Model-Assisted Domain Estimation When Combining Survey Data with Administrative Records

• Activity Number: 83
• Type: Contributed
• Date/Time: Sunday, August 3, 2014 : 4:00 PM to 5:50 PM
• Sponsor: Survey Research Methods Section
• Author(s): Dan Liao*+ and Phillip Kott
• Companies: RTI International and RTI International
• Keywords: data integration ; double sampling ; generalized regression estimator ; imputation ; prediction ; multiple data sources

Abstract

In this paper, we will examine domain estimation with the use of auxiliary information, when combining survey data with administrative records. Two competing approaches are considered: calibration weighting and probability-weighted linear prediction. When there is a domain indicator among the calibration targets, these two approaches will produce the same results. But what if there isn't? Comparisons will be made between the validity (bias) and reliability (variance) of these two methods through a simulation study based on the 2010 US birth data file. A bias test will be proposed to determine whether or not the bias of a domain estimate derived from the weighted prediction method is significantly different from zero. If it is not, the variance of this domain estimate can be measured and compared with the variance of the corresponding domain estimate derived using calibration weighting. These rival methods are also frequently used when there is a two-phase sample and the calibration targets for the final sample are computed from the first-phase sample. We will discuss the additional complications in variance estimation caused by the existence of two sampling phases.