Implications of Survey Sampling Design for Missing Data Imputation
AbstractPrevious studies that analyzed multiple imputation using survey data did not take into account the survey sampling design. The objective of the current study is to analyze the impact of survey sampling design missing data imputation, using multivariate multiple imputation method. The results of the current study show that multiple imputation methods result in lower standard errors for regression analysis than the regression using only complete observation. Furthermore, the standard errors for all regression coefficients are found to be higher for multiple imputation with taking into account the survey sampling design than without taking into account the survey sampling design. Hence, sampling based estimation leads to more realistic standard errors.
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Bibliographic InfoPaper provided by Agricultural and Applied Economics Association in its series 2013 Annual Meeting, August 4-6, 2013, Washington, D.C. with number 149679.
Date of creation: May 2013
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Multiple Imputation; Sampling Based Estimation; Missing Data; Research Methods/ Statistical Methods;
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- Reiter, Jerome P. & Raghunathan, Trivellore E., 2007. "The Multiple Adaptations of Multiple Imputation," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 1462-1471, December.
- Horton, Nicholas J. & Kleinman, Ken P., 2007. "Much Ado About Nothing: A Comparison of Missing Data Methods and Software to Fit Incomplete Data Regression Models," The American Statistician, American Statistical Association, vol. 61, pages 79-90, February.
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- Michael W. Robbins & T. Kirk White, 2011. "Farm Commodity Payments and Imputation in the Agricultural Resource Management Survey," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 93(2), pages 606-612.
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