Sampling Weight Calibration with Estimated Control Totals

Sample weight calibration, also referred to as calibration estimation, is a widely applied technique in the analysis of survey data. This method borrows strength from a set of auxiliary variables and can produce weighted estimates with smaller mean square errors than those estimators that do not use the calibration adjustments. Poststratification is a well-known calibration method that forces weighted counts within cells generated by cross-classifying the categorical (or categorized) auxiliary variables to equal the corresponding population control totals. Several assumptions are critical to the theory developed to date for weight calibration. Two assumptions relevant to this research include: (i) the control totals calculated from the population of interest and known without (sampling) error; and (ii) the sample units selected for the survey are taken from a sampling frame that completely covers the population of interest (e.g., no problems with frame undercoverage). With a few exceptions, research to date generally is conducted as if these assumptions hold, or that any violation does not affect estimation. Our research directly examines the violation of the two assumptions by evaluating the theoretical and empirical properties of the mean square error for a set of calibration estimators, newly labeled as estimated-control (EC) calibration estimators. Specifically, this dissertation addresses the use of control totals estimated from a relatively small survey to calibrate sample weights for an independent survey suffering from undercoverage and sampling errors. The EC calibration estimators under review in the current work include estimated totals and ratios of two totals, both across all and within certain domains. The ultimate goal of this research is to provide survey statisticians with a sample variance estimator that accounts for the violated assumptions, and has good theoretical and empirical properties

Population-Based Correlates of Covid-19 infection: an analysis From the Dfw Covid-19 Prevalence Study

BACKGROUND: COVID-19 has resulted in over 1 million deaths in the U.S. as of June 2022, with continued surges after vaccine availability. Information on related attitudes and behaviors are needed to inform public health strategies. We aimed to estimate the prevalence of COVID-19, risk factors of infection, and related attitudes and behaviors in a racially, ethnically, and socioeconomically diverse urban population. METHODS: The DFW COVID-19 Prevalence Study Protocol 1 was conducted from July 2020 to March 2021 on a randomly selected sample of adults aged 18-89 years, living in Dallas or Tarrant Counties, Texas. Participants were asked to complete a 15-minute questionnaire and COVID-19 PCR and antibody testing. COVID-19 prevalence estimates were calculated with survey-weighted data. RESULTS: Of 2969 adults who completed the questionnaire (7.4% weighted response), 1772 (53.9% weighted) completed COVID-19 testing. Overall, 11.5% of adults had evidence of COVID-19 infection, with a higher prevalence among Hispanic and non-Hispanic Black persons, essential workers, those in low-income neighborhoods, and those with lower education attainment compared to their counterparts. We observed differences in attitudes and behaviors by race and ethnicity, with non-Hispanic White persons being less likely to believe in the importance of mask wearing, and racial and ethnic minorities more likely to attend social gatherings. CONCLUSION: Over 10% of an urban population was infected with COVID-19 early during the pandemic. Differences in attitudes and behaviors likely contribute to sociodemographic disparities in COVID-19 prevalence

Estimating Propensity Adjustments for Volunteer Web Surveys

Panels of persons who volunteer to participate in Web surveys are used to make estimates for entire populations, including persons who have no access to the Internet. One method of adjusting a volunteer sample to attempt to make it representative of a larger population involves randomly selecting a reference sample from the larger population. The act of volunteering is treated as a quasi-random process where each person has some probability of volunteering. One option for computing weights for the volunteers is to combine the reference sample and Web volunteers and estimate probabilities of being a Web volunteer via propensity modeling. There are several options for using the estimated propensities to estimate population quantities. Careful analysis to justify these methods is lacking. The goals of this article are (a) to identify the assumptions and techniques of estimation that will lead to correct inference under the quasi-random approach, (b) to explore whether methods used in practice are biased, and (c) to illustrate the performance of some estimators that use estimated propensities. Two of our main findings are (a) that estimators of means based on estimates of propensity models that do not use the weights associated with the reference sample are biased even when the probability of volunteering is correctly modeled and (b) if the probability of volunteering is associated with analysis variables collected in the volunteer survey, propensity modeling does not correct bias.calibration estimator; logistic regression; nonignorable selection; propensity model; reference survey; Web survey

Consumerisme et ecologisme: analyse du rapprochement entre les mouvements de consommateurs et les associations de defense de l'environnement

CNRS-CDST / INIST-CNRS - Institut de l'Information Scientifique et TechniqueSIGLEFRFranc

