Comparative Analysis of the Efficiency of Sampling Scheme Estimators in Estimating Population Total

Sampling is a fundamental tool in statistical research, providing a practical alternative to complete enumeration where  of time, cost, personal and accessibility are constraints Choosing the best estimator for population total estimation is one of the main issues in survey sampling.  Even though many different sampling strategies and estimators are available, it is still difficult to assess how effective they are in diverse situations. This study presents a comparative analysis of the efficiency of sampling scheme estimators (Hansen Hurwitz, Horvitz-Thompson, Rao-Hartley-Cochran’s and Sen Yates-Grundy) in estimating population total using child birth data) from Ekiti State . Population totals and variances of each estimator were obtained and the most efficient estimator determined in terms of variance. The results revealed that the Rao–Hartley–Cochran estimator consistently produced the lowest population total estimates with the least variance for 2 years, while in the other year, the Sen–Yates–Grundy estimator demonstrated superior efficiency with minimal variance. (The study offers empirical evidence regarding the relative efficiency of the probability proportional to size estimators with replacement (Hansen–Hurwitz) and those without (Horvitz–Thompson, Rao–Hartley–Cochran, and Sen–Yates–Grundy in estimating population totals).  The study further showed that' efficiency  of estimators  is not constant but rather fluctuates over time and across data distributions. The study also closes the gap between theoretical sampling principles and real-world application in demographic and health statistics by using these sample strategies on actual child birth registration data from Ekiti State.

Relative efficiency, childbirth, Estimator, Sampling re-arrange for neatness

1. Anderson, M., and Scott, D. (2024). Investigating the Rao-Hartley-Cochran (RHC) method’s effectiveness in estimating labor force participation rates in informal economies. Journal of Labor Economics, 41(2), 75-88. [Google Scholar] [Crossref]

2. Brown, P., Clark, E., and Thompson, J. (2024). The Horvitz-Thompson (HT) estimator’s application in estimating malnutrition prevalence among children in low-income regions. Public Health Statistics Review, 34(2), 56-70. [Google Scholar] [Crossref]

3. Cochran, W. G. (1977). Sampling techniques (3rd ed.). Wiley. Chowdhury, S., and Ahmed, N. (2021). Assessment of the Midzuno-Sen sampling scheme in estimating deforestation rates in tropical regions. Environmental Monitoring and Assessment, 172(4), 210-221. [Google Scholar] [Crossref]

4. Horvitz, D. G., and Thompson, D. J. (1952). A generalization of sampling without replacement from a finite universe. Journal of the American Statistical Association, 47(260), 663–685. [Google Scholar] [Crossref]

5. Kish, L. (1965). Survey sampling. Wiley.Singh, D., and Mangat, N. S. (1996). Theory and analysis of sample survey designs. [Google Scholar] [Crossref]

6. Wiley Yates, F., and Grundy, P. (2018). The Yates-Grundy draw-by-draw method’s application in environmental migration studies. Population and Environment Studies Journal, 30(3), 45 [Google Scholar] [Crossref]

• Investigating the Performance of Colour Oxide on the PS/CNSO Oil Paint Production
• Real-Time Fabric Defect Detection Using a Lightweight Deformable YOLO Network
• Recent Trends in the Stock Market: An Analytical Study of Market Dynamics, Investor Behaviour, and Technological Influence
• Evaluation of Structural Dynamics and Equilibrium State, with Case Study of Large Cantilever Projection for a 10- Storey Reinforced Concrete Building in Lagos, Nigeria.
• Multi-Criteria Evaluation of AI-Based Adaptive Learning Platforms in Global Higher Education: A Fuzzy AHP Perspective