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Stata for the Behavioral Sciences, by Michael Mitchell, is the ideal reference for researchers using Stata to fit ANOVA models and other models commonly applied to behavioral science data. Drawing on his education in psychology and his experience in consulting, Mitchell uses terminology and examples familiar to the reader as he demonstrates how to fit a variety of models, how to interpret results, how to understand simple and interaction effects, and how to explore results graphically.

Author | Michael N. Mitchell |
---|---|

ISBN 13 | 978-1-59718-173-0 |

Pages | 646 |

Copyright | 2015 |

Book type | Paperback |

Stata for the Behavioral Sciences, by Michael Mitchell, is the ideal reference for researchers using Stata to fit ANOVA models and other models commonly applied to behavioral science data. Drawing on his education in psychology and his experience in consulting, Mitchell uses terminology and examples familiar to the reader as he demonstrates how to fit a variety of models, how to interpret results, how to understand simple and interaction effects, and how to explore results graphically.

Although this book is not designed as an introduction to Stata, it is appealing even to Stata novices. Throughout the text, Mitchell thoughtfully addresses any features of Stata that are important to understand for the analysis at hand. He also is careful to point out additional resources such as related videos from Stata's YouTube channel.

The book is divided into five sections.

The first section contains a chapter that introduces Stata commands for descriptive statistics and another that covers basic inferential statistics such as one- and two-sample t tests.

The second section focuses on between-subjects ANOVA modeling. The discussion moves from one-way ANOVA models to ANCOVA models to two-way and three-way ANOVA models. In each case, special attention is given to the use of commands such as contrast and margins for testing specific hypotheses of interest. Mitchell also emphasizes the understanding of interactions through contrasts and graphs. Underscoring the importance of planning any experiment, he discusses power analysis for t tests, for one- and two-way ANOVA models, and for ANCOVA models.

Section three of the book extends the discussion in the previous section to models for repeated-measures data and for longitudinal data.

The fourth section of the book illustrates the use of the regress command for fitting multiple regression models. Mitchell then turns his attention to tools for formatting regression output, for testing assumptions, and for model building. This section ends with a discussion of power analysis for simple, multiple, and nested regression models.

The final section has a tone that differs from the first four. Rather than focusing on a particular type of analysis, Mitchell describes elements of Stata. He first discusses estimation commands and similarities in syntax from command to command. Then, he details a set of postestimation commands that are available after most estimation commands. Another chapter provides an overview of data management commands. This section ends with a chapter that will be of particular interest to anyone who has used IBM® SPSS®; it lists commonly used SPSS® commands and provides equivalent Stata syntax.

This book is an easy-to-follow guide to analyzing data using Stata for researchers in the behavioral sciences and a valuable addition to the bookshelf of anyone interested in applying ANOVA methods to a variety of experimental designs.

Although this book is not designed as an introduction to Stata, it is appealing even to Stata novices. Throughout the text, Mitchell thoughtfully addresses any features of Stata that are important to understand for the analysis at hand. He also is careful to point out additional resources such as related videos from Stata's YouTube channel.

The book is divided into five sections.

The first section contains a chapter that introduces Stata commands for descriptive statistics and another that covers basic inferential statistics such as one- and two-sample t tests.

The second section focuses on between-subjects ANOVA modeling. The discussion moves from one-way ANOVA models to ANCOVA models to two-way and three-way ANOVA models. In each case, special attention is given to the use of commands such as contrast and margins for testing specific hypotheses of interest. Mitchell also emphasizes the understanding of interactions through contrasts and graphs. Underscoring the importance of planning any experiment, he discusses power analysis for t tests, for one- and two-way ANOVA models, and for ANCOVA models.

Section three of the book extends the discussion in the previous section to models for repeated-measures data and for longitudinal data.

The fourth section of the book illustrates the use of the regress command for fitting multiple regression models. Mitchell then turns his attention to tools for formatting regression output, for testing assumptions, and for model building. This section ends with a discussion of power analysis for simple, multiple, and nested regression models.

The final section has a tone that differs from the first four. Rather than focusing on a particular type of analysis, Mitchell describes elements of Stata. He first discusses estimation commands and similarities in syntax from command to command. Then, he details a set of postestimation commands that are available after most estimation commands. Another chapter provides an overview of data management commands. This section ends with a chapter that will be of particular interest to anyone who has used IBM® SPSS®; it lists commonly used SPSS® commands and provides equivalent Stata syntax.

