Response and nonresponse
Results
All (3)
All (3) ((3 results))
- Articles and reports: 12-001-X202100200004Description:
This note presents a comparative study of three methods for constructing confidence intervals for the mean and quantiles based on survey data with nonresponse. These methods, empirical likelihood, linearization, and that of Woodruff’s (1952), were applied to data on income obtained from the 2015 Mexican Intercensal Survey, and to simulated data. A response propensity model was used for adjusting the sampling weights, and the empirical performance of the methods was assessed in terms of the coverage of the confidence intervals through simulation studies. The empirical likelihood and linearization methods had a good performance for the mean, except when the variable of interest had some extreme values. For quantiles, the linearization method had a poor performance, while the empirical likelihood and Woodruff methods had a better one, though without reaching the nominal coverage when the variable of interest had values with high frequency near the quantile of interest.
Release date: 2022-01-06 - Articles and reports: 11-522-X201300014262Description:
Measurement error is one source of bias in statistical analysis. However, its possible implications are mostly ignored One class of models that can be especially affected by measurement error are fixed-effects models. By validating the survey response of five panel survey waves for welfare receipt with register data, the size and form of longitudinal measurement error can be determined. It is shown, that the measurement error for welfare receipt is serially correlated and non-differential. However, when estimating the coefficients of longitudinal fixed effect models of welfare receipt on subjective health for men and women, the coefficients are biased only for the male subpopulation.
Release date: 2014-10-31 - Articles and reports: 12-001-X19970013103Description:
This paper discusses the use of some simple diagnostics to guide the formation of nonresponse adjustment cells. Following Little (1986), we consider construction of adjustment cells by grouping sample units according to their estimated response probabilities or estimated survey items. Four issues receive principal attention: assessment of the sensitivity of adjusted mean estimates to changes in k, the number of cells used; identification of specific cells that require additional refinement; comparison of adjusted and unadjusted mean estimates; and comparison of estimation results from estimated-probability and estimated-item based cells. The proposed methods are motivated and illustrated with an application involving estimation of mean consumer unit income from the U.S. Consumer Expenditure Survey.
Release date: 1997-08-18
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Analysis (3)
Analysis (3) ((3 results))
- Articles and reports: 12-001-X202100200004Description:
This note presents a comparative study of three methods for constructing confidence intervals for the mean and quantiles based on survey data with nonresponse. These methods, empirical likelihood, linearization, and that of Woodruff’s (1952), were applied to data on income obtained from the 2015 Mexican Intercensal Survey, and to simulated data. A response propensity model was used for adjusting the sampling weights, and the empirical performance of the methods was assessed in terms of the coverage of the confidence intervals through simulation studies. The empirical likelihood and linearization methods had a good performance for the mean, except when the variable of interest had some extreme values. For quantiles, the linearization method had a poor performance, while the empirical likelihood and Woodruff methods had a better one, though without reaching the nominal coverage when the variable of interest had values with high frequency near the quantile of interest.
Release date: 2022-01-06 - Articles and reports: 11-522-X201300014262Description:
Measurement error is one source of bias in statistical analysis. However, its possible implications are mostly ignored One class of models that can be especially affected by measurement error are fixed-effects models. By validating the survey response of five panel survey waves for welfare receipt with register data, the size and form of longitudinal measurement error can be determined. It is shown, that the measurement error for welfare receipt is serially correlated and non-differential. However, when estimating the coefficients of longitudinal fixed effect models of welfare receipt on subjective health for men and women, the coefficients are biased only for the male subpopulation.
Release date: 2014-10-31 - Articles and reports: 12-001-X19970013103Description:
This paper discusses the use of some simple diagnostics to guide the formation of nonresponse adjustment cells. Following Little (1986), we consider construction of adjustment cells by grouping sample units according to their estimated response probabilities or estimated survey items. Four issues receive principal attention: assessment of the sensitivity of adjusted mean estimates to changes in k, the number of cells used; identification of specific cells that require additional refinement; comparison of adjusted and unadjusted mean estimates; and comparison of estimation results from estimated-probability and estimated-item based cells. The proposed methods are motivated and illustrated with an application involving estimation of mean consumer unit income from the U.S. Consumer Expenditure Survey.
Release date: 1997-08-18
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