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The multilevel model is increasingly used as a flexible tool in the statistical analysis of dependent behavioral research data. A drawback of this model's flexibility is that it complicates designing the study. For example, an important additional consideration in the design of a multilevel study is choosing the number and the size of the clusters to sample to ensure sufficient efficiency as quantified by precision, bias, or statistical power. To help researchers in designing their multilevel study, a user-friendly simulation tool is introduced (Multilevel Design Efficiency Using Simulation, or ML-DEs), also allowing for design questions that have not been dealt with analytically in the literature, while avoiding complex specifications of simulation studies. ML-DEs generates MLwiN macros for running the simulations and handles its output using R scripts to compare the designs' efficiencies for both fixed and random parameters, allowing for small sample sizes, unbalanced data, and more than two levels.

ML-DEs: A program for designing efficient multilevel studies