Power Analysis and Sample Size Planning

A 3-Day Remote Seminar Taught by
Christopher L. Aberson, Ph.D.

This seminar is currently sold out. Email info@statisticalhorizons.com to be added to the waitlist.

Statistical power analysis addresses the question “How large a sample do I need?” Alternatively, sample size may be determined by other factors (e.g., cost), and researchers then need to determine how much power the design affords for detecting effects of various sizes (sensitivity). Although many tools exist for calculating power and sample size for simple designs, these tasks can become quite daunting for more complex situations (e.g., designs with multiple predictor variables or mediation).

This seminar focuses on power for detecting effects across a wide range of research designs. It begins with a discussion of basic theoretical issues, such as why power is important and factors affecting power. The course then moves on to examine and demonstrate power and sensitivity approaches for designs that include t-tests, chi-square, multiple regression, logistic regression, ANOVA (between, within, and mixed designs), and mediation.

Starting December 3, we are offering this seminar as a 3-day synchronous*, remote workshop for the first time. Each day will consist of a 4-hour live lecture held via the free video-conferencing software Zoom. Participants are encouraged to join the lecture live, but will have the opportunity to view the recorded session later in the day if they are unable to attend at the scheduled time.

Each lecture session will conclude with a hands-on exercise reviewing the content covered, to be completed on your own. An additional session will be held Thursday and Friday afternoons as an “office hour”, where participants can review the exercise results with the instructor and ask any questions.

*We understand that scheduling is difficult during this unpredictable time. If you prefer, you may take all or part of the course asynchronously. The video recordings will be made available within 24 hours of each session and will be accessible for one week after the seminar, meaning that you will get all of the class content and discussions even if you cannot participate synchronously. 


For each topic, there is a strong focus on “how-to” examples for conducting analyses using the pwr2ppl R package. Attendees will also receive code and examples for analyses using other software (e.g., STATA, G*Power, SPSS). Each day of the course includes a hands-on lab session. “Office hour” sessions will be held Thursday and Friday afternoons in which participants can ask additional questions.

Participants in this seminar can expect to come away with:

  1. A conceptual understanding of power and the factors that affect power.
  2. An understanding of common misconceptions and pitfalls in conducting power analysis.
  3. An appreciation for design and analysis issues that impact power (e.g., multiple predictors, scale reliability).
  4. Experience with software for conducting statistical power and sensitivity analyses.
  5. Practical tools and strategies for conducting power/sensitivity analyses across a wide range of research designs.


This remote seminar is held via Zoom, a free video conferencing application. Instructions for joining a session via Zoom are available here. Before the seminar begins, participants will receive an email with the meeting code and password you must use to join.

This seminar will use R for examples and exercises. Very little previous experience with R is needed as most analyses require a single line of code. Code in SPSS and Stata, as well as extra examples via G*Power (where applicable), will also be provided.

WHO SHOULD Register? 

This course will be helpful for researchers in any field who design or conduct research studies. This should be especially useful for researchers submitting grant proposals that require power analysis. It will be helpful to have a basic understanding of power, effect size, and the analytic methods listed in the outline below.


Day 1

  1. What is power?
  2. Factors affecting power
  3. Power vs. Sensitivity
  4. Power for chi-square and tests of proportions
  5. Power for t-tests
  6. Power for between-subjects ANOVA

Day 2

  1. Power for within-subjects ANOVA
  2. Power for mixed models using ANOVA or linear mixed models
  3. Power for correlation and test comparing correlations
  4. Power for multiple regression designs

Day 3

  1. Power for moderated regression
  2. Power for logistic regression
  3. Power for mediator and conditional processes
  4. Building simulations for complex designs (e.g., multilevel models, structural equation modeling)
  5. How to report power analyses