This book is an easy-to-follow guide to analyzing data using Stata for researchers in the behavioral sciences and a valuable addition to the bookshelf of anyone interested in applying ANOVA methods to a variety of experimental designs.

1 Introduction

1.1 Read me first!

1.1.1 Downloading the example datasets and programs

1.1.2 Other user-written programs

The fre command

The esttab command

The extremes command

1.2 Why use Stata?

1.2.1 ANOVA

1.2.2 Supercharging your ANOVA

1.2.3 Stata is economical

1.2.4 Statistical powerhouse

1.2.5 Easy to learn

1.2.6 Simple and powerful data management

1.2.7 Access to user-written programs

1.2.8 Point and click or commands: Your choice

1.2.9 Powerful yet simple

1.2.10 Access to Stata source code

1.2.11 Online resources for learning Stata 1.2.12 And yet there is more!

1.3 Overview of the book

1.3.1 Part I: Warming up

1.3.2 Part II: Between-subjects ANOVA models

1.3.3 Part III: Repeated measures and longitudinal models

1.3.4 Part IV: Regression models

1.3.5 Part V: Stata overview

1.3.6 The GSS dataset

1.3.7 Language used in the book

1.3.8 Online resources for this book

1.4 Recommended resources and books

1.4.1 Getting started

1.4.2 Data management in Stata

1.4.3 Reproducing your results

1.4.4 Recommended Stata Press books

2 Descriptive statistics

2.1 Chapter overview

2.2 Using and describing the GSS dataset

2.3 One-way tabulations

2.4 Summary statistics

2.5 Summary statistics by one group

2.6 Two-way tabulations

2.7 Cross-tabulations with summary statistics

2.8 Closing thoughts

3 Basic inferential statistics

3.1 Chapter overview

3.2 Two-sample t tests

3.3 Paired sample t tests

3.4 One-sample t tests

3.5 Two-sample test of proportions

3.6 One-sample test of proportions

3.7 Chi-squared and Fisher's exact test

3.8 Correlations

3.9 Immediate commands

3.9.1 Immediate test of two means

3.9.2 Immediate test of one mean

3.9.3 Immediate test of two proportions 3.9.4 Immediate test of one proportion

3.9.5 Immediate cross-tabulations

3.10 Closing thoughts

II Between-subjects ANOVA models

4 One-way between-subjects ANOVA

4.1 Chapter overview

4.2 Comparing two groups using a t test

4.3 Comparing two groups using ANOVA

4.3.1 Computing effect sizes

4.4 Comparing three groups using ANOVA

4.4.1 Testing planned comparisons using contrast

4.4.2 Computing effect sizes for planned comparisons

4.5 Estimation commands and postestimation commands

4.6 Interpreting confidence intervals

4.7 Closing thoughts

5 Contrasts for a one-way ANOVA

5.1 Chapter overview

5.2 Introducing contrasts

5.2.1 Computing and graphing means

5.2.2 Making contrasts among means

5.2.3 Graphing contrasts

5.2.4 Options with the margins and contrast commands

5.2.5 Computing effect sizes for contrasts

5.2.6 Summary

5.3 Overview of contrast operators

5.4 Compare each group against a reference group

5.4.1 Selecting a specific contrast

5.4.2 Selecting a different reference group 5.4.3 Selecting a contrast and reference group

5.5 Compare each group against the grand mean

5.5.1 Selecting a specific contrast

5.6 Compare adjacent means

5.6.1 Reverse adjacent contrasts

5.6.2 Selecting a specific contrast

5.7 Comparing with the mean of subsequent and previous levels

5.7.1 Comparing with the mean of previous levels

5.7.2 Selecting a specific contrast

5.8 Polynomial contrasts

5.9 Custom contrasts

5.10 Weighted contrasts

5.11 Pairwise comparisons

5.12 Closing thoughts

6 Analysis of covariance

6.1 Chapter overview

6.2 Example 1: ANCOVA with an experiment using a pretest

6.3 Example 2: Experiment using covariates

6.4 Example 3: Observational data

6.4.1 Model 1: No covariates

6.4.2 Model 2: Demographics as covariates

6.4.3 Model 3: Demographics, socializing as covariates

