LAUSR

The sharp increase in the number of experimental studies evaluating development programs raises the need for accurate intraclass correlations (ICC) to conduct power calculations so that researchers can design studies to detect meaningful effects with sufficient statistical power. The intraclass correlation is an important parameter for determining the statistical power of cluster-randomized trials. The parameter is rarely available to researchers planning a study until after the design is set and data are already collected. This paper takes an important step towards helping researchers working in sub-Saharan Africa to accurately estimate appropriate sample sizes for their clustered RCTs. The study draws from rich data sets in Kenya, Malawi, Zambia, and Zimbabwe. We present ICCs for a wide range of domains common for development research. Our results suggest that ICCs for commonly studied indicators in sub-Saharan Africa are lower than is often assumed in power calculations. ICC values are especially low for indicators associated with child nutrition and food security, suggesting that cluster-RCTs might be a viable design even when faced with limited budgets because sample size requirements are not much different from an individual random assignment design.