Understanding Stunting and its determinant among children in the most populous state of India using Estimation of Population Mean under Ranked Set Sampling in the presence of missing data

Anamika Kumari, Assistant Professor
Rajesh Singh, Professor

In real-life scenarios, encountering data with missing values is common, and if not managed carefully from the outset of a study, it can lead to significant biases in survey estimates. Ranked set sampling is widely recognized for its superior efficiency compared to simple random sampling. This article introduces novel imputation methods designed to estimate population means in the context of missing data under ranked set sampling. Through simulation studies and an application to stunting and its determinants among children in Uttar Pradesh, India's most populous state, the effectiveness of the suggested estimators in handling missing data is demonstrated. Despite India's recent strides in reducing childhood stunting, it remains a significant public health challenge as the country harbors the highest number of stunted children globally. Uttar Pradesh stands out as a hotspot for childhood stunting. While recent NFHS-5 reports have shown improvement in various indicators associated with childhood stunting over the past five years, the progress in reducing childhood stunting has not been as pronounced. Therefore, it is imperative to investigate the relationship between stunting and factors known to influence it, to facilitate more effective interventions. Numerical examples involving stunting in Uttar Pradesh, as well as simulated data, confirm the superior performance of the proposed estimators over existing methods.

Keywords: Children, Adolescents, and Youth, Health and Morbidity, Sexual and Reproductive Health and Rights

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