6.4.4 Model 4: Demographics, socializing, health as covariates

6.5 Some technical details about adjusted means

6.5.1 Computing adjusted means: Method 1

6.5.2 Computing adjusted means: Method 2

6.5.3 Computing adjusted means: Method 3

6.5.4 Differences between method 2 and method 3 6.5.5 Adjusted means: Summary

6.6 Closing thoughts

7 Two-way factorial between-subjects ANOVA

7.1 Chapter overview

7.2 Two-by-two models: Example 1

7.2.1 Simple effects

7.2.2 Estimating the size of the interaction

7.2.3 More about interaction

7.2.4 Summary

7.3 Two-by-three models

7.3.1 Example 2

Simple effects

Simple contrasts

Partial interaction

Comparing optimism therapy with traditional therapy

7.3.2 Example 3

Simple effects

Partial interactions

7.3.3 Summary

7.4 Three-by-three models: Example 4

7.4.1 Simple effects

7.4.2 Simple contrasts

7.4.3 Partial interaction

7.4.4 Interaction contrasts

7.4.5 Summary

7.5 Unbalanced designs

7.6 Interpreting confidence intervals

7.7 Closing thoughts

8 Analysis of covariance with interactions

8.1 Chapter overview

8.2 Example 1: IV has two levels

8.2.1 Question 1: Treatment by depression interaction

8.2.2 Question 2: When is optimism therapy superior?

8.2.3 Example 1: Summary

8.3 Example 2: IV has three levels

8.3.1 Questions 1a and 1b

Question 1a

Question 1b

8.3.2 Questions 2a and 2b

Question 2a

Question 2b

8.3.3 Overall interaction

8.3.4 Example 2: Summary

8.4 Closing thoughts

9 Three-way between-subjects analysis of variance

9.1 Chapter overview

9.2 Two-by-two-by-two models

9.2.1 Simple interactions by season

9.2.2 Simple interactions by depression status

9.2.3 Simple effects

9.3 Two-by-two-by-three models

9.3.1 Simple interactions by depression status

9.3.2 Simple partial interaction by depression status 9.3.3 Simple contrasts

9.3.4 Partial interactions

9.4 Three-by-three-by-three models and beyond

9.4.1 Partial interactions and interaction contrasts

9.4.2 Simple interactions

9.4.3 Simple effects and simple contrasts

9.5 Closing thoughts

10 Supercharge your analysis of variance (via regression)

10.1 Chapter overview

10.2 Performing ANOVA tests via regression

10.3 Supercharging your ANOVA

10.3.1 Complex surveys

10.3.2 Homogeneity of variance 10.3.3 Robust regression

10.3.4 Quantile regression

10.4 Main effects with interactions: anova versus regress

10.5 Closing thoughts

11 Power analysis for analysis of variance and covariance

11.1 Chapter overview

11.2 Power analysis for a two-sample t test

11.2.1 Example 1: Replicating a two-group comparison

11.2.2 Example 2: Using standardized effect sizes

11.2.3 Estimating effect sizes

11.2.4 Example 3: Power for a medium effect

11.2.5 Example 4: Power for a range of effect sizes 11.2.6 Example 5: For a given N, compute the effect size

11.2.7 Example 6: Compute effect sizes given unequal Ns

11.3 Power analysis for one-way ANOVA

11.3.1 Overview

Hypothesis 1. Traditional therapy versus control

Hypothesis 2: Optimism therapy versus control

Hypothesis 3: Optimism therapy versus traditional therapy Summary of hypotheses

11.3.2 Example 7: Testing hypotheses 1 and 2

11.3.3 Example 8: Testing hypotheses 2 and 3

11.3.4 Summary

11.4 Power analysis for ANCOVA

11.4.1 Example 9: Using pretest as a covariate 11.4.2 Example 10: Using correlated variables as covariates

11.5 Power analysis for two-way ANOVA

11.5.1 Example 11: Replicating a two-by-two analysis

11.5.2 Example 12: Standardized simple effects

11.5.3 Example 13: Standardized interaction effect

11.5.4 Summary: Power for two-way ANOVA

11.6 Closing thoughts

III Repeated measures and longitudinal designs

12 Repeated measures designs

12.1 Chapter overview

12.2 Example 1: One-way within-subjects designs

12.3 Example 2: Mixed design with two groups

