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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]

Recent advances in computing technologies have enabled the development of low-cost, compact weather and air quality monitors. The U.S. federally funded Array of Things (AoT) project has deployed more than 140 such sensor nodes throughout the City of Chicago. This article combines a year's worth of AoT sensor data with household data collected from 450 elderly Chicagoans in order to explore the feasibility of using previously unavailable data on local environmental conditions to improve traditional neighborhood research. Specifically, we pilot the use of AoT sensor data to overcome limitations in research linking air pollution to poor physical and mental health and find support for recent findings that exposure to pollutants contributes to both respiratory- and dementia-related diseases. We expect that this support will become even stronger as sensing technologies continue to improve and more AoT nodes come online, enabling additional applications to social science research where environmental context matters. [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]

Objectives. The sample for the second wave (2010) of National Social Life, Health, and Aging Project (NSHAP) was designed to increase the scientific value of the Wave 1 (2005) data set by revisiting sample members 5 years after their initial interviews and augmenting this sample where possible. Method. There were 2 important innovations. First, the scope of the study was expanded by collecting data from coresident spouses or romantic partners. Second, to maximize the representativeness of the Wave 2 data, nonrespondents from Wave 1 were again approached for interview in the Wave 2 sample. Results. The overall unconditional response rate for the Wave 2 panel was 74%; the conditional response rate of Wave 1 respondents was 89%; the conditional response rate of partners was 84%; and the conversion rate for Wave 1 nonrespondents was 26%. Discussion. The inclusion of coresident partners enhanced the study by allowing the examination of how intimate, household relationships are related to health trajectories and by augmenting the size of the NSHAP sample size for this and future waves. The uncommon strategy of returning to Wave 1 nonrespondents reduced potential bias by ensuring that to the extent possible the whole of the original sample forms the basis for the field effort. NSHAP Wave 2 achieved its field objectives of consolidating the panel, recruiting their resident spouses or romantic partners, and converting a significant proportion of Wave 1 nonrespondents. [ABSTRACT FROM AUTHOR]

Objectives. The sample for the second wave (2010) of National Social Life, Health, and Aging Project (NSHAP) was designed to increase the scientific value of the Wave 1 (2005) data set by revisiting sample members 5 years after their initial interviews and augmenting this sample where possible. Method. There were 2 important innovations. First, the scope of the study was expanded by collecting data from coresident spouses or romantic partners. Second, to maximize the representativeness of the Wave 2 data, nonrespondents from Wave 1 were again approached for interview in the Wave 2 sample. Results. The overall unconditional response rate for the Wave 2 panel was 74%; the conditional response rate of Wave 1 respondents was 89%; the conditional response rate of partners was 84%; and the conversion rate for Wave 1 nonrespondents was 26%. Discussion. The inclusion of coresident partners enhanced the study by allowing the examination of how intimate, household relationships are related to health trajectories and by augmenting the size of the NSHAP sample size for this and future waves. The uncommon strategy of returning to Wave 1 nonrespondents reduced potential bias by ensuring that to the extent possible the whole of the original sample forms the basis for the field effort. NSHAP Wave 2 achieved its field objectives of consolidating the panel, recruiting their resident spouses or romantic partners, and converting a significant proportion of Wave 1 nonrespondents. [ABSTRACT FROM AUTHOR]

