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Stata for the Behavioral Sciences

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Author Michael N. Mitchell
ISBN 13 978-1-59718-173-0
Pages 646
Copyright 2015
Book type Paperback

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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.

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
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