12.4 Example 3: Mixed design with three groups

12.5 Comparing models with different residual covariance structures

12.6 Example 1 revisited: Using compound symmetry

12.7 Example 1 revisited again: Using small-sample methods

12.8 An alternative analysis: ANCOVA

12.9 Closing thoughts

13 Longitudinal designs

13.1 Chapter overview

13.2 Example 1: Linear effect of time

13.3 Example 2: Interacting time with a between-subjects IV

13.4 Example 3: Piecewise modeling of time

13.5 Example 4: Piecewise effects of time by a categorical predictor

13.5.1 Baseline slopes

13.5.2 Treatment slopes

13.5.3 Jump at treatment

13.5.4 Comparisons among groups at particular days

13.5.5 Summary of example 4

13.6 Closing thoughts

IV Regression models

14 Simple and multiple regression

14.1 Chapter overview

14.2 Simple linear regression

14.2.1 Decoding the output

14.2.2 Computing predicted means using the margins command

14.2.3 Graphing predicted means using the marginsplot command

14.3 Multiple regression

14.3.1 Describing the predictors

14.3.2 Running the multiple regression model

14.3.3 Computing adjusted means using the margins command

14.3.4 Describing the contribution of a predictor

One-unit change

Multiple-unit change

Milestone change in units

One SD change in predictor

Partial and semipartial correlation

14.4 Testing multiple coefficients

14.4.1 Testing whether coefficients equal zero

14.4.2 Testing the equality of coefficients

14.4.3 Testing linear combinations of coefficients 14.5 Closing thoughts

15 More details about the regress command

15.1 Chapter overview

15.2 Regression options

15.3 Redisplaying results

15.4 Identifying the estimation sample

15.5 Stored results

15.6 Storing results

15.7 Displaying results with the estimates table command

15.8 Closing thoughts

16 Presenting regression results

16.1 Chapter overview

16.2 Presenting a single model

16.3 Presenting multiple models

16.4 Creating regression tables using esttab

16.4.1 Presenting a single model with esttab 16.4.2 Presenting multiple models with esttab

16.4.3 Exporting results to other file formats

16.5 More commands for presenting regression results

16.5.1 outreg

16.5.2 outreg2

16.5.3 xml_tab

16.5.4 coefplot

16.6 Closing thoughts

17 Tools for model building

17.1 Chapter overview 17.2 Fitting multiple models on the same sample

17.3 Nested models

17.3.1 Example 1: A simple example

17.3.2 Example 2: A more realistic example

17.4 Stepwise models

17.5 Closing thoughts

18 Regression diagnostics

18.1 Chapter overview

18.2 Outliers

18.2.1 Standardized residuals

18.2.2 Studentized residuals, leverage, Cook's D

18.2.3 Graphs of residuals, leverage, and Cook's D

18.2.4 DFBETAs and avplots

18.2.5 Running a regression with and without observations

18.3 Nonlinearity

18.3.1 Checking for nonlinearity graphically

18.3.2 Using scatterplots to check for nonlinearity 18.3.3 Checking for nonlinearity using residuals

18.3.4 Checking for nonlinearity using a locally weighted smoother

18.3.5 Graphing an outcome mean at each level of predictor

18.3.6 Summary

18.3.7 Checking for nonlinearity analytically

Adding power terms

Using factor variables

18.4 Multicollinearity

18.5 Homoskedasticity

18.6 Normality of residuals

18.7 Closing thoughts

19 Power analysis for regression

19.1 Chapter overview

19.2 Power for simple regression

19.3 Power for multiple regression

19.4 Power for a nested multiple regression

19.5 Closing thoughts

V Stata overview

20 Common features of estimation commands

20.1 Chapter overview

20.2 Common syntax

20.3 Analysis using subsamples

20.4 Robust standard errors

20.5 Prefix commands

20.5.1 The by: prefix

20.5.2 The nestreg: prefix

20.5.3 The stepwise: prefix

20.5.4 The svy: prefix

20.5.5 The mi estimate: prefix

20.6 Setting confidence levels

