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.
Acknowledgments
List of tables
List of figures
Preface
I WARMING UP
1. INTRODUCTION
Read me first!
Downloading the example datasets and programs
Other user-written programs
The fre command
The esttab command
The extremes command
Why use Stata?
ANOVA
Supercharging your ANOVA
Stata is economical
Statistical powerhouse
Easy to learn
Simple and powerful data management
Access to user-written programs
Point and click or commands: Your choice
Powerful yet simple
Access to Stata source code
Online resources for learning Stata
And yet there is more!
Overview of the book
Part I: Warming up
Part II: Between-subjects ANOVA models
Part III: Repeated measures and longitudinal models
Part IV: Regression models
Part V: Stata overview
The GSS dataset
Language used in the book
Online resources for this book
Recommended resources and books
Getting started
Data management in Stata
Reproducing your results
Recommended Stata Press books
2. DESCRIPTIVE STATISTICS
Chapter overview
Using and describing the GSS dataset
One-way tabulations
Summary statistics
Summary statistics by one group
Two-way tabulations
Cross-tabulations with summary statistics
Closing thoughts
3. BASIC INFERENTIAL STATISTICS
Chapter overview
Two-sample t tests
Paired sample t tests
One-sample t tests
Two-sample test of proportions
One-sample test of proportions
Chi-squared and Fisher’s exact test
Correlations
Immediate commands
Immediate test of two means
Immediate test of one mean
Immediate test of two proportions
Immediate test of one proportion
Immediate cross-tabulations
Closing thoughts
II BETWEEN-SUBJECTS ANOVA MODELS
4. ONE-WAY BETWEEN-SUBJECTS ANOVA
Chapter overview
Comparing two groups using a t test
Comparing two groups using ANOVA
Computing effect sizes
Comparing three groups using ANOVA
Testing planned comparisons using contrast
Computing effect sizes for planned comparisons
Estimation commands and postestimation commands
Interpreting confidence intervals
Closing thoughts
5. CONTRASTS FOR A ONE-WAY ANOVA
Chapter overview
Introducing contrasts
Computing and graphing means
Making contrasts among means
Graphing contrasts
Options with the margins and contrast commands
Computing effect sizes for contrasts
Summary
Overview of contrast operators
Compare each group against a reference group
Selecting a specific contrast
Selecting a different reference group
Selecting a contrast and reference group
Compare each group against the grand mean
Selecting a specific contrast
Compare adjacent means
Reverse adjacent contrasts
Selecting a specific contrast
Comparing with the mean of subsequent and previous levels
Comparing with the mean of previous levels
Selecting a specific contrast
Polynomial contrasts
Custom contrasts
Weighted contrasts
Pairwise comparisons
Closing thoughts
6. ANALYSIS OF COVARIANCE
Chapter overview
Example 1: ANCOVA with an experiment using a pretest
Example 2: Experiment using covariates
Example 3: Observational data
Model 1: No covariates
Model 2: Demographics as covariates
Model 3: Demographics, socializing as covariates
Model 4: Demographics, socializing, health as covariates
Some technical details about adjusted means
Computing adjusted means: Method 1
Computing adjusted means: Method 2
Computing adjusted means: Method 3
Differences between method 2 and method 3
Adjusted means: Summary
Closing thoughts
7. TWO-WAY FACTORIAL BETWEEN-SUBJECTS ANOVA
Chapter overview
Two-by-two models: Example 1
Simple effects
Estimating the size of the interaction
More about interaction
Summary
Two-by-three models
Example 2
Simple effects
Simple contrasts
Partial interaction
Comparing optimism therapy with traditional therapy
Example 3
Simple effects
Partial interactions
Summary
Three-by-three models: Example 4
Simple effects
Simple contrasts
Partial interaction
Interaction contrasts
Summary
Unbalanced designs
Interpreting confidence intervals
Closing thoughts
8. ANALYSIS OF COVARIANCE WITH INTERACTIONS
Chapter overview
Example 1: IV has two levels
Question 1: Treatment by depression interaction
Question 2: When is optimism therapy superior?
