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

Somnath Jana, PhD Research Scholar

Survey precision is an important consideration in large-scale investigations; Stratification enhances accuracy by dividing populations into subgroups based on key traits. This approach reduces sample variability in homogeneous groups, potentially lowering standard errors and improving data representativeness, especially when seeking precise estimates from diverse populations. This study investigates the effectiveness of different stratification approaches in large-scale surveys, focusing on health indicators in India. Comparing 2x stratification (urban-rural division) with 4x stratification (three rural segments and one urban segment), the research analyzes data from districts in Uttar Pradesh and Kerala (States of India). Key indicators include stunting, immunization, antenatal care visits, and access to sanitation facilities. The study found that 2x stratification generally outperforms 4x stratification across most districts and health indicators in India. In Uttar Pradesh, 2x stratification performed better in 64% of districts for stunting, 54.67% for immunization, 58.67% for antenatal care, and 46.67% for sanitation. Kerala demonstrated even more pronounced advantages for 2x stratification. The research concludes that 2x stratification may be more effective at capturing local contexts, leading to more representative sampling and precise estimates. These findings suggest that simpler stratification approaches can often yield more accurate and cost-effective results in health and development surveys in India.

Keywords: Data and Methods, Data visualisation

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