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A number of researchers have suggested list-assisted sampling for the selection of telephone households to overcome some of the operational difficulties associated with the Mitofsky-Waksberg methods of random digit dialing (RDD). An advantage of a list-assisted method of RDD is that an equal probability systematic sample of telephone numbers can be selected and the variances of estimates from such a sample are usually lower than from a clustered design like the Mitofsky-Waksberg method. The main disadvantage of the list-assisted method is that it excludes some households from the sample, thus creating a coverage bias in the estimates. This article describes research on the coverage bias for a particular method of list-assisted sampling. The two key determinants of coverage bias are the proportion of households that are not eligible for the sample and the differences in the characteristics of the covered and not covered populations. The results show that about 4 percent of all households are excluded in national samples using this method of sampling. Furthermore, they show that the differences between the covered and uncovered populations are generally not large. The coverage bias resulting from these conditions may often be small., Introduction The percentage of households with telephones in the United States has increased steadily over the last 30 years, and approximately 94 percent of all persons now live in such [...]

For over a decade, address-based sampling (ABS) frames have often been used to draw samples for multistage area sample surveys in lieu of traditionally listed (or enumerated) address frames. However, it is well known that the use of ABS frames for face-to-face surveys suffer from undercoverage due to, for example, households that receive mail via a PO Box rather than being delivered to the household's street address. Undercoverage of ABS frames has typically been more prominent in rural areas but can also occur in urban areas where recent construction of households has taken place. Procedures have been developed to supplement ABS frames to address this undercoverage. In this article, we investigate a procedure called Address Coverage Enhancement (ACE) that supplements the ABS frame with addresses not found on the frame, and the resulting effects the addresses added to the sample through ACE have on estimates. Weighted estimates from two studies, the Population Assessment of Tobacco and Health Study and the 2017 US Program for the International Assessment of Adult Competencies, are calculated with and without supplemental addresses. Estimates are then calculated to assess if poststratifying analysis weights to control for urbanicity at the person level brings estimates closer to estimates from the supplemented frame. Our findings show that the noncoverage bias was likely minimal across both studies for a range of estimates. The main reason is because the Computerized Delivery Sequence file coverage rate is high, and when the coverage rate is high, only very large differences between the covered and not covered will result in meaningful bias., (© The Author(s) 2021. Published by Oxford University Press on behalf of the American Association for Public Opinion Research. All rights reserved. For permissions, please email: journals.permissions@oup.com.)