Simulation and Power Analysis Using R

Simulation-based methods are increasingly central to modern applied research, particularly as study designs and statistical models grow more complex. Traditional analytic tools for power analysis and study planning often rely on simplifying assumptions that are difficult to justify in real-world settings. Simulation provides a flexible alternative, allowing researchers to explore design choices, modeling assumptions, and inferential tradeoffs under realistic data-generating processes. Simulation and Power Analysis Using R presents simulation not only as a tool for estimating power, but as a broader framework for study design and causal reasoning. Using applied examples and clear conceptual motivation, the book demonstrates how simulation can inform decisions before data are collected.

Features:

⢠Practical and applied introduction to simulation-based power analysis and study design

⢠Coverage of generalized linear models and generalized linear mixed models, including clustered and hierarchical data

⢠Extensions to experimental and quasi-experimental designs such as randomized controlled trials, cluster randomized trials, regression discontinuity, difference-in-differences, and interrupted time series

⢠Strategies for varying simulation parameters to explore robustness and design tradeoffs

⢠Methods for simulating realistic data conditions, including missing data and model misspecification

⢠Fully reproducible R code throughout, built around a modular simulation framework using the simglm package

This book is intended for applied researchers, data scientists, and graduate students in fields such as statistics, education, psychology, public health, and the social sciences. It is particularly well suited for researchers who need to justify study design or power calculations in grant proposals, or who work with complex designs for which standard formulas are inadequate. The book may also be used as a text for graduate courses or advanced seminars on simulation, power analysis, or applied causal inference.