Stata for the Behavioral Sciences

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Author: Michael N. Mitchell
Publisher: Stata Press
Copyright: 2015
ISBN-13: 978-1-59718-173-0
Pages: 646; 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.


Michael Mitchell is a senior statistician working in the area of sleep research as well as the prevention of child maltreatment. He is the author of A Visual Guide to Stata GraphicsData Management Using Stata, and Interpreting and Visualizing Regression Models Using Stata. Previously, he worked for 12 years as a statistical consultant and manager of the UCLA ATS Statistical Consulting Group. There he envisioned the UCLA Statistical Consulting Resources website and wrote hundreds of webpages about Stata.

List of tables
List of figures
Preface (PDF)
I Warming up
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 
23.4 ANOVA 
23.10 DATA LIST 
23.13 DISPLAY 
23.15 FACTOR 
23.16 FILTER 
23.17 FORMATS 
23.19 GET FILE 
23.23 MEANS 
23.25 MIXED 
23.27 NOMREG 
23.28 PLUM 
23.29 PROBIT 
23.30 RECODE 
23.33 SAVE 
23.34 SELECT IF 
23.39 T-TEST 
23.43 Closing thoughts