PracTools: computations for design of finite population samples

PracTools is an R package with functions that compute sample sizes for various types of finite population sampling designs when totals or means are estimated. One-, two-, and three-stage designs are covered as well as allocations for stratified sampling and probability proportional to size sampling. Sample allocations can be computed that minimize the variance of an estimator subject to a budget constraint or that minimize cost subject to a precision constraint. The package also contains some specialized functions for estimating variance components and design effects. Several finite populations are included that are useful for classroom instruction

Practical tools for designing and weighting survey samples

The goal of this book is to put an array of tools at the fingertips of students, practitioners, and researchers by explaining approaches long used by survey statisticians, illustrating how existing software can be used to solve survey problems, and developing some specialized software where needed. This volume serves at least three audiences: (1) students of applied sampling techniques; 2) practicing survey statisticians applying concepts learned in theoretical or applied sampling courses; and (3) social scientists and other survey practitioners who design, select, and weight survey samples. The text thoroughly covers fundamental aspects of survey sampling, such as sample size calculation (with examples for both single- and multi-stage sample design) and weight computation, accompanied by software examples to facilitate implementation. Features include step-by-step instructions for calculating survey weights, extensive real-world examples and applications, and representative programming code in R, SAS, and other packages. Since the publication of the first edition in 2013, there have been important developments in making inferences from nonprobability samples, in address-based sampling (ABS), and in the application of machine learning techniques for survey estimation. New to this revised and expanded edition: • Details on new functions in the PracTools package • Additional machine learning methods to form weighting classes • New coverage of nonlinear optimization algorithms for sample allocation • Reflecting effects of multiple weighting steps (nonresponse and calibration) on standard errors • A new chapter on nonprobability sampling • Additional examples, exercises, and updated references throughout Richard Valliant, PhD, is Research Professor Emeritus at the Institute for Social Research at the University of Michigan and at the Joint Program in Survey Methodology at the University of Maryland. He is a Fellow of the American Statistical Association, an elected member of the International Statistical Institute, and has been an Associate Editor of the Journal of the American Statistical Association, Journal of Official Statistics, and Survey Methodology. Jill A. Dever, PhD, is Senior Research Statistician at RTI International in Washington, DC. She is a Fellow of the American Statistical Association, Associate Editor for Survey Methodology and the Journal of Official Statistics, and an Assistant Research Professor in the Joint Program in Survey Methodology at the University of Maryland. She has served on several panels for the National Academy of Sciences and as a task force member for the American Association of Public Opinion Research’s report on nonprobability sampling. Frauke Kreuter, PhD, is Professor and Director of the Joint Program in Survey Methodology at the University of Maryland, Professor of Statistics and Methodology at the University of Mannheim, and Head of the Statistical Methods Research Department at the Institute for Employment Research (IAB) in Nürnberg, Germany. She is a Fellow of the American Statistical Association and has been Associate Editor of the Journal of the Royal Statistical Society,Journal of Official Statistics, Sociological Methods and Research, Survey Research Methods, Public Opinion Quarterly, American Sociological Review, and the Stata Journal. She is founder of the International Program for Survey and Data Science and co-founder of the Coleridge Initiative

PracTools: computations for design of finite population samples

PracTools is an R package with functions that compute sample sizes for various types of finite population sampling designs when totals or means are estimated. One-, two-, and three-stage designs are covered as well as allocations for stratified sampling and probability proportional to size sampling. Sample allocations can be computed that minimize the variance of an estimator subject to a budget constraint or that minimize cost subject to a precision constraint. The package also contains some specialized functions for estimating variance components and design effects. Several finite populations are included that are useful for classroom instruction

Internet surveys: can statistical adjustments eliminate coverage bias?

"The Internet is an attractive mode of data collection to survey researchers due to cost savings and timeliness in comparison with other modes. However, survey estimates are subject to coverage bias if sampled persons with Internet access are systematically different from those without Internet access who were excluded from the survey. Statistical adjustments, either through weighting or modeling methods, can minimize or even eliminate bias due to non-coverage. In the current paper, the authors examine the coverage bias associated with conducting a hypothetical Internet survey on frame of persons obtained through a random-digit-dial (RDD) sample. They compare estimates collected during telephone interviews from households with and without Internet access using data from the 2003 Michigan Behavioral Risk Factor Surveillance System in the United States. Statistical models are developed such that the coverage bias is negligible for most of the health outcomes analyzed from the Michigan survey. Though not definitive, the analysis results suggest that statistical adjustments can reduce, if not eliminate, coverage bias in the situation the authors study." (author's abstract

Practical Tools for Designing and Weighting Survey Samples

XXI, 670 p. 64 illus.online resource