20.7 Postestimation commands

20.8 Closing thoughts

21 Postestimation commands

21.1 Chapter overview

21.2 The contrast command

21.3 The margins command

21.3.1 The at() option

21.3.2 Margins with factor variables

21.3.3 Margins with factor variables and the at() option

21.3.4 The dydx() option

21.4 The marginsplot command

21.5 The pwcompare command

21.6 Closing thoughts

22 Stata data management commands

22.1 Chapter overview

22.2 Reading data into Stata 22.2.1 Reading Stata datasets

22.2.2 Reading Excel workbooks

22.2.3 Reading comma-separated files

22.2.4 Reading other file formats

22.3 Saving data

22.4 Labeling data

22.4.1 Variable labels

22.4.2 A looping trick

22.4.3 Value labels

22.5 Creating and recoding variables

22.5.1 Creating new variables with generate

22.5.2 Modifying existing variables with replace

22.5.3 Extensions to generate egen

22.5.4 Recode

22.6 Keeping and dropping variables

22.7 Keeping and dropping observations

22.8 Combining datasets

22.8.1 Appending datasets 22.8.2 Merging datasets

22.9 Reshaping datasets

22.9.1 Reshaping datasets wide to long

22.9.2 Reshaping datasets long to wide

22.10 Closing thoughts

23 Stata equivalents of common IBM SPSS Commands

23.1 Chapter overview

23.2 ADD FILES

23.3 AGGREGATE

23.4 ANOVA

23.5 AUTORECODE

23.6 CASESTOVARS

23.7 COMPUTE

23.8 CORRELATIONS

23.9 CROSSTABS

23.10 DATA LIST

23.11 DELETE VARIABLES 23.12 DESCRIPTIVES

23.13 DISPLAY

23.14 DOCUMENT

23.15 FACTOR

23.16 FILTER

23.17 FORMATS

23.18 FREQUENCIES

23.19 GET FILE

23.20 GET TRANSLATE

23.21 LOGISTIC REGRESSION

23.22 MATCH FILES

23.23 MEANS

23.24 MISSING VALUES

23.25 MIXED

23.26 MULTIPLE IMPUTATION

23.27 NOMREG

23.28 PLUM

23.29 PROBIT

23.30 RECODE

23.31 RELIABILITY

23.32 RENAME VARIABLES

23.33 SAVE

23.34 SELECT IF

23.35 SAVE TRANSLATE

23.36 SORT CASES

23.37 SORT VARIABLES

23.38 SUMMARIZE

23.39 T-TEST

23.40 VALUE LABELS

23.41 VARIABLE LABELS

23.42 VARSTOCASES

23.43 Closing thoughts

1.1 Read me first!

1.1.1 Downloading the example datasets and programs

1.1.2 Other user-written programs

The fre command

The esttab command

The extremes command

1.2 Why use Stata?

1.2.1 ANOVA

1.2.2 Supercharging your ANOVA

1.2.3 Stata is economical

1.2.4 Statistical powerhouse

1.2.5 Easy to learn

1.2.6 Simple and powerful data management

1.2.7 Access to user-written programs

1.2.8 Point and click or commands: Your choice

1.2.9 Powerful yet simple

1.2.10 Access to Stata source code

1.2.11 Online resources for learning Stata 1.2.12 And yet there is more!

1.3 Overview of the book

1.3.1 Part I: Warming up

1.3.2 Part II: Between-subjects ANOVA models

1.3.3 Part III: Repeated measures and longitudinal models

1.3.4 Part IV: Regression models

1.3.5 Part V: Stata overview

1.3.6 The GSS dataset

1.3.7 Language used in the book

1.3.8 Online resources for this book

1.4 Recommended resources and books

1.4.1 Getting started

1.4.2 Data management in Stata

1.4.3 Reproducing your results

1.4.4 Recommended Stata Press books

2 Descriptive statistics

2.1 Chapter overview

2.2 Using and describing the GSS dataset

2.3 One-way tabulations

2.4 Summary statistics

2.5 Summary statistics by one group

2.6 Two-way tabulations

2.7 Cross-tabulations with summary statistics

2.8 Closing thoughts

3 Basic inferential statistics

3.1 Chapter overview

3.2 Two-sample t tests

3.3 Paired sample t tests

3.4 One-sample t tests

3.5 Two-sample test of proportions

3.6 One-sample test of proportions

3.7 Chi-squared and Fisher's exact test

3.8 Correlations

3.9 Immediate commands

3.9.1 Immediate test of two means

3.9.2 Immediate test of one mean

3.9.3 Immediate test of two proportions 3.9.4 Immediate test of one proportion

3.9.5 Immediate cross-tabulations

3.10 Closing thoughts

