These slides are designed for a day-long course on power analysis. It supports the book I wrote on power analysis, available here:
Here is an abstract of the course:
Understanding statistical power is important for researchers, funders, study participants, and the Education Science Field at large. This course will introduce the major concepts in power analysis (e.g., types of errors) and how they relate to statistical analyses. This introduction will study the treatment-control design for simple random samples, simple random samples with covariates, multilevel models, and multilevel models with covariates. Discussion will also include how to research the assumptions that go into a power analysis and how to write about power.
This course will include lecture and hands on activities. Free software (R and R studio) to perform power analysis will be introduced with interactive exercises.
After the course, students will be able to perform power analysis for studies for a variety of designs, know how to research assumptions, and how to write about power.
This course is geared towards study personnel involved in the planning and execution of education studies: professors at all levels, post-docs, graduate students, and staff at research firms.
Prerequisite skills or knowledge
A passing grade in an introductory statistics course at the undergraduate level is preferable. No previous experience with R or programming is necessary.