Does the Measure of Dispersion Matter in Multilevel Research?
Quinetta M. Roberson Cornell University Michael C. Sturman Cornell University Tony L. Simons Cornell University Within the context of climate strength, this simulation study examines the validity of various dispersion indexes for detecting meaningful relationships between variability in group member perceptions and outcome variables. We used the simulation to model both individual-and group-level phenomena, vary appropriate population characteristics, and test the proclivity of standard and average deviation, interrater agreement indexes (rwg, r*wg, awg), and coefficient of variation (both normed and unnormed) for Type I and Type II errors. The results show that the coefficient of variation was less likely to detect interaction effects although it outperformed other measures when detecting level effects. Standard deviation was shown to be inferior to other indexes when no level effect is present although it may be an effective measure of dispersion when modeling strength or interaction
