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“Power analysis is vitally important for designing strong studies and convincing grant panels that a study is fundable, yet it is rarely (if ever) part of formal quantitative methods courses or training sequences. This book empowers researchers by providing an accessible explanation of Monte Carlo approaches to power analysis, which are particularly useful for modern, mainstream multivariate statistical models popular in the behavioral sciences. The book walks through the mechanics of Monte Carlo studies and deftly explains how to write corresponding code in Mplus and R, interpret the output, and understand implications for sample size. This is an indispensable resource for behavioral scientists and methodologists seeking to design studies and craft effective grant proposals.”

—Daniel McNeish, PhD, Department of Psychology, Arizona State University


“This book fills a critical gap for researchers. Typical textbook examples for power analysis usually refer to relatively simple models with complete data sets, whereas the reality of models and data is often much messier. The topics and simulation approach outlined here addresses this reality, by providing researchers guidance and accessible software to run Monte Carlo power analyses for complex models with multilevel and missing data. Using this book, research faculty and graduate students alike will be better equipped to conduct more solid research and to write grant proposals with more realistic sample size estimates.”

—Fred Oswald, PhD, Professor and Herbert S. Autrey Chair in Social Sciences, Department of Psychological Sciences, Rice University


“Power analysis is essential for designing robust and replicable studies, yet it can seem daunting and complex. Peugh and Litson demystify the process, offering a step-by-step guide that encourages researchers to engage deeply with their data and avoid the pitfalls of underpowered studies. The included Mplus and R syntax go well beyond basic models, and the authors’ clear explanations make simulation-based power analysis both approachable and immediately applicable.”

—Francis Huang, PhD, College of Education and Human Development, University of Missouri–Columbia

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About the Authors

James Peugh, PhD, is Director of Quantitative Services in the Division of Behavioral Medicine and Clinical Psychology, Cincinnati Children’s Hospital Medical Center, and Research Professor in the Department of Pediatrics at the University of Cincinnati Medical School. His methodological interests focus on the use of Monte Carlo simulation techniques to test advanced statistical analyses. Dr. Peugh has also published pedagogical “how-to” papers demonstrating the application of statistical techniques. He has publications in a variety of quantitative, educational, and psychological journals.

Kaylee Litson, PhD, is Assistant Professor in the Department of Psychology at the University of Houston. As an interdisciplinary quantitative psychologist, they have a particular interest in the link between statistical model estimation and theory-driven interpretation, especially in the context of complex, multimethod, and longitudinal research design. Dr. Litson's work highlights the translation of quantitative psychology methods to applied research in fields such as cognition and educational psychology. Their publications have appeared in applied and quantitative journals.

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Audience

Researchers and graduate students in psychology, education, management, family studies, public health, sociology, and social work.

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Course Use

May serve as a supplemental text in graduate-level courses in advanced quantitative methods, longitudinal analysis, power analysis, simulation design, and grant writing.