Table Of Contents:

• Preface
• Acknowledgments
• About the Author
• 1. The New Statistics
• Required Background
• What Is the “New Statistics”?
• Common Misinterpretations of p Values
• Problems With NHST Logic
• Common Misuses of NHST
• The Replication Crisis
• Some Proposed Remedies for Problems With NHST
• Review of Confidence Intervals
• Effect Size
• Brief Introduction to Meta-Analysis
• Recommendations for Better Research and Analysis
• Summary
• 2. Advanced Data Screening: Outliers and Missing Values
• Introduction
• Variable Names and File Management
• Sources of Bias
• Screening Sample Data
• Possible Remedy for Skewness: Nonlinear Data Transformations
• Identification of Outliers
• Handling Outliers
• Testing Linearity Assumptions
• Evaluation of Other Assumptions Specific to Analyses
• Describing Amount of Missing Data
• How Missing Data Arise
• Patterns in Missing Data
• Empirical Example: Detecting Type a Missingness
• Possible Remedies for Missing Data
• Empirical Example: Multiple Imputation to Replace Missing Values
• Data Screening Checklist
• Reporting Guidelines
• Summary
• Appendix 2A: Brief Note About Zero-Inflated Binomial or Poisson Regression
• 3. Statistical Control: What Can Happen When You Add a Third Variable?
• What Is Statistical Control?
• First Research Example: Controlling for a Categorical X2 Variable
• Assumptions for Partial Correlation Between X1 and Y, Controlling for X2
• Notation for Partial Correlation
• Understanding Partial Correlation: Use of Bivariate Regressions to Remove Variance Predictable by X2 From Both X1 and Y
• Partial Correlation Makes No Sense if There Is an X1 × X2 Interaction
• Computation of Partial r From Bivariate Pearson Correlations
• Significance Tests, Confidence Intervals, and Statistical Power for Partial Correlations
• Comparing Outcomes for ry1.2 and ry1
• Introduction to Path Models
• Possible Paths Among X1, Y, and X2
• One Possible Model: X1 and Y are Not Related Whether You Control for X2 or Not
• Possible Model: Correlation Between X1 and Y is the Same Whether X2 Is Statistically Controlled or Not (X2 is Irrelevant to the X1, Y Relationship)
• When You Control for X2, Correlation Between X1 and Y Drops to Zero
• When You Control for X2, the Correlation Between X1 and Y Becomes Smaller (But Does not Drop to 0 or Change Sign)
• Some Forms of Suppression: When You Control for X2, r1y.2 Becomes Larger Than r1y or Opposite in Sign to r1y
• “None of the Above”
• Results Section
• Summary
• 4. Regression Analysis and Statistical Control
• Introduction
• Hypothetical Research Example
• Graphic Representation of Regression Plane
• Semipartial (or “Part”) Correlation
• Partition of Variance In Y in Regression With Two Predictors
• Assumptions for Regression With Two Predictors
• Formulas for Regression With Two Predictors
• SPSS Regression
• Conceptual Basis: Factors That Affect the Magnitude and Sign of ß and b Coefficients in Multiple Regression With Two Predictors
• Tracing Rules for Path Models
• Comparison of Equations for ß, b, pr, and sr
• Nature of Predictive Relationships
• Effect Size Information in Regression with Two Predictors
• Statistical Power
• Issues in Planning a Study
• Results
• Summary
• 5. Multiple Regression With Multiple Predictors
• Research Questions
• Empirical Example
• Screening for Violations of Assumptions
• Issues in Planning a Study
• Computation of Regression Coefficients with k Predictor Variables
• Methods of Entry for Predictor Variables
• Variance Partitioning in Standard Regression Versus Hierarchical and Statistical Regression
• Significance Test for an Overall Regression Model
• Significance Tests for Individual Predictors in Multiple Regression
• Effect Size
• Changes in F and R as Additional Predictors Are Added to a Model in Sequential or Statistical Regression
• Statistical Power
• Nature of the Relationship Between Each X Predictor and Y (Controlling for Other Predictors)
• Assessment of Multivariate Outliers in Regression
• SPSS Examples