II Between-subjects ANOVA models

4 One-way between-subjects ANOVA

4.1 Chapter overview

4.2 Comparing two groups using a t test

4.3 Comparing two groups using ANOVA

4.3.1 Computing effect sizes

4.4 Comparing three groups using ANOVA

4.4.1 Testing planned comparisons using contrast

4.4.2 Computing effect sizes for planned comparisons

4.5 Estimation commands and postestimation commands

4.6 Interpreting confidence intervals

4.7 Closing thoughts

5 Contrasts for a one-way ANOVA

5.1 Chapter overview

5.2 Introducing contrasts

5.2.1 Computing and graphing means

5.2.2 Making contrasts among means

5.2.3 Graphing contrasts

5.2.4 Options with the margins and contrast commands

5.2.5 Computing effect sizes for contrasts

5.2.6 Summary

5.3 Overview of contrast operators

5.4 Compare each group against a reference group

5.4.1 Selecting a specific contrast

5.4.2 Selecting a different reference group 5.4.3 Selecting a contrast and reference group

5.5 Compare each group against the grand mean

5.5.1 Selecting a specific contrast

5.6 Compare adjacent means

5.6.1 Reverse adjacent contrasts

5.6.2 Selecting a specific contrast

5.7 Comparing with the mean of subsequent and previous levels

5.7.1 Comparing with the mean of previous levels

5.7.2 Selecting a specific contrast

5.8 Polynomial contrasts

5.9 Custom contrasts

5.10 Weighted contrasts

5.11 Pairwise comparisons

5.12 Closing thoughts

6 Analysis of covariance

6.1 Chapter overview

6.2 Example 1: ANCOVA with an experiment using a pretest

6.3 Example 2: Experiment using covariates

6.4 Example 3: Observational data

6.4.1 Model 1: No covariates

6.4.2 Model 2: Demographics as covariates

6.4.3 Model 3: Demographics, socializing as covariates

6.4.4 Model 4: Demographics, socializing, health as covariates

6.5 Some technical details about adjusted means

6.5.1 Computing adjusted means: Method 1

6.5.2 Computing adjusted means: Method 2

6.5.3 Computing adjusted means: Method 3

6.5.4 Differences between method 2 and method 3 6.5.5 Adjusted means: Summary

6.6 Closing thoughts

7 Two-way factorial between-subjects ANOVA

7.1 Chapter overview

7.2 Two-by-two models: Example 1

7.2.1 Simple effects

7.2.2 Estimating the size of the interaction

7.2.3 More about interaction

7.2.4 Summary

7.3 Two-by-three models

7.3.1 Example 2

Simple effects

Simple contrasts

Partial interaction

Comparing optimism therapy with traditional therapy

7.3.2 Example 3

Simple effects

Partial interactions

7.3.3 Summary

7.4 Three-by-three models: Example 4

7.4.1 Simple effects

7.4.2 Simple contrasts

7.4.3 Partial interaction

7.4.4 Interaction contrasts

7.4.5 Summary

7.5 Unbalanced designs

7.6 Interpreting confidence intervals

7.7 Closing thoughts

8 Analysis of covariance with interactions

8.1 Chapter overview

8.2 Example 1: IV has two levels

8.2.1 Question 1: Treatment by depression interaction

8.2.2 Question 2: When is optimism therapy superior?

8.2.3 Example 1: Summary

8.3 Example 2: IV has three levels

8.3.1 Questions 1a and 1b

Question 1a

Question 1b

8.3.2 Questions 2a and 2b

Question 2a

Question 2b

8.3.3 Overall interaction

8.3.4 Example 2: Summary

8.4 Closing thoughts

9 Three-way between-subjects analysis of variance

9.1 Chapter overview

9.2 Two-by-two-by-two models

9.2.1 Simple interactions by season

9.2.2 Simple interactions by depression status

9.2.3 Simple effects

9.3 Two-by-two-by-three models

9.3.1 Simple interactions by depression status

9.3.2 Simple partial interaction by depression status 9.3.3 Simple contrasts

9.3.4 Partial interactions

9.4 Three-by-three-by-three models and beyond

9.4.1 Partial interactions and interaction contrasts

9.4.2 Simple interactions

9.4.3 Simple effects and simple contrasts

9.5 Closing thoughts

