Statistical Power for Detecting Moderation in Partially Nested Designs

• In Event: Advances in Multilevel Modeling for Causal Inferences

Abstract

Analysis of the differential treatment effects across targeted subgroups and contexts is a critical objective in educational research because it delineates for whom and under what conditions a treatment is effective. Unfortunately, it is unclear how to plan efficient and effective studies that include these moderated effects when the design includes partial nesting (i.e., disparate grouping structures across treatment conditions). We develop statistical power formulas to identify requisite sample sizes and guide the planning of studies probing moderation under two-level partially nested designs. The results suggest that the power to detect moderation effects in partially nested designs is substantially influenced by sample size, moderation effect size, and moderator variance structure.

Authors

• Kyle T. Cox, University of North Carolina - Charlotte

• Benjamin Kelcey, University of Cincinnati