Design-Based Methods for State-Level Survey Estimation under Three-year Data Pooling

Oregon Convention Center 

Household surveys administered by the U.S. Census Bureau, including the National Health Interview Survey, are stratified by state yet do not provide reliable survey estimates for many individual states. Bayesian hierarchical models can stabilize small area estimates using auxiliary data, yet they are still subject to model bias and misspecification. Thus, it is desirable to employ design-based methods that improve both the underlying sampling variance and their corresponding estimators.

The lack of adequate sample sizes and PSUs for general state-level estimation stems, in part, from the high cost of in-person recruitment of sampled households. To mitigate these costs, PSUs are fixed for ten-year periods. A by-product of this design is that combining three years of cross-sectional survey data ostensibly to improve precision by tripling state sample sizes has lower inferential benefits compared to independent annual PSU samples. Furthermore, spatial clustering abates the amount of travel for an interviewer in exchange for a higher design effect. We show potential gains in reliability under three-year pooled survey data with relatively cost-effective changes to the sample design.

Survey Methods, Cross-sectional data.

Design effect, Variance Estimation, Taylor-Series

Sample Design, Clustering, Intraclass Correlation

Small Area Estimation

Design-based methods

Markov Chain methods 

Survey Research Methods Section