Abstract
Hierarchical data sets arise when data for units (e.g., students) are nested within various clusters (e.g., classes and schools), and often appear in behavioral research. Estimating statistical power and sample size requirements is one of the fundamental questions in data collection, especially in experimental research where obtaining large samples is sometimes unrealistic. In the present research, we discuss a general procedure for evaluating statistical power to test intervention effects in experimental research with hierarchical data, focusing mainly on a two-way between-subjects design. This approach enables the statistical power of various types of contrasts to be evaluated with respect to main effects and interaction effects by using multiparameter tests based on Wald statistics. Additionally, several numerical examples are presented to show how the statistical power for various contrasts changes with various values of sample size, sizes of intervention effects, intraclass correlation and some data assumptions. Extensions of the proposed method and issues for practical applications are noted in discussion.
