Adjusting nonresponse bias at subdomain levels using multiple response phases

When a sampling unit doesn't respond to a survey it is termed unit nonresponse. Unit nonresponse may have a dramatic affect on estimation results of interest. Using only those who responded to the survey to calculate the estimate may bias the estimate, known as nonresponse bias. Many approaches have been created in order to account for nonresponse. One such approach is to resample those nonrespondents in a second response "phase" (or more). We build a Bayesian hierarchical model that uses information from multiple response "phases" to estimate the phase specific response rates from I subdomains. This information is simultaneously used to estimate the success rates in those I subdomains. Conditional success rates are then estimated for the first phase respondents, second phase respondents, and nonrespondents (the third response phase). A relationship between these three sets of conditional success rates is incorporated into the model. This is done through a spatially dependent structure. The 1998 Missouri Turkey Hunting Survey is used to illustrate this methodology. The success rate estimates from nonrespondents have a significant impact on the overall success rate.