Member Training: Power Analysis and Sample Size Determination Using Simulation

by guest

This webinar will show you strategies and steps for using simulations to estimate sample size and power. You will learn:
  • A review of basic concepts of statistical power and effect size
  • A simulation-based approach to power analysis
  • An overview of how to implement simulations in various popular software programs.
We will then explore two examples. One will compare sample size estimates from a simulation to those obtained from GPower and from hand calculations in a very simple test.
Then we’ll expand the technique to a more practical example, two-way ANOVA. We will discuss the flexibility of simulations to estimate sample sizes for difficult models, including multilevel models and path analysis.
We will further discuss strategies for estimating effect sizes in other tricky situations:
  • When information is limited and uncertainty is high
  • Buffering power against inaccurate effect size estimates
  • Strategies for optimizing power beyond increasing sample size.

Note: This training is an exclusive benefit to members of the Statistically Speaking Membership Program and part of the Stat’s Amore Trainings Series. Each Stat’s Amore Training is approximately 90 minutes long.

About the Instructor

Sean Lane is an assistant professor at Purdue University in the Department of Psychological Sciences. His substantive interests center on emotion regulation processes, their downstream effects on health and relationships, and how they differ between healthy and clinical subgroups. He also develops and applies new quantitative methods in the service of answering such questions. Most recently, he has been interested in developing flexible and robust power analysis tools that can be used to optimize the design of complex longitudinal studies. Sean received a Ph.D. in Psychology from New York University in 2013.

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You’ll get access to this training webinar, plus live Q&A sessions, a private stats forum, 70+ other stats trainings, and more.

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