Dedication
Title
Copyright
Brief Contents
Detailed Contents
Preface
Acknowledgments
About the Author
Chapter 1: A Review of Basic Statistical Concepts
Introduction
How Numbers and Language Revolutionized Human History
Descriptive Statistics
Central Tendency and Dispersion
The Shape of Distributions
Inferential Statistics
Probability Theory
A Study of Cheating
Things That Go Bump in the Light: Factors That Influence the Results of Significance Tests
Alpha Levels and Type I and II Errors
Effect Size and Significance Testing
Measurement Error and Significance Testing
Sample Size and Significance Testing
Restriction of Range and Significance Testing
The Changing State of the Art: Alternate Perspectives on Statistical Hypothesis Testing
Estimates of Effect Size
Meta-Analysis
Summary
Appendix 1.1: Some Common Statistical Tests and Their Uses
Notes
Chapter 2: Descriptive Statistics
The Very Small Survey of Moderately Large Shoe Sizes
Estimating Spending in the U.S. Population
Missing Data and Variable Values
Describing the Ethnic Diversity of U.S. States
Descriptive Statistics in Public Opinion Polls
Shape Matters: The Normal Distribution, Skewness, and Kurtosis
Sometimes Shape Really Matters
How Much Skewness or Kurtosis Is Too Much (or Too Little)?
Correcting for Skewness and Kurtosis
For Further Thought
Chapter 3: Linear and Curvilinear Correlation
Introduction: A Brief Tribute to Karl Pearson
A Hypothetical Study of How Unfair Life Is
A Hypothetical Correlational Study of Afrocentrism
A Study of Freedom of the Press and Perceived Corruption in Europe
The Power of Impossible Outliers
A Look at Brandeis’s Hypothesis Through a Curved Lens
Appendix 3.1: A Primer for Predicting Scores on Y From Scores on X
Chapter 4: Nonparametric Statistics (Tests Involving Nominal Variables)
Introduction: The Correlation Coefficient’s Nominal Cousins
A Pilot Study of Name-Letter Preferences
A Second Pilot Study of Name-Letter Preferences
The Chi-Square Statistic, Phi Coefficients, and Odds Ratios
A Correlational Study of Interpersonal Attraction
A Small Change of Pace: From Marriage to Mental Illness
How to Report the Results of a Chi-Square Analysis of Nominal Variables
Appendix 4.1: How to Report the Results of a Chi-Square Analysis
Notes
Chapter 5: Reliability (and a Little Bit of Factor Analysis)
Chapter Overview
Introduction: The Concept of Reliability
“Just the Factors, Ma’am”
Caveats Regarding Real Data
Principal Components Analysis With Real Data
Checking Out the Eigenvalues
Reliability Analysis
Adding Items Together to Make a Scale
A Comparison of Cronbach’s Alpha and Split-Half Reliability
Applying What You Learned to a Hypothetical Study of Self-Esteem
A Return to Extraversion: Reliability Analysis as a Tool for Item Development
Limitations of Cronbach’s Alpha
Appendix 5.1: Why Psychological Scales Are More Reliable Than the Average of Their Imperfectly Reliable Components
Appendix 5.2: Reporting the Results of a Factor Analysis and a Reliability Analysis
Notes
Chapter 6: Single-Sample and Two-Sample t Tests
Introduction
Bending the Rules About Happiness
Simplifying the Outcome
The Independent Samples (Two-Samples) t Test
Results of the Teacher Expectancy Study
More Simplification
Yet Another Name-Letter Preference Study
An Archival Study of Heat and Aggression
A Blind Cola Taste Test
Appendix 6.1: Reporting the Results of One-Sample and Two-Sample t Tests
Appendix 6.2 Some Useful SPSS Syntax Statements and Logical Operands
Appendix 6.3: Running a One-Sample Chi-Square Test in Older Versions of SPSS (SPSS 19 or Earlier)
Chapter 7: One-Way and Factorial Analysis of Variance (ANOVA)
Introduction: The Trouble With Levels
Understanding One-Way ANOVAs by Experimenting With Alcohol
Finding Meaning in Means: Using Contrasts
Looking at More Than One Independent Variable: Factorial ANOVAs
A Hypothetical Example of When and How “It Depends”
More Practice Understanding Main Effects and Interactions
Practice Study 1: A Lab Study of Aggression Among Kids
Practice Study 2: A Lab Study of Self-Pay
Three-Way ANOVAs and Beyond
Putting It All Together
Appendix 7.1: Results of a Unique Memory Study That Used Planned Contrasts
Chapter 8: Within-Subjects and Mixed Model Analyses
Introduction: Controlling for Individual Differences
Some Bogus Within-Subjects Studies of Bogus Traits