Example 1: Summary
Example 2: IV has three levels
Questions 1a and 1b
Question 1a
Question 1b
Questions 2a and 2b
Question 2a
Question 2b
Overall interaction
Example 2: Summary
Closing thoughts
9. THREE-WAY BETWEEN-SUBJECTS ANALYSIS OF VARIANCE
Chapter overview
Two-by-two-by-two models
Simple interactions by season
Simple interactions by depression status
Simple effects
Two-by-two-by-three models
Simple interactions by depression status
Simple partial interaction by depression status
Simple contrasts
Partial interactions
Three-by-three-by-three models and beyond
Partial interactions and interaction contrasts
Simple interactions
Simple effects and simple contrasts
Closing thoughts
10. SUPERCHARGE YOUR ANALYSIS OF VARIANCE (via regression)
Chapter overview
Performing ANOVA tests via regression
Supercharging your ANOVA
Complex surveys
Homogeneity of variance
Robust regression
Quantile regression
Main effects with interactions: anova versus regress
Closing thoughts
11. POWER ANALYSIS FOR ANALYSIS OF VARIANCE AND COVARIANCE
Chapter overview
Power analysis for a two-sample t test
Example 1: Replicating a two-group comparison
Example 2: Using standardized effect sizes
Estimating effect sizes
Example 3: Power for a medium effect
Example 4: Power for a range of effect sizes
Example 5: For a given N, compute the effect size
Example 6: Compute effect sizes given unequal Ns
Power analysis for one-way ANOVA
Overview
Hypothesis 1. Traditional therapy versus control
Hypothesis 2: Optimism therapy versus control
Hypothesis 3: Optimism therapy versus traditional therapy Summary of hypotheses
Example 7: Testing hypotheses 1 and 2
Example 8: Testing hypotheses 2 and 3
Summary
Power analysis for ANCOVA
Example 9: Using pretest as a covariate
Example 10: Using correlated variables as covariates
Power analysis for two-way ANOVA
Example 11: Replicating a two-by-two analysis
Example 12: Standardized simple effects
Example 13: Standardized interaction effect
Summary: Power for two-way ANOVA
Closing thoughts
III REPEATED MEASURES AND LONGITUDINAL DESIGNS
12. REPEATED MEASURES DESIGNS
Chapter overview
Example 1: One-way within-subjects designs
Example 2: Mixed design with two groups
Example 3: Mixed design with three groups
Comparing models with different residual covariance structures
Example 1 revisited: Using compound symmetry
Example 1 revisited again: Using small-sample methods
An alternative analysis: ANCOVA
Closing thoughts
13. LONGITUDINAL DESIGNS
Chapter overview
Example 1: Linear effect of time
Example 2: Interacting time with a between-subjects IV
Example 3: Piecewise modeling of time
Example 4: Piecewise effects of time by a categorical predictor
Baseline slopes
Treatment slopes
Jump at treatment
Comparisons among groups at particular days
Summary of example 4
Closing thoughts
IV REGRESSION MODELS
14. SIMPLE AND MULTIPLE REGRESSION
Chapter overview
Simple linear regression
Decoding the output
Computing predicted means using the margins command
Graphing predicted means using the marginsplot command
Multiple regression
Describing the predictors
Running the multiple regression model
Computing adjusted means using the margins command
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
Testing multiple coefficients
Testing whether coefficients equal zero
Testing the equality of coefficients
Testing linear combinations of coefficients
Closing thoughts
15. MORE DETAILS ABOUT THE REGRESS COMMAND
Chapter overview
Regression options
Redisplaying results
Identifying the estimation sample
Stored results
Storing results
Displaying results with the estimates table command
Closing thoughts
16. PRESENTING REGRESSION RESULTS
Chapter overview
Presenting a single model
Presenting multiple models
Creating regression tables using esttab
Presenting a single model with esttab
Presenting multiple models with esttab
Exporting results to other file formats
More commands for presenting regression results
outreg
outreg2
xml_tab
coefplot
Closing thoughts
17. TOOLS FOR MODEL BUILDING
Chapter overview
Fitting multiple models on the same sample
Nested models
Example 1: A simple example
Example 2: A more realistic example
Stepwise models
Closing thoughts
18. REGRESSION DIAGNOSTICS
Chapter overview
Outliers
Standardized residuals
Studentized residuals, leverage, Cook’s D
Graphs of residuals, leverage, and Cook’s D
DFBETAs and avplots
Running a regression with and without observations
Nonlinearity
Checking for nonlinearity graphically
Using scatterplots to check for nonlinearity
Checking for nonlinearity using residuals
Checking for nonlinearity using a locally weighted smoother
Graphing an outcome mean at each level of predictor
Summary
Checking for nonlinearity analytically
Adding power terms
Using factor variables
Multicollinearity
Homoskedasticity
Normality of residuals
Closing thoughts
19. POWER ANALYSIS FOR REGRESSION
Chapter overview
Power for simple regression
Power for multiple regression
Power for a nested multiple regression
Closing thoughts
V STATA OVERVIEW
20. COMMON FEATURES OF ESTIMATION COMMANDS
Chapter overview
Common syntax
Analysis using subsamples
Robust standard errors
Prefix commands
The by: prefix
The nestreg: prefix
The stepwise: prefix
The svy: prefix
The mi estimate: prefix
Setting confidence levels
Postestimation commands
Closing thoughts
21. POSTESTIMATION COMMANDS
Chapter overview
The contrast command
The margins command
The at() option
Margins with factor variables
Margins with factor variables and the at() option
The dydx() option
The marginsplot command
The pwcompare command
Closing thoughts
22. STATA DATA MANAGEMENT COMMANDS
Chapter overview
Reading data into Stata
Reading Stata datasets
Reading Excel workbooks
Reading comma-separated files
Reading other file formats
Saving data
Labeling data
Variable labels
A looping trick
Value labels
Creating and recoding variables
Creating new variables with generate
Modifying existing variables with replace
Extensions to generate egen
Recode
Keeping and dropping variables
Keeping and dropping observations
Combining datasets
Appending datasets
Merging datasets
Reshaping datasets
Reshaping datasets wide to long
Reshaping datasets long to wide
Closing thoughts
23. STATA EQUIVALENTS OF COMMON IBM SPSS COMMANDS
Chapter overview
ADD FILES
AGGREGATE
ANOVA
AUTORECODE
CASESTOVARS
COMPUTE
CORRELATIONS
CROSSTABS
DATA LIST
DELETE VARIABLES
DESCRIPTIVES
DISPLAY
DOCUMENT
FACTOR
FILTER
FORMATS
FREQUENCIES
GET FILE
GET TRANSLATE
LOGISTIC REGRESSION
MATCH FILES
MEANS
MISSING VALUES
MIXED
MULTIPLE IMPUTATION
NOMREG
PLUM
PROBIT
RECODE
RELIABILITY
RENAME VARIABLES
SAVE
SELECT IF
SAVE TRANSLATE
SORT CASES
SORT VARIABLES
SUMMARIZE
T-TEST
VALUE LABELS
VARIABLE LABELS
VARSTOCASES
Closing thoughts
References
Author index
Subject index
