Suitability of the Chosen Variables for Stratification in Large-Scale Surveys: An Exploration of the Indian Version of DHS

Abstract

Stratification is a foundational technique in survey sampling, designed to enhance the precision of estimates by reducing within-group variability and maximizing differences between subgroups. Choosing the right level of stratification is essential for obtaining accurate and policy-relevant data in a vast and diverse nation like India, where socioeconomic and health status vary significantly even within relatively small administrative units called districts. National surveys use the typical urban-rural division to solve this, although finer segmentation can yield even more precise results, particularly in the more diverse rural settings. This study assesses the relative efficiency of two- versus four-segment stratification frameworks in estimating key health indicators at the district-level in India. Using data from a large-scale health survey, four indicators child stunting, immunization coverage, four plus antenatal care, and sanitation access were analyzed. Results showed that while the two-segment design produced better precision in most districts, particularly in the populous states, the four-segment approach delivered substantial improvements in specific states and for indicators prone to higher sampling variability. These findings emphasize the importance of context-specific stratification strategies, balancing operational feasibility with statistical precision. The study advocates for adaptive survey designs that align stratification choices with local population heterogeneity and indicator-specific characteristics to optimize data quality and policy utility.