1. Introduction

- Statement of the Problem: Statistical Power in the Empirical Literature

- What Does This Mean?

- Why Monte Carlo Simulation Power Analyses?

- Software to Conduct Power Analyses

- Why Use the Approach Shown in This Book?

- Proof of Concept: Why and How the Assumption of Standardization Simplifies

- A Relationship Research Question Power Analysis

- Mplus Monte Carlo Power Analysis: Bivariate Regression

- R Monte Carlo Power Analysis: Bivariate Regression using lavaan and simsem

- A Comparison Research Question Power Analysis

- Mplus Monte Carlo Power Analysis: Two-Group Comparison

- R Monte Carlo Power Analysis: Two-Group Comparison

- Conclusion

- Suggested Readings & Resources

- Appendix 1.1: Chapter Addendum: The Fundamentals

2. A Multivariate, Two-Group, Pretest-Posttest Power Analysis

- Mplus Monte Carlo Power Analysis: Multivariate Two-Group Comparison

- R Monte Carlo Power Analysis: Multivariate Two-Group Comparison

- Simulation Power Analysis Write-Up: Multivariate Two-Group Comparison

- Suggested Readings

3. Path Analysis

- Mplus Monte Carlo Power Analysis: Mediated Path Analysis

- R Monte Carlo Power Analysis: Mediated Path Analysis

- Simulation Power Analysis Write-Up: Mediated Path Analysis

- Suggested Readings

4. Structural Equation Model

- Measurement Model: CFA

- SEM: Predictive Relationships Among CFA Models

- R Code for SEM Model-Reproduced Correlation Matrix

- Mplus Monte Carlo Power Analysis: SEM

- R Monte Carlo Power Analysis: SEM

- Impacts of Unreliability on SEM Power Estimates

- Mplus Syntax for Lower Reliability SEM Power Analysis

- R Syntax for Lower Reliability SEM Power Analysis

- Simulation Power Analysis Write-Up: SEM

- Suggested Readings

5. Logistic Regression

- The Logistical Foundation: Probabilities, Odds and Log Odds (Logits)

- Logistic Regression Power Analysis: Vakhitova and Alston-Knox (2018)

- Mplus Monte Carlo Power Analysis: Logistic Regression

- Simulation Power Analysis Write-Up: Logistic Regression

- Problems Using R packages lavaan or simsem for Logistic Regression Power Analysis

- Suggested Readings

6. Missing Data in Monte Carlo Simulation Power Analyses

- Missing Data in Mplus

- Missing Data in R Using simsem and lavaan Packages

- A Univariate Example of MCAR

- A Simple Regression MAR Example

- Monte Carlo Simulation Power Estimates and Missing Data

- Multivariate Two-Group Power Analysis Using Mplus

- Multivariate Two-Group Power Analysis Using R

- Multivariate Two-Group Simulation Power Analysis with Missing Data Write-Up

- Structural Equation Model

- Structural Equation Model Power Analysis Using Mplus

- Structural Equation Model Power Analysis Using R

- Structural Equation Model Simulation Power Analysis with Missing Data Write-Up

- Missing Data Concluding Remarks

- Suggested Readings

II. Longitudinal Power Analyses

7. Unconditional Latent Growth Curve

- The Metric of Time: Scaling and Centering

- An Unconditional Latent Growth Curve Model Power Analysis

- Mplus Monte Carlo Simulation Power Analysis

- R Monte Carlo Simulation Power Analysis

- Unconditional Latent Growth Curve Model Simulation Power Analysis Write-Up

- Latent Growth Curve Models: Moving Forward

- Suggested Readings

8. Time-Invariant Covariates

- A Tauber et al. (2021) Replication Power Analysis

- Mplus Monte Carlo Power Analysis: Longitudinal RCT Pilot

- R Monte Carlo Power Analysis: Longitudinal RCT Pilot

- Longitudinal RCT Pilot Model Simulation Power Analysis Write-Up

- But, What If…?

- Mplus Monte Carlo Power Analysis: Longitudinal Treatment Effect

- R Monte Carlo Power Analysis: Longitudinal Treatment Effect

- Longitudinal RCT Treatment Effect Model Simulation Power Analysis Write-Up

- Ok, BUT…?

- Mplus Monte Carlo Power Analysis: Longitudinal RCT Covariate

- R Monte Carlo Power Analysis: Longitudinal RCT Covariate Issues

- Longitudinal RCT Covariate Simulation Power Analysis Write-Up

- Just One More Thing

- Mplus Monte Carlo Power Analysis: Longitudinal RCT Moderation Model

- R Monte Carlo Power Analysis: Longitudinal RCT Moderation Model Issues

- Longitudinal RCT Moderation Model Simulation Power Analysis Write-Up

- A Final Note

- Suggested Readings

- Appendix 8.1: “Old School” Power Analyses Using “Old School” Methods

- Mixed-Factorial ANOVA Design Matrices

- A Mixed-Factorial ANOVA Model Simulation Power Analysis Write-Up

9. Adding Time-Varying Covariates

- Mplus Monte Carlo Power Analysis: Adding Time-Varying Covariates

- R Monte Carlo Power Analysis: Adding Time-Varying Covariates Issues

- Longitudinal Time-Varying Covariates Simulation Power Analysis Write-Up

- Mplus Monte Carlo Power Analysis: A Random Effect Model

- R Monte Carlo Power Analysis: Random Effect Model Issues

- Longitudinal Random Effect Model Simulation Power Analysis Write-Up

- Suggested Readings

10. Parallel-Process Mediation

- A Parallel-Process Power Analysis Based on Becker et al. (2016)

- Mplus Monte Carlo Power Analysis for Parallel-Process Mediation

- R Monte Carlo Power Analysis for Parallel-Process Mediation

- Parallel-Process Simulation Power Analysis Write-Up

- Suggested Readings

11. Power Analysis for Complex Longitudinal Designs

- A Complex Longitudinal Power Analysis Based on Beal et al. (2020)

- Maltreatment Predicting CDI Trajectory Variance

- CDI Trajectory and Maltreatment Predict Quality of Life (QOL)

- CDI Trajectory and Maltreatment Predict Biomarkers

- Logistic Prediction of Opioid Use Disorder

- Prediction of Opioid Misuse Disorder

- Assembling the Mplus Syntax

- A Note on RSyntax for this Design

- Complex Longitudinal Simulation Power Analysis Write-Up

- Suggested Readings

III. Conclusion

12. Statistical Power in a “Post-p < .05” World

- Ringing the Alarm Bell

- Possible Paths Toward a “Post-p < .05” World

- What Does All of This Mean?

- Suggested Readings

References

Author Index

Subject Index

About the Author

Online-Only Appendices:

Appendix A. Statistical Power for Latent Variable Moderation

Appendix B. Part 1: Statistical Power for Survival Analysis

Appendix B. Part 2: Continuous-Time Survival Analysis

Appendix C. Monte Carlo Simulation Power for Two-Level Models (Arend and Schafer, 2019)

Appendix D. Statistical Power for Moderated Mediation