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- Articles and reports: 11-522-X202100100014Description: Recent developments in questionnaire administration modes and data extraction have favored the use of nonprobability samples, which are often affected by selection bias that arises from the lack of a sample design or self-selection of the participants. This bias can be addressed by several adjustments, whose applicability depends on the type of auxiliary information available. Calibration weighting can be used when only population totals of auxiliary variables are available. If a reference survey that followed a probability sampling design is available, several methods can be applied, such as Propensity Score Adjustment, Statistical Matching or Mass Imputation, and doubly robust estimators. In the case where a complete census of the target population is available for some auxiliary covariates, estimators based in superpopulation models (often used in probability sampling) can be adapted to the nonprobability sampling case. We studied the combination of some of these methods in order to produce less biased and more efficient estimates, as well as the use of modern prediction techniques (such as Machine Learning classification and regression algorithms) in the modelling steps of the adjustments described. We also studied the use of variable selection techniques prior to the modelling step in Propensity Score Adjustment. Results show that adjustments based on the combination of several methods might improve the efficiency of the estimates, and the use of Machine Learning and variable selection techniques can contribute to reduce the bias and the variance of the estimators to a greater extent in several situations.
Key Words: nonprobability sampling; calibration; Propensity Score Adjustment; Matching.
Release date: 2021-10-15 - Articles and reports: 12-001-X202100100004Description: Multiple data sources are becoming increasingly available for statistical analyses in the era of big data. As an important example in finite-population inference, we consider an imputation approach to combining data from a probability survey and big found data. We focus on the case when the study variable is observed in the big data only, but the other auxiliary variables are commonly observed in both data. Unlike the usual imputation for missing data analysis, we create imputed values for all units in the probability sample. Such mass imputation is attractive in the context of survey data integration (Kim and Rao, 2012). We extend mass imputation as a tool for data integration of survey data and big non-survey data. The mass imputation methods and their statistical properties are presented. The matching estimator of Rivers (2007) is also covered as a special case. Variance estimation with mass-imputed data is discussed. The simulation results demonstrate the proposed estimators outperform existing competitors in terms of robustness and efficiency.Release date: 2021-06-24
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- Articles and reports: 11-522-X202100100014Description: Recent developments in questionnaire administration modes and data extraction have favored the use of nonprobability samples, which are often affected by selection bias that arises from the lack of a sample design or self-selection of the participants. This bias can be addressed by several adjustments, whose applicability depends on the type of auxiliary information available. Calibration weighting can be used when only population totals of auxiliary variables are available. If a reference survey that followed a probability sampling design is available, several methods can be applied, such as Propensity Score Adjustment, Statistical Matching or Mass Imputation, and doubly robust estimators. In the case where a complete census of the target population is available for some auxiliary covariates, estimators based in superpopulation models (often used in probability sampling) can be adapted to the nonprobability sampling case. We studied the combination of some of these methods in order to produce less biased and more efficient estimates, as well as the use of modern prediction techniques (such as Machine Learning classification and regression algorithms) in the modelling steps of the adjustments described. We also studied the use of variable selection techniques prior to the modelling step in Propensity Score Adjustment. Results show that adjustments based on the combination of several methods might improve the efficiency of the estimates, and the use of Machine Learning and variable selection techniques can contribute to reduce the bias and the variance of the estimators to a greater extent in several situations.
Key Words: nonprobability sampling; calibration; Propensity Score Adjustment; Matching.
Release date: 2021-10-15 - Articles and reports: 12-001-X202100100004Description: Multiple data sources are becoming increasingly available for statistical analyses in the era of big data. As an important example in finite-population inference, we consider an imputation approach to combining data from a probability survey and big found data. We focus on the case when the study variable is observed in the big data only, but the other auxiliary variables are commonly observed in both data. Unlike the usual imputation for missing data analysis, we create imputed values for all units in the probability sample. Such mass imputation is attractive in the context of survey data integration (Kim and Rao, 2012). We extend mass imputation as a tool for data integration of survey data and big non-survey data. The mass imputation methods and their statistical properties are presented. The matching estimator of Rivers (2007) is also covered as a special case. Variance estimation with mass-imputed data is discussed. The simulation results demonstrate the proposed estimators outperform existing competitors in terms of robustness and efficiency.Release date: 2021-06-24
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