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All (3) ((3 results))
- 1. Panel surveys: Adding the fourth dimension ArchivedArticles and reports: 12-001-X199300214452Description:
Surveys across time can serve many objectives. The first half of the paper reviews the abilities of alternative survey designs across time - repeated surveys, panel surveys, rotating panel surveys and split panel surveys - to meet these objectives. The second half concentrates on panel surveys. It discusses the decisions that need to be made in designing a panel survey, the problems of wave nonresponse, time-in-sample bias and the seam effect, and some methods for the longitudinal analysis of panel survey data.
Release date: 1993-12-15 - Articles and reports: 12-001-X199300214460Description:
Methods for estimating response bias in surveys require “unbiased” remeasurements for at least a subsample of observations. The usual estimator of response bias is the difference between the mean of the original observations and the mean of the unbiased observations. In this article, we explore a number of alternative estimators of response bias derived from a model prediction approach. The assumed sampling design is a stratified two-phase design implementing simple random sampling in each phase. We assume that the characteristic, y, is observed for each unit selected in phase 1 while the true value of the characteristic, \mu, is obtained for each unit in the subsample selected at phase 2. We further assume that an auxiliary variable x is known for each unit in the phase 1 sample and that the population total of x is known. A number of models relating y, \mu and x are assumed which yield alternative estimators of E (y - \mu), the response bias. The estimators are evaluated using a bootstrap procedure for estimating variance, bias, and mean squared error. Our bootstrap procedure is an extension of the Bickel-Freedman single phase method to the case of a stratified two-phase design. As an illustration, the methodology is applied to data from the National Agricultural Statistics Service reinterview program. For these data, we show that the usual difference estimator is outperformed by the model-assisted estimator suggested by Särndal, Swensson and Wretman (1991), thus indicating that improvements over the traditional estimator are possible using the model prediction approach.
Release date: 1993-12-15 - Articles and reports: 12-001-X199300114476Description:
This paper focuses on how to deal with record linkage errors when engaged in regression analysis. Recent work by Rubin and Belin (1991) and by Winkler and Thibaudeau (1991) provides the theory, computational algorithms, and software necessary for estimating matching probabilities. These advances allow us to update the work of Neter, Maynes, and Ramanathan (1965). Adjustment procedures are outlined and some successful simulations are described. Our results are preliminary and intended largely to stimulate further work.
Release date: 1993-06-15
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Analysis (3)
Analysis (3) ((3 results))
- 1. Panel surveys: Adding the fourth dimension ArchivedArticles and reports: 12-001-X199300214452Description:
Surveys across time can serve many objectives. The first half of the paper reviews the abilities of alternative survey designs across time - repeated surveys, panel surveys, rotating panel surveys and split panel surveys - to meet these objectives. The second half concentrates on panel surveys. It discusses the decisions that need to be made in designing a panel survey, the problems of wave nonresponse, time-in-sample bias and the seam effect, and some methods for the longitudinal analysis of panel survey data.
Release date: 1993-12-15 - Articles and reports: 12-001-X199300214460Description:
Methods for estimating response bias in surveys require “unbiased” remeasurements for at least a subsample of observations. The usual estimator of response bias is the difference between the mean of the original observations and the mean of the unbiased observations. In this article, we explore a number of alternative estimators of response bias derived from a model prediction approach. The assumed sampling design is a stratified two-phase design implementing simple random sampling in each phase. We assume that the characteristic, y, is observed for each unit selected in phase 1 while the true value of the characteristic, \mu, is obtained for each unit in the subsample selected at phase 2. We further assume that an auxiliary variable x is known for each unit in the phase 1 sample and that the population total of x is known. A number of models relating y, \mu and x are assumed which yield alternative estimators of E (y - \mu), the response bias. The estimators are evaluated using a bootstrap procedure for estimating variance, bias, and mean squared error. Our bootstrap procedure is an extension of the Bickel-Freedman single phase method to the case of a stratified two-phase design. As an illustration, the methodology is applied to data from the National Agricultural Statistics Service reinterview program. For these data, we show that the usual difference estimator is outperformed by the model-assisted estimator suggested by Särndal, Swensson and Wretman (1991), thus indicating that improvements over the traditional estimator are possible using the model prediction approach.
Release date: 1993-12-15 - Articles and reports: 12-001-X199300114476Description:
This paper focuses on how to deal with record linkage errors when engaged in regression analysis. Recent work by Rubin and Belin (1991) and by Winkler and Thibaudeau (1991) provides the theory, computational algorithms, and software necessary for estimating matching probabilities. These advances allow us to update the work of Neter, Maynes, and Ramanathan (1965). Adjustment procedures are outlined and some successful simulations are described. Our results are preliminary and intended largely to stimulate further work.
Release date: 1993-06-15
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