Skip to main content
Fig. 3 | BMC Neuroscience

Fig. 3

From: Multilevel analysis quantifies variation in the experimental effect while optimizing power and preventing false positives

Fig. 3

Use of conventional analysis methods on design B data can result in a loss of power. Using conventional analysis methods to model design B data that includes cluster-related variation in the intercept and no cluster-related variation in the experimental effect (\(\sigma_{u0}^{2}\) >0 and \(\sigma_{u1}^{2}\) = 0; study 1b) results in a loss of statistical power compared to using a multilevel model. The presented results are equal for the multilevel model that only includes variation in the intercept, and the multilevel model that includes variation in both the intercept and the experimental effect. Fitted conventional analysis methods were a a t test on individual observations and b a paired t test on the experimental condition specific cluster means. The loss in statistical power is overall greatest when both the number of clusters and effect size d are small and the cluster-related variation in the intercept is considerable. In case that the cluster-related variation in the intercept and in the experimental effect both equal zero (that is, ICC = \(\sigma_{u1}^{2}\) = 0; study 1a), using a t test on individual observations is equally powerful as multilevel analysis, but using multilevel analysis is more powerful compared to a paired t test on summary statistics. The actual statistical power of multilevel analysis given \(\sigma_{u1}^{2}\) = 0, = 0.20 or 0.50, N = 10, and increasing numbers of observations per experimental effect per cluster is given in Fig. 5b, solid line

Back to article page