I. Cross-Sectional Power Analyses 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, PretestâPosttest 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.