• Summary
• Appendix 5A: Use of Matrix Algebra to Estimate Regression Coefficients for Multiple Predictors
• Appendix 5B: Tables for Wilkinson and Dallal (1981) Test of Significance of Multiple R2 in Forward Statistical Regression
• Appendix 5C: Confidence Interval for R2
• 6. Dummy Predictor Variables in Multiple Regression
• What Dummy Variables Are and When They Are Used
• Empirical Example
• Screening for Violations of Assumptions
• Issues in Planning a Study
• Parameter Estimates and Significance Tests for Regressions With Dummy Predictor Variables
• Group Mean Comparisons Using One-Way Between-S ANOVA
• Three Methods of Coding for Dummy Variables
• Regression Models That Include Both Dummy and Quantitative Predictor Variables
• Effect Size and Statistical Power
• Nature of the Relationship and/or Follow-Up Tests
• Results
• Summary
• 7. Moderation: Interaction in Multiple Regression
• Terminology
• Interaction Between Two Categorical Predictors: Factorial ANOVA
• Interaction Between One Categorical and One Quantitative Predictor
• Preliminary Data Screening: One Categorical and One Quantitative Predictor
• Scatterplot for Preliminary Assessment of Possible Interaction Between Categorical and Quantitative Predictor
• Regression to Assess Statistical Significance of Interaction Between One Categorical and One Quantitative Predictor
• Interaction Analysis With More Than Three Categories
• Example With Different Data: Significant Sex-by-Years Interaction
• Follow-Up: Analysis of Simple Main Effects
• Interaction Between Two Quantitative Predictors
• SPSS Example of Interaction Between Two Quantitative Predictors
• Results for Interaction of Age and Habits as Predictors of Symptoms
• Graphing Interaction for Two Quantitative Predictors
• Results Section for Interaction of Two Quantitative Predictors
• Additional Issues and Summary
• Appendix 7A: Graphing Interactions Between Quantitative Variables “by Hand”
• 8. Analysis of Covariance
• Research Situations for Analysis of Covariance
• Empirical Example
• Screening for Violations of Assumptions
• Variance Partitioning in ANCOVA
• Issues in Planning a Study
• Formulas for ANCOVA
• Computation of Adjusted Effects and Adjusted Y * Means
• Conceptual Basis: Factors That Affect the Magnitude of SSAadj and SSresidual and the Pattern of Adjusted Group Means
• Effect Size
• Statistical Power
• Nature of the Relationship and Follow-Up Tests: Information to Include in the “Results” Section
• SPSS Analysis and Model Results
• Additional Discussion of ANCOVA Results
• Summary
• Appendix 8A: Alternative Methods for the Analysis of Pretest–Posttest Data
• 9. Mediation
• Definition of Mediation
• Hypothetical Research Example
• Limitations of “Causal” Models
• Questions in a Mediation Analysis
• Issues in Designing a Mediation Analysis Study
• Assumptions in Mediation Analysis and Preliminary Data Screening
• Path Coefficient Estimation
• Conceptual Issues: Assessment of Direct Versus Indirect Paths
• Evaluating Statistical Significance
• Effect Size Information
• Sample Size and Statistical Power
• Additional Examples of Mediation Models
• Note About Use of Structural Equation Modeling Programs to Test Mediation Models
• Results Section
• Summary
• 10. Discriminant Analysis
• Research Situations and Research Questions
• Introduction to Empirical Example
• Screening for Violations of Assumptions
• Issues in Planning a Study
• Equations for Discriminant Analysis
• Conceptual Basis: Factors That Affect the Magnitude of Wilks’ Lambda
• Effect Size
• Statistical Power and Sample Size Recommendations
• Follow-Up Tests to Assess What Pattern of Scores Best Differentiates Groups
• Results
• One-Way ANOVA on Scores on Discriminant Functions
• Summary
• Appendix 10A: The Eigenvalue/Eigenvector Problem
• Appendix 10B: Additional Equations for Discriminant Analysis
• 11. Multivariate Analysis of Variance
• Research Situations and Research Questions
• First Research Example: One-Way MANOVA
• Why Include Multiple Outcome Measures?
