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Big data has been fruitfully leveraged as a supplement for survey data—and sometimes as its replacement—and in the best of worlds, as a "force multiplier" to improve survey analytics and insight. We detail a use case, the big data classifier (BDC), as a replacement to the more traditional methods of targeting households in survey sampling for given specific household and personal attributes. Much like geographic targeting and the use of commercial vendor flags, we detail the ability of BDCs to predict the likelihood that any given household is, for example, one that contains a child or someone who is Hispanic. We specifically build 15 BDCs with the combined data from a large nationally representative probability-based panel and a range of big data from public and private sources, and then assess the effectiveness of these BDCs to successfully predict their range of predicted attributes across three large survey datasets. For each BDC and each data application, we compare the relative effectiveness of the BDCs against historical sample targeting techniques of geographic clustering and vendor flags. Overall, BDCs offer a modest improvement in their ability to target subpopulations. We find classes of predictions that are consistently more effective, and others where the BDCs are on par with vendor flagging, though always superior to geographic clustering. We present some of the relative strengths and weaknesses of BDCs as a new method to identify and subsequently sample low incidence and other populations. [ABSTRACT FROM AUTHOR]

This is a correction to: David Dutwin and others, Leveraging Predictive Modelling from Multiple Sources of Big Data to Improve Sample Efficiency and Reduce Survey Nonresponse Error, Journal of Survey Statistics and Methodology, 2023, smad016, https://doi.org/10.1093/jssam/smad016In the originally published version of this manuscript, the author Joshua Lerner was inadvertently omitted from the author byline.This error has been corrected.By David Dutwin; Patrick Coyle; Joshua Lerner; Ipek Bilgen and Ned EnglishReported by Author; Author; Author; Author; Author [Extracted from the article]

Frames of residential mailing addresses based on U.S. Postal Service (USPS) sources are often used for selecting samples of housing units for surveys to be conducted in-person, by mail, by web, or using mixed modes. Address lists designed for mail delivery require some modifications for sampling purposes, which therefore require familiarity with aspects of the address files themselves. This paper takes a detailed look at the address components of these files along with the vendors from which such frames and samples are available. More specifically, this paper describes the types of address records and the components of an address record that are available. It discusses how vendors differ in both the services they provide with respect to sampling frames as well as in how those sampling frames are updated and maintained. The paper also details ways in which address-based frames can be enhanced with auxiliary data such as geocodes, area-level demographic variables, and commercial indicators at the individual address level. The match rate, append rate, accuracy, and utility of auxiliary variables are described, along with potential uses in survey design and estimation. Information contained within the paper will be useful for survey designers who have an interest in tailoring address lists for a given survey's target population and budget. [ABSTRACT FROM AUTHOR]

Address frames based largely on United States Postal Service lists can have shortcomings for in-person surveys in areas with non-locatable addresses. To give every housing unit a known, nonzero probability of selection, field methods may be implemented to improve frame coverage. Three coverage improvement methods currently in use in the survey research industry are compared—Enhanced Listing, Check for Housing Units Missed (CHUM), and Address Coverage Enhancement (ACE)—so that the reader may understand their similarities and differences. Such a comparison is useful for research practitioners and survey designers who need to consider nuances in frame construction methods that weigh cost, effectiveness, and ease of implementation. [ABSTRACT FROM AUTHOR]