IDEAS home Printed from https://ideas.repec.org/p/ags/aaea13/149679.html
   My bibliography  Save this paper

Implications of Survey Sampling Design for Missing Data Imputation

Author

Listed:
  • Gedikoglu, Haluk
  • Parcell, Joseph L.

Abstract

Previous 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.

Suggested Citation

  • Gedikoglu, Haluk & Parcell, Joseph L., 2013. "Implications of Survey Sampling Design for Missing Data Imputation," 2013 Annual Meeting, August 4-6, 2013, Washington, D.C. 149679, Agricultural and Applied Economics Association.
  • Handle: RePEc:ags:aaea13:149679
    as

    Download full text from publisher

    File URL: http://purl.umn.edu/149679
    Download Restriction: no

    References listed on IDEAS

    as
    1. 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.
    2. Mary Ahearn & David Banker & Dawn Marie Clay & Daniel Milkove, 2011. "Comparative Survey Imputation Methods for Farm Household Income," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 93(2), pages 613-618.
    3. 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.
    4. 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.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    Multiple Imputation; Sampling Based Estimation; Missing Data; Research Methods/ Statistical Methods;

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ags:aaea13:149679. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (AgEcon Search). General contact details of provider: http://edirc.repec.org/data/aaeaaea.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.