Nonprobability Samples for Small Area Estimation: A Review and Comparative Simulation Study

2026-08-13

AI Summary

This study addresses the lack of methodological guidance for selecting non-probability sampling methods in small area estimation by proposing a unified classification framework centered on data defect relevance. Through a systematic review and comprehensive simulation experiments, various existing methods are extended to small area contexts and rigorously evaluated. The research elucidates the applicability boundaries and comparative performance of these methods under varying data quality conditions. Consequently, this work provides both theoretical foundations and practical guidelines for method selection in small area estimation, effectively bridging a critical gap in current methodological literature.

Abstract

Nonprobability samples (NPS) are attractive because they are less costly to collect, can provide substantially larger sample sizes, and may reach populations that traditional probability surveys do not. As response rates for traditional surveys fall, interest in NPS has grown rapidly within the field of survey statistics. These methods are especially relevant for small area estimation (SAE), where there is ever-present demand for estimates at fine geographic scales and detailed demographic domains. Despite rapid methodological development, there remains limited understanding of which approaches perform best under different conditions. In this paper, we review recent developments in NPS methodology, including the concept of data defect correlation (DDC) as a measure of data quality and as a tool for categorizing the various NPS methods. We then present a comprehensive simulation study that evaluates a range of NPS approaches under varying levels of DDC and extend several existing methods to the SAE setting.

Problem

• Nonprobability Samples
• Small Area Estimation
• Data Defect Correlation

Innovation

• Nonprobability Samples
• Small Area Estimation
• Data Defect Correlation
• Comparative Simulation Study