• Equivalence of MANOVA and DA
• The General Linear Model
• Assumptions and Data Screening
• Issues in Planning a Study
• Conceptual Basis of MANOVA
• Multivariate Test Statistics
• Factors That Influence the Magnitude of Wilks’ Lambda
• Effect Size for MANOVA
• Statistical Power and Sample Size Decisions
• One-Way MANOVA: Career Group Data
• 2 × 3 Factorial MANOVA: Career Group Data
• Significant Interaction in a 3 × 6 MANOVA
• Comparison of Univariate and Multivariate Follow-Up Analyses
• Summary
• 12. Exploratory Factor Analysis
• Research Situations
• Path Model for Factor Analysis
• Factor Analysis as a Method of Data Reduction
• Introduction of Empirical Example
• Screening for Violations of Assumptions
• Issues in Planning a Factor-Analytic Study
• Computation of Factor Loadings
• Steps in the Computation of PC and Factor Analysis
• Analysis 1: PC Analysis of Three Items Retaining All Three Components
• Analysis 2: PC Analysis of Three Items Retaining Only the First Component
• PC Versus PAF
• Analysis 3: PAF of Nine Items, Two Factors Retained, No Rotation
• Geometric Representation of Factor Rotation
• Factor Analysis as Two Sets of Multiple Regressions
• Analysis 4: PAF With Varimax Rotation
• Questions to Address in the Interpretation of Factor Analysis
• Results Section for Analysis 4: PAF With Varimax Rotation
• Factor Scores Versus Unit-Weighted Composites
• Summary of Issues in Factor Analysis
• Appendix 12A: The Matrix Algebra of Factor Analysis
• Appendix 12B: A Brief Introduction to Latent Variables in SEM
• 13. Reliability, Validity, and Multiple-Item Scales
• Assessment of Measurement Quality
• Cost and Invasiveness of Measurements
• Empirical Examples of Reliability Assessment
• Concepts from Classical Measurement Theory
• Use of Multiple-Item Measures to Improve Measurement Reliability
• Computation of Summated Scales
• Assessment of Internal Homogeneity for Multiple-Item Measures: Cronbach’s Alpha Reliability Coefficient
• Validity Assessment
• Typical Scale Development Process
• A Brief Note About Modern Measurement Theories
• Reporting Reliability
• Summary
• Appendix 13A: The CES-D
• Appendix 13B: Web Resources on Psychological Measurement
• 14. More About Repeated Measures
• Introduction
• Review of Assumptions for Repeated-Measures ANOVA
• First Example: Heart Rate and Social Stress
• Test for Participant-by-Time or Participant-by-Treatment Interaction
• One-Way Repeated-Measures Results for Heart Rate and Social Stress Data
• Testing the Sphericity Assumption
• MANOVA for Repeated Measures
• Results for Heart Rate and Social Stress Analysis Using MANOVA
• Doubly Multivariate Repeated Measures
• Mixed-Model ANOVA: Between-S and Within-S Factors
• Order and Sequence Effects
• First Example: Order Effect as a Nuisance
• Second Example: Order Effect Is of Interest
• Summary and Other Complex Designs
• 15. Structural Equation Modeling With AMOS: A Brief Introduction
• What Is Structural Equation Modeling?
• Review of Path Models
• More Complex Path Models
• First Example: Mediation Structural Model
• Introduction to AMOS®
• Screening and Preparing Data for SEM
• Specifying the SEM Model (Variable Names and Paths)
• Specify the Analysis Properties
• Running the Analysis and Examining Results
• Locating Bootstrapped CI Information
• Sample Results for the Mediation Analysis
• Selected SEM Model Terminology
• SEM Goodness-of-Fit Indexes
• Second Example: Confirmatory Factor Analysis
• Third Example: Model With Both Measurement and Structural Components
• Comparing Structural Equation Models
• Reporting SEM
• Summary
• 16. Binary Logistic Regression
• Research Situations
• First Example: Dog Ownership and Odds of Death
• Conceptual Basis for Binary Logistic Regression Analysis
• Definition and Interpretation of Odds
• A New Type of Dependent Variable: The Logit
• Terms Involved in Binary Logistic Regression Analysis
• Logistic Regression for First Example: Prediction of Death From Dog Ownership
• Issues in Planning and Conducting a Study
• More Complex Models
• Binary Logistic Regression for Second Example: Drug Dose and Sex as Predictors of Odds of Death
• Comparison of Discriminant Analysis With Binary Logistic Regression
• Summary
• 17. Additional Statistical Techniques
• Introduction
• A Brief History of Developments in Statistics
• Survival Analysis
• Cluster Analyses
• Time-Series Analyses
• Poisson and Binomial Regression for Zero-Inflated Count Data
• Bayes’ Theorem
• Multilevel Modeling
• Some Final Words
• Glossary
• References
• Index