RESUMO
The Population Assessment of Tobacco and Health (PATH) Study is a nationally representative, longitudinal study of the US population on tobacco use and its effects on health, collecting data annually since 2013. The COVID-19 pandemic interrupted in-person survey data collections around the world. In the USA, this included a PATH Study data collection focused on youth (13-17) and young adults (18-19) as well as other US surveys on tobacco use. Given that it was necessary to pause data collection and considering that tobacco-use behaviours could be expected to change along with pandemic-related changes in the social environment, the original design for the 2020 PATH Study data collection for youth and young adults was modified. Also, the PATH Study Adult Telephone Survey was developed to address the need for adult tobacco use monitoring in this unprecedented time. This article describes the modifications made to the 2020 PATH Study design and protocol to provide nationally representative data for youth and adults after the onset of the COVID-19 pandemic as well as the implications of these modifications for researchers.
RESUMO
The Population Assessment of Tobacco and Health (PATH) Study is a nationally representative study of the US population on tobacco use and its effects on health, with four waves of data collection between 2013 and 2017. Prior work described the methods of the first three waves. In this paper, we describe the methods of Wave 4, during which a replenishment sample was added to the ongoing cohort. We describe the design and estimation methods of the continuing Wave 1 cohort (with four waves of data) and the Wave 4 cohort (the new cohort created at Wave 4). We provide survey quality metrics, including response rates for both cohorts and a panel conditioning analysis, and guidance on understanding the target populations for both cohorts.
RESUMO
Sampling households using commercial lists has the potential to reduce costs and to efficiently identify some subgroups for which target sample sizes are desired. However, the information on the lists for demographics like age is usually incomplete and inaccurate. We demonstrate that this inexact information can still be used to improve the efficiency with which some, but not all, demographic subgroups can be located during sampling. The paper also illustrates the use of nonlinear programming as a means for finding sample allocations that are subject to a variety of practical constraints. A commercial address list and data from the National Survey of Family Growth and the Health and Retirement Study are used to illustrate the calculation of allocations to strata of housing units defined by information on the list.