10 Supercharge your analysis of variance (via regression)

10.1 Chapter overview

10.2 Performing ANOVA tests via regression

10.3 Supercharging your ANOVA

10.3.1 Complex surveys

10.3.2 Homogeneity of variance 10.3.3 Robust regression

10.3.4 Quantile regression

10.4 Main effects with interactions: anova versus regress

10.5 Closing thoughts

11 Power analysis for analysis of variance and covariance

11.1 Chapter overview

11.2 Power analysis for a two-sample t test

11.2.1 Example 1: Replicating a two-group comparison

11.2.2 Example 2: Using standardized effect sizes

11.2.3 Estimating effect sizes

11.2.4 Example 3: Power for a medium effect

11.2.5 Example 4: Power for a range of effect sizes 11.2.6 Example 5: For a given N, compute the effect size

11.2.7 Example 6: Compute effect sizes given unequal Ns

11.3 Power analysis for one-way ANOVA

11.3.1 Overview

Hypothesis 1. Traditional therapy versus control

Hypothesis 2: Optimism therapy versus control

Hypothesis 3: Optimism therapy versus traditional therapy Summary of hypotheses

11.3.2 Example 7: Testing hypotheses 1 and 2

11.3.3 Example 8: Testing hypotheses 2 and 3

11.3.4 Summary

11.4 Power analysis for ANCOVA

11.4.1 Example 9: Using pretest as a covariate 11.4.2 Example 10: Using correlated variables as covariates

11.5 Power analysis for two-way ANOVA

11.5.1 Example 11: Replicating a two-by-two analysis

11.5.2 Example 12: Standardized simple effects

11.5.3 Example 13: Standardized interaction effect

11.5.4 Summary: Power for two-way ANOVA

11.6 Closing thoughts

III Repeated measures and longitudinal designs

12 Repeated measures designs

12.1 Chapter overview

12.2 Example 1: One-way within-subjects designs

12.3 Example 2: Mixed design with two groups

12.4 Example 3: Mixed design with three groups

12.5 Comparing models with different residual covariance structures

12.6 Example 1 revisited: Using compound symmetry

12.7 Example 1 revisited again: Using small-sample methods

12.8 An alternative analysis: ANCOVA

12.9 Closing thoughts

13 Longitudinal designs

13.1 Chapter overview

13.2 Example 1: Linear effect of time

13.3 Example 2: Interacting time with a between-subjects IV

13.4 Example 3: Piecewise modeling of time

13.5 Example 4: Piecewise effects of time by a categorical predictor

13.5.1 Baseline slopes

13.5.2 Treatment slopes

13.5.3 Jump at treatment

13.5.4 Comparisons among groups at particular days

13.5.5 Summary of example 4

13.6 Closing thoughts

IV Regression models

14 Simple and multiple regression

14.1 Chapter overview

14.2 Simple linear regression

14.2.1 Decoding the output

14.2.2 Computing predicted means using the margins command

14.2.3 Graphing predicted means using the marginsplot command

14.3 Multiple regression

14.3.1 Describing the predictors

14.3.2 Running the multiple regression model

14.3.3 Computing adjusted means using the margins command

14.3.4 Describing the contribution of a predictor

One-unit change

Multiple-unit change

Milestone change in units

One SD change in predictor

Partial and semipartial correlation

14.4 Testing multiple coefficients

14.4.1 Testing whether coefficients equal zero

14.4.2 Testing the equality of coefficients

14.4.3 Testing linear combinations of coefficients 14.5 Closing thoughts

15 More details about the regress command

15.1 Chapter overview

15.2 Regression options

15.3 Redisplaying results

15.4 Identifying the estimation sample

15.5 Stored results

15.6 Storing results

15.7 Displaying results with the estimates table command

15.8 Closing thoughts

16 Presenting regression results

16.1 Chapter overview

16.2 Presenting a single model

16.3 Presenting multiple models

16.4 Creating regression tables using esttab

16.4.1 Presenting a single model with esttab 16.4.2 Presenting multiple models with esttab

