Errors in Housing Unit Listing and their Effects on Survey Estimates
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In the absence of a national population or housing register, field work organizations in many countries use in-field housing unit listings to create a sampling frame for in-person household surveys. Survey designers select small geographic clusters called segments and specially trained listers are sent to the segments to record the address and/or description of every housing unit. These frames are then returned to the central office where statisticians select a sample of units for the survey. The quality of these frames is critically important for the overall survey quality. A well designed and executed sample, efforts to reduce nonresponse and measurement error, and high quality data editing and analysis cannot make up for errors of undercoverage and overcoverage on the survey frame. Previous work on housing unit frame quality has focused largely on estimating net coverage rates and identifying the types of units and segments that are vulnerable to undercoverage. This dissertation advances our understanding of the listing process, using sociological and psychological theories to derive hypotheses about lister behavior and frame coverage. Two multiple-listing datasets supporttests of these hypotheses. Chapter 1 demonstrates that two well-trained and experienced listers produce different housing unit frames in the same segments. Chapter 2 considers listing as a principal-agent interaction, but finds limited support for the ability of this perspective to explain undercoverage in traditional listing. Chapter 3 has more success explaining the mechanisms of error in dependent listing. Listers tend not to correct the errors of inclusion and exclusion on the frame they update, leading to undercoverage and overcoverage. Chapter 4 tests for bias due to the observed undercoverage, but finds little evidence that lister error would lead to substantial changes in survey estimates. Housing unit listing is a complex task that deserves more research in the survey methods literature. This work fills in some of the gaps in our understanding of the listing process, but also raises many questions. The good news for survey researchers is that the listers' errors appear to be somewhat random with respect to the household and person characteristics, at least for the variables and datasets studied in this work.