Search Results for « Survey Sampling »

The twentieth century saw a dramatic change in the way information was generated as probability sampling replaced full enumeration.We examine some key events of the past and issues being addressed today to gain perspective on the requirements that might steer changes in the direction of survey sampling in the future.The potential for online Web panels and other methods that do not use probability sampling are considered.Finally, we conclude with some thoughts on how the future of survey sampling might be shaped by unpredictable factors.Contemplating the potential directions that survey sampling may take in the future requires considering the current state of affairs and how we arrived at this point.Despite occasional issues and controversies, it seems fair to say that survey sampling, and probability sampling in particular, has helped transform our view of society and the issues it faces.Perhaps even more importantly, sampling has changed how we think about obtaining information from a large population.We have seen the transition from full enumeration as the only acceptable method of learning about a population that was the standard in the late nineteenth century to ''partial investigations'' and finally to a full theory of sampling.Kruskal and Mosteller (1980) describe this transformation.This change has made it possible to study a wide range of topics about our society in a cost-effective and timely manner.The findings from surveys are generally accepted in popular, legal, and technical areas, although there are still debates about methods of implementation.Election surveys, both those in the run-up to the election and exit polls, demonstrate both the general acceptance and concerns about methods.Election surveys are very public and their accuracy is easily judged; the results of these surveys are highly reported and the

Summary statistics, two-sample tests, rank tests, generalised linear models, cumulative link models, Cox models, loglinear models, and general maximum pseudolikelihood estimation for multistage stratified, cluster-sampled, unequally weighted survey samples. Variances by Taylor series linearisation or replicate weights. Post-stratification, calibration, and raking. Two-phase subsampling designs. Graphics. PPS sampling without replacement. Small-area estimation.

Summary Calibration estimation, where the sampling weights are adjusted to make certain estimators match known population totals, is commonly used in survey sampling. The generalized regression estimator is an example of a calibration estimator. Given the functional form of the calibration adjustment term, we establish the asymptotic equivalence between the functional‐form calibration estimator and an instrumental variable calibration estimator where the instrumental variable is directly determined from the functional form in the calibration equation. Variance estimation based on linearization is discussed and applied to some recently proposed calibration estimators. The results are extended to the estimator that is a solution to the calibrated estimating equation. Results from a limited simulation study are presented.

Estimation of finite population totals in the presence of auxiliary information is considered. A class of estimators based on local polynomial regression is proposed. Like generalized regression estimators, these estimators are weighted linear combinations of study variables, in which the weights are calibrated to known control totals, but the assumptions on the superpopulation model are considerably weaker. The estimators are shown to be asymptotically design-unbiased and consistent under mild assumptions. A variance approximation based on Taylor linearization is suggested and shown to be consistent for the design mean squared error of the estimators. The estimators are robust in the sense of asymptotically attaining the Godambe–Joshi lower bound to the anticipated variance. Simulation experiments indicate that the estimators are more efficient than regression estimators when the model regression function is incorrectly specified, while being approximately as efficient when the parametric specification is correct.

Finite population inference is a central goal in survey sampling. Probability sampling is the main statistical approach to finite population inference. Challenges arise due to high cost and increasing non-response rates. Data integration provides a timely solution by leveraging multiple data sources to provide more robust and efficient inference than using any single data source alone. The technique for data integration varies depending on types of samples and available information to be combined. This article provides a systematic review of data integration techniques for combining probability samples, probability and non-probability samples, and probability and big data samples. We discuss a wide range of integration methods such as generalized least squares, calibration weighting, inverse probability weighting, mass imputation, and doubly robust methods. Finally, we highlight important questions for future research.

Statistics in Survey Sampling offers a comprehensive and rigorous introduction to the principles and practices of survey sampling. Bridging the gap between statistical theory and real-world data collection, this textbook presents both classical methods and modern developments, equipping readers with the tools to design effective surveys and make reliable inferences from sample data. With a strong foundation in design-based inference and frequentist methodology, the book emphasizes representativeness, efficiency, and the integration of auxiliary information in estimation procedures. It also introduces emerging research topics that reflect the evolving landscape of data collection and analysis. Key Features: Rigorous treatment of statistical theory for design-based inference in probability sampling Thorough exploration of model-assisted estimation techniques using auxiliary data Coverage of modern topics including data integration, analytic inference, predictive inference, and voluntary sample analysis Detailed examples illustrate the methods throughout the book Focused development within the frequentist framework, with limited emphasis on Bayesian or nonparametric methods Exercises in all chapters enable use as a course text or for self-study Includes appendices on key background topics such as asymptotic theory and projection techniques This textbook is ideal for graduate students in statistics with prior courses in statistical theory and linear models. It is also a valuable reference for researchers and practitioners engaged in survey design, public policy evaluation, official statistics, and data science applications involving sample-based inference.

Abstract Analysis of non-probability survey samples requires auxiliary information at the population level. Such information may also be obtained from an existing probability survey sample from the same finite population. Mass imputation has been used in practice for combining non-probability and probability survey samples and making inferences on the parameters of interest using the information collected only in the non-probability sample for the study variables. Under the assumption that the conditional mean function from the non-probability sample can be transported to the probability sample, we establish the consistency of the mass imputation estimator and derive its asymptotic variance formula. Variance estimators are developed using either linearization or bootstrap. Finite sample performances of the mass imputation estimator are investigated through simulation studies. We also address important practical issues of the method through the analysis of a real-world non-probability survey sample collected by the Pew Research Centre.