The Bayesian information criterion (BIC) can be useful for model selection within multilevel modeling studies. However, the formula for BIC requires a value for N, which is unclear in multilevel models, since N is observed in at least two levels. The present study uses simulated data to evaluate the rate of false positives and power when using a…

There have been increasing calls for applied researchers to see and utilize effect sizes as the primary outcomes of their research. However, this sometimes places a methodological burden on researchers whose primary interests are substantive. Motivated by a desire to help applied researchers better report effect sizes and their confidence…

Recent studies show cluster randomized trials may be well powered to detect mediation or indirect effects in multilevel settings. However, literature has rarely provided guidance on designing cluster-randomized trials aim to assess indirect effects. In this study, we developed closed-form expression to estimate the variance of and the statistical…

Mediation in multi-level data can be examined using conflated multilevel modeling (CMM), unconflated multilevel modeling (UMM), or multilevel structural equation modeling (MSEM). A Monte Carlo study was performed to compare the three methods on bias, type I error, and power in a 1-1-1 model with random slopes. The three methods showed no…