Objectives: We investigate the impact of data collection mode on responses to variables in NSHAP Round 4 and discuss how potential mode differences should (and should not) be addressed in substantive analyses., Methods: Among the set of respondents who were eligible to be contacted remotely in Round 4, we randomly selected 398 to be contacted instead for an in-person interview. We compare response rates and the distributions of selected key outcomes among those 398 respondents to those among the control group who were initially approached remotely. As a contrast, we compare all R4 respondents according to the mode in which they completed the interview, including those not part of the randomized experiment., Results: Among those included in the experiment, there was no evidence of systematic differences in responses to physical and mental health questions between remote and in-person modes, nor in responses to number recall measures. In-person respondents scored moderately lower on cognitive function measures requiring careful attention to a figure and/or task, though this difference became less with each similar item. Remote respondents named fewer social network members. Comparing all respondents according to their final mode yielded substantially different results in all cases., Discussion: Mode did not appear to affect reports of physical and mental health based on a randomized comparison, though it did moderately affect other items in predictable ways. Naïve estimates of mode effects based on comparing all respondents according to mode yielded misleading results, and should not be used to adjust for mode differences in analyses., (© The Author(s) 2024. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Background: Studies conducted in the United States such as the National Survey of Family Growth (NSFG) and the Pregnancy Risk Assessment Monitoring System (PRAMS) collect data on pregnancy intentions to aid in improving health education, services, and programs. PRAMS collects data from specific sites, and NSFG is a national household-based survey. Like NSFG, the Surveys of Women was designed to survey participants residing in households using an address-based sample and a multimode data collection approach. The Surveys of Women collects data from eligible participants in 9 states within the United States on contraception use, reproductive health, and pregnancy intentions. In this paper, we focus on the baseline data collection protocol, including sample design, data collection procedures, and data processing. We also include a brief discussion on the follow-up and endline survey methodologies. Our goal is to inform other researchers on methods to consider when fielding a household-level reproductive health survey., Objective: The Surveys of Women was developed to support state-specific research and evaluation projects, with an overall goal of understanding contraceptive health practices among women aged 18-44 years. The project collects data from respondents in 9 different states (Arizona, Alabama, Delaware, Iowa, Maryland, New Jersey, Ohio, South Carolina, and Wisconsin) over multiple rounds., Methods: Households were selected at random using address-based sampling methods. This project includes a cross-sectional baseline survey, 2 or 3 follow-up surveys with an opt-in panel of respondents, and a cross-sectional endline survey. Each round of data collection uses a multimode design through the use of a programmed web survey and a formatted hard copy questionnaire. Participants from the randomly selected households access their personalized surveys through a web survey or mail in a hard copy questionnaire. To maximize responses, these surveys follow a rigorous schedule of various prompts bolstering the survey implementation design, and the participants received a modest monetary incentive., Results: This is an ongoing project with results published separately by the evaluation teams involved with data analysis., Conclusions: The methods used in the first baseline survey informed modifications to the methods used in subsequent statewide surveys. Data collected from this project will provide insight into women's reproductive health, contraceptive use, and abortion attitudes in the 9 selected states. The long-term goal of the project is to use a data collection methodology that collects data from a representative sample of participants to assess changes in reproductive health behaviors over time., International Registered Report Identifier (irrid): DERR1-10.2196/40675., (©Stephanie Poland, Michael Stern, Ned English, Steven Pedlow, Katherine Archambeau, Kari Carris. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 15.03.2023.)

Objectives: This article, and corresponding articles for the earlier rounds of the National Social Life, Health, and Aging Project (NSHAP), provide the scientific underpinning for the statistical analysis of NSHAP data. The 2015-2016 round of data collection for NSHAP comprised the third wave of data collection for the original cohort born 1920-1947 (C1) and the first wave of data collection for a second cohort born 1948-1965 (C2). Here we describe (a) our protocol for reinterviewing C1; (b) our approach to the sample design for C2, including the frame construction, stratification, clustering, and within-household selection; and (c) the construction of cross-sectional weights for the entire 2015-2016 sample when analyzed at the individual level or when analyzed as a sample of cohabiting couples. We also provide guidance on computing design-based standard errors., Methods: The sample for C2 was drawn independently of the C1 sample using the NORC U.S. National Sampling Frame. A probability sample of households containing at least one individual born 1948-1965 was drawn, and from these, each age-eligible individual was included together with their cohabiting spouse or partner (even if not age-eligible). This C2 sample was combined with the C1 sample to yield a sample representative of the U.S. population of adults born 1920-1965., Results: Among C1, we conducted 2,409 interviews corresponding to a 91% conditional response rate (i.e., among previous respondents); the unconditional three-wave response rate for the original C1 sample was 71%. Among C2, we conducted 2,368 interviews corresponding to a response rate of 76%., Discussion: Together C1 and C2 permit inference about the U.S. population of home-dwelling adults born from 1920 to 1965. In addition, three waves of data from C1 are now available, permitting longitudinal analyses of health outcomes and their determinants among older adults., (© The Author(s) 2021. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)