Overview
Planning effective research investigations requires sophisticated power analysis techniques. This book provides readers with clearly explained tools for using Monte Carlo simulations to estimate the needed sample sizes for adequate statistical power for a variety of modern research designs. Featuring step-by-step instructions, chapters move from simpler cross-sectional designs and path tracing rules to advanced longitudinal designs, while incorporating mediation, moderation, and missing data considerations. Worked-through applied examples with annotated Mplus and R syntax scripts, sample power analysis write-ups, and end-of-chapter suggested readings are also included. The companion website offers Mplus and R code for four additional power analysis models--latent variable moderation, discrete- and continuous-time survival analyses, cross-sectional and longitudinal two-level models, and moderated mediation--as well as supplemental computational materials.
Full Product Details
Author: James Peugh (Cincinnati Children's Hospital Medical Center, United States) ,
Kaylee Litson (University of Houston, United States)
Publisher: Guilford Publications
Imprint: Guilford Press
ISBN: 9781462562848
ISBN 10: 1462562841
Pages: 350
Publication Date: 12 June 2026
Audience:
College/higher education
,
Postgraduate, Research & Scholarly
Format: Paperback
Publisher's Status: Forthcoming
Availability: Not yet available
This item is yet to be released. You can pre-order this item and we will dispatch it to you upon its release.
Reviews
""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 ""Drawing on nearly 20 years of teaching graduate-level quantitative methods, this text is an exceptional resource for lessons in power analysis across courses in multiple linear and logistic regression, multilevel modeling, and introductory and advanced latent/structural equation modeling. Beyond power analysis, the book can also be used for instruction in how missingness and incorrect model specification can impact parameter point and interval estimates. The progression of concepts and the writing style are easy to follow, and the companion code is invaluable. I will most certainly use this book for my classes.""--Elizabeth A. Sanders, PhD, Professor and Director, Measurement and Statistics Program, College of Education, University of Washington, Seattle-
""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-
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 Drawing on nearly 20 years of teaching graduate-level quantitative methods, this text is an exceptional resource for lessons in power analysis across courses in multiple linear and logistic regression, multilevel modeling, and introductory and advanced latent/structural equation modeling. Beyond power analysis, the book can also be used for instruction in how missingness and incorrect model specification can impact parameter point and interval estimates. The progression of concepts and the writing style are easy to follow, and the companion code is invaluable. I will most certainly use this book for my classes.--Elizabeth A. Sanders, PhD, Professor and Director, Measurement and Statistics Program, College of Education, University of Washington, Seattle 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.--Frederick L. 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 -
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 Drawing on nearly 20 years of teaching graduate-level quantitative methods, this text is an exceptional resource for lessons in power analysis across courses in multiple linear and logistic regression, multilevel modeling, and introductory and advanced latent/structural equation modeling. Beyond power analysis, the book can also be used for instruction in how missingness and incorrect model specification can impact parameter point and interval estimates. The progression of concepts and the writing style are easy to follow, and the companion code is invaluable. I will most certainly use this book for my classes.--Elizabeth A. Sanders, PhD, Professor and Director, Measurement and Statistics Program, College of Education, University of Washington, Seattle 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 address 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.--Frederick L. 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 -
Author Information
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.