16.4.3 Exporting results to other file formats

16.5 More commands for presenting regression results

16.5.1 outreg

16.5.2 outreg2

16.5.3 xml_tab

16.5.4 coefplot

16.6 Closing thoughts

17 Tools for model building

17.1 Chapter overview 17.2 Fitting multiple models on the same sample

17.3 Nested models

17.3.1 Example 1: A simple example

17.3.2 Example 2: A more realistic example

17.4 Stepwise models

17.5 Closing thoughts

18 Regression diagnostics

18.1 Chapter overview

18.2 Outliers

18.2.1 Standardized residuals

18.2.2 Studentized residuals, leverage, Cook's D

18.2.3 Graphs of residuals, leverage, and Cook's D

18.2.4 DFBETAs and avplots

18.2.5 Running a regression with and without observations

18.3 Nonlinearity

18.3.1 Checking for nonlinearity graphically

18.3.2 Using scatterplots to check for nonlinearity 18.3.3 Checking for nonlinearity using residuals

18.3.4 Checking for nonlinearity using a locally weighted smoother

18.3.5 Graphing an outcome mean at each level of predictor

18.3.6 Summary

18.3.7 Checking for nonlinearity analytically

Adding power terms

Using factor variables

18.4 Multicollinearity

18.5 Homoskedasticity

18.6 Normality of residuals

18.7 Closing thoughts

19 Power analysis for regression

19.1 Chapter overview

19.2 Power for simple regression

19.3 Power for multiple regression

19.4 Power for a nested multiple regression

19.5 Closing thoughts

V Stata overview

20 Common features of estimation commands

20.1 Chapter overview

20.2 Common syntax

20.3 Analysis using subsamples

20.4 Robust standard errors

20.5 Prefix commands

20.5.1 The by: prefix

20.5.2 The nestreg: prefix

20.5.3 The stepwise: prefix

20.5.4 The svy: prefix

20.5.5 The mi estimate: prefix

20.6 Setting confidence levels

20.7 Postestimation commands

20.8 Closing thoughts

21 Postestimation commands

21.1 Chapter overview

21.2 The contrast command

21.3 The margins command

21.3.1 The at() option

21.3.2 Margins with factor variables

21.3.3 Margins with factor variables and the at() option

21.3.4 The dydx() option

21.4 The marginsplot command

21.5 The pwcompare command

21.6 Closing thoughts

22 Stata data management commands

22.1 Chapter overview

22.2 Reading data into Stata 22.2.1 Reading Stata datasets

22.2.2 Reading Excel workbooks

22.2.3 Reading comma-separated files

22.2.4 Reading other file formats

22.3 Saving data

22.4 Labeling data

22.4.1 Variable labels

22.4.2 A looping trick

22.4.3 Value labels

22.5 Creating and recoding variables

22.5.1 Creating new variables with generate

22.5.2 Modifying existing variables with replace

22.5.3 Extensions to generate egen

22.5.4 Recode

22.6 Keeping and dropping variables

22.7 Keeping and dropping observations

22.8 Combining datasets

22.8.1 Appending datasets 22.8.2 Merging datasets

22.9 Reshaping datasets

22.9.1 Reshaping datasets wide to long

22.9.2 Reshaping datasets long to wide

22.10 Closing thoughts

23 Stata equivalents of common IBM SPSS Commands

23.1 Chapter overview

23.2 ADD FILES

23.3 AGGREGATE

23.4 ANOVA

23.5 AUTORECODE

23.6 CASESTOVARS

23.7 COMPUTE

23.8 CORRELATIONS

23.9 CROSSTABS

23.10 DATA LIST

23.11 DELETE VARIABLES 23.12 DESCRIPTIVES

23.13 DISPLAY

23.14 DOCUMENT

23.15 FACTOR

23.16 FILTER

23.17 FORMATS

23.18 FREQUENCIES

23.19 GET FILE

23.20 GET TRANSLATE

23.21 LOGISTIC REGRESSION

23.22 MATCH FILES

23.23 MEANS

23.24 MISSING VALUES

23.25 MIXED

23.26 MULTIPLE IMPUTATION

23.27 NOMREG

23.28 PLUM

23.29 PROBIT

23.30 RECODE

23.31 RELIABILITY

23.32 RENAME VARIABLES

23.33 SAVE

23.34 SELECT IF

23.35 SAVE TRANSLATE

23.36 SORT CASES

23.37 SORT VARIABLES

23.38 SUMMARIZE

23.39 T-TEST

23.40 VALUE LABELS

23.41 VARIABLE LABELS

23.42 VARSTOCASES

23.43 Closing thoughts

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