Examining Three Within-Subjects Versions of the Same Study
Combining Between-Subjects and Within-Subjects Designs: Mixed Model Designs
A Repeated Measures Study of Optimism With Countries as the Unit of Analysis
A Mixed Model Study of Implicit Political Attitudes
Appendix 8.1: Sample Results of a Study Using a Mixed Model Design
Chapter 9: Multiple Regression
Introduction: Ceteris Paribus
Predictor Variables and Criterion Variables
The Logic of Multiple Regression Analysis
Considering More Data
Checking Your Answers in SPSS
Correlation, Multiple Regression, and Multiple Predictor Variables
R-Square, Adjusted R-Square, and Standard Errors in Multiple Regression
A Real-World Multiple Regression Application
Logistic Regression: Multiple Regression Analysis With Categorical Criterion Variables
Back to Missing Cookies
Logistic Regression Analysis of Cookie Thefts: Disentangling Bart and Fred
Understanding Odds Ratios in Logistic Regression
Misunderstanding Odds Ratios in Logistic Regression
Back to Missing Cookies
Confidence Intervals in Logistic Regression
It Sure Is Messy Out There: Multivariate Data Cleaning
Appendix 9.1: Terms for Further Reading or Discussion
Chapter 10: Examining Interactions in Multiple Regression Analysis
Introduction: Type of Variable Determines Type of Analysis
Moderators and Interactions in Multiple Regression
A More Realistic Example: Centering and Simple Slopes Tests in Multiple Regression Analysis
Beyond Median Splits: Isolating and Analyzing Subgroups in Multiple Regression
Some Practice With Real Data
More Real Practice Data
Important Moderator Effects Sometimes Add Minimally to R-Square Values
Examining Interactions Between Categorical and Continuous Predictors in Multiple Regression
Why Does This Technique for Estimating Simple Slopes Work?
It’s Not Easy Studying Green: Dealing With Interactions Involving Categorical Predictors With More Than Two Levels
Appendix 10.1: Testing for and Interpreting Three-Way Interactions in Multiple Regression
Appendix 10.2: An Example of How to Report the Results of a Two-Way Interaction in Multiple Regression
Notes
Chapter 11: ANCOVA, Covariate-Adjusted Means, and Predicted Scores
Introduction: Ends to a Mean
The Analysis of Covariance (ANCOVA)
Data Set 1: Gender Differences in Income
The Ghosts in the Machine: Generating Predicted Scores in a Multiple Regression Analysis
Data Set 2: Political Party Affiliation and Attitudes
Data Set 3: A Survey of Smoking and Well-Being
Chapter 12: Suppressor Variables
Introduction: Multiple Regression and Suppression
Uncovering Causes: Attribution Theory and Suppression
A Practice Example of Suppression: Running and Squatting
Practice With Suppression: Three Data Sets to Analyze
Data Set 1: Anagram Difficulty and Self-Pay
Data Set 2: Predicting Voting Behavior
Data Set 3: Predicting Homicide Rates From Country-Level Statistics
A Cautionary Note Regarding Multicollinearity
Coda: Why Suppression?
Note
Chapter 13: Mediation and Path Analysis
Introduction: Disentangling Competing Causes
Third Variables Versus Causal Starting Points
Causal Plausibility
Empirical Plausibility
Moderation in All Things—Except for Mediation
A Mediational Model of How Frustration Leads to Aggression
Formal Testing for the Significance of Mediation Requires Knowledge of Standard Errors
What Mediates the Association Between Self-Esteem and Relationship Satisfaction?
Mediation Analysis as a Specific Case of Path Analysis
The Logic of Path Analysis
A Hypothetical Path Model Involving Positive Beliefs and Health
For Further Reading
Useful Web Pages
Appendix 13.1: An Analysis of Teasing From Kruger, Gordon, and Kuban (2006)
Notes
Chapter 14: Data Cleaning
Introduction: Data Cleaning
Missing Data
That’s Not Normal: Outliers
Identifying and Dealing With Univariate Outliers
Identifying and Dealing With Multivariate Outliers
Putting Your Data-Cleaning Skills to Work
A Final Worry: Multicollinearity
For Further Reading
Appendix 14.1: An Illustration of Multicollinearity
Chapter 15: Data Merging and Data Management 387
Chapter 16: Avoiding Bias: Characterizing Without Capitalizing
Introduction: Some Common Errors and Biases in Human Thinking
Confirmatory Biases + Human Statisticians = Statistical Bias
Phineas and Ferb Are Just the Tip of the Iceberg
Four Simple Rules for Avoiding Bias in Data Analysis
References
Author Index
Subject Index