Intraclass Correlations Values in International Development: Evidence Across Commonly Studied Domains in sub-Saharan...

Area of Science:

• Development Economics
• Public Health Research
• Statistical Methodology

Background:

• Accurate intraclass correlations (ICC) are crucial for power calculations in cluster-randomized trials (CRTs).
• Estimating ICCs is challenging as data are often unavailable during the study design phase.
• Development program evaluations increasingly require robust statistical power.

Purpose of the Study:

• To provide accurate intraclass correlation (ICC) estimates for commonly studied indicators in sub-Saharan Africa.
• To aid researchers in conducting appropriate power calculations for clustered randomized trials (CRTs) in development research.
• To inform the design of more efficient and cost-effective development program evaluations.

Main Methods:

• Analysis of rich datasets from Kenya, Malawi, Zambia, and Zimbabwe.
• Calculation of intraclass correlations (ICCs) across diverse domains relevant to development research.
• Comparison of estimated ICCs with commonly assumed values in power calculations.

Main Results:

• Intraclass correlations (ICCs) for many development research indicators in sub-Saharan Africa are lower than typically assumed.
• Particularly low ICC values were observed for indicators related to child nutrition and food security.
• These findings suggest that CRT designs may be feasible even with budget constraints.

Conclusions:

• Lower ICCs reduce the required sample size for cluster-randomized trials (CRTs), making them more accessible.
• The study provides essential data for improving the design and power of development program evaluations in sub-Saharan Africa.
• Cluster-randomized trials are a viable and potentially cost-effective design for evaluating interventions in this region.