;ises
11 Logistic regression
11.1 Introduct ion to logistic regn^ssion
11.2 An exam})le
11.3 What is an odds ratio and a logit?
11.3.1 The odds ratio
11.3.2 Tlie logit transformation
11.4 Data u.sed in the rest of the chapter
11.5 Logist ic regression
ll.G Hypotiiesis testing
ll.G.l Testing individual coeflicients
11.6.2 Test ing sets of coeflicients
11.7 More on interpret ing results from logistic regres.sioii
11.8 Ni'.stc(l logistic rcgrc.ssioiis
11.9 Power analysis when doing logist ic regression
11.10 Snnnnary
11.11 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 Ecini valence
12.3.3 Split-half and alpha reliability— internal consistency
12.3.4 Ktider Richardson reliability for dichotomous items .
12.3.5 Rater agreement—kappa (k)
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 Oblicine rotation: Promax
12.7 But we wanted one .scale, not four scales
12.7.1 Scoring our variabl(>
12.8 Summary
12.9 Exercises.
13 Working with missing values—multiple imputation
1.1.1 The nature of the problem
13.2 Multiple imputation and its a.s.sumptions about the mechani.sm 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 B? and standardized /3s
13.5.5 When impossible values are imputed
13.6 Summary
13.7 Exercises .
14 The sem and gsem commands
14.1 Ordinary least-squares regression models using sem
14.1.1 Using the SEM Builder to fit a basic regression model
14.2 A quick way to draw a regression model and a fresh start
14.2.1 Using sem without the SEM Builder
14.3 The gsem command for logistic regression
14.3.1 Fitting the model using the logit command
14.3.2 Fitting the model using the gsem command
14.4 Path analysis and mediation
14.5 Conclusions and what is next for the sem command .
14.6 Exercises
A What's next?
A.l 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
9781597181426 (pb)
Computer Programming
005.55 / ACO/G