Monte Carlo Simulation Power Analysis Using Mplus and R

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A01=James Peugh
A01=Kaylee Litson
advanced power analysis in behavioural sciences
advanced quantitative methods
analyzing
applied statistics
Author_James Peugh
Author_Kaylee Litson
Category=JMB
classes
courses
cross-sectional
determining sample sizes
eq_bestseller
eq_isMigrated=1
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eq_nobargain
eq_non-fiction
eq_society-politics
estimating
forthcoming
graduate students
guidebook
justification
justifying research designs
longitudinal
longitudinal research methods
mediation analysis
missing data techniques
models
moderation models
primer
replication
sample size calculation
statistical power estimates
statistical power estimation
syntax scripts

Product details

  • ISBN 9781462562848
  • Dimensions: 178 x 254mm
  • Publication Date: 12 Jun 2026
  • Publisher: Guilford Publications
  • Publication City/Country: US
  • Product Form: Paperback
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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.

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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