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
    DOI: 10.22004/ag.econ.149679
    as

    Download full text from publisher

    File URL: https://ageconsearch.umn.edu/record/149679/files/AAEA%20Implications%20of%20Survey%20Design%20Design%20for%20Missing%20Data%20Imputation%20Paper.pdf
    Download Restriction: no
    File URL: https://libkey.io/10.22004/ag.econ.149679?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    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)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. D'Antoni, Jeremy M. & Khanal, Aditya R. & Mishra, Ashok K., 2014. "Examining Labor Substitution: Does Family Matter for U.S. Cash Grain Farmers?," Journal of Agricultural and Applied Economics, Southern Agricultural Economics Association, vol. 46(2), pages 1-12, May.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kilic, Talip & Zezza, Alberto & Carletto, Calogero & Savastano, Sara, 2017. "Missing(ness) in Action: Selectivity Bias in GPS-Based Land Area Measurements," World Development, Elsevier, vol. 92(C), pages 143-157.
    2. Burns, Christopher & Prager, Daniel & Ghosh, Sujit & Goodwin, Barry, 2015. "Imputing for Missing Data in the ARMS Household Section: A Multivariate Imputation Approach," 2015 AAEA & WAEA Joint Annual Meeting, July 26-28, San Francisco, California 205291, Agricultural and Applied Economics Association.
    3. Morehart, Mitch & Milkove, Dan & Xu, Yang, 2014. "Multivariate Farm Debt Imputation in the Agricultural Resource Management Survey (ARMS)," 2014 Annual Meeting, July 27-29, 2014, Minneapolis, Minnesota 169401, Agricultural and Applied Economics Association.
    4. Gedikoglu, Haluk & Parcell, Joseph L., 2012. "Implications of Missing Data Imputation for Agricultural Household Surveys: An Application to Technology Adoption," 2012 Annual Meeting, August 12-14, 2012, Seattle, Washington 124333, Agricultural and Applied Economics Association.
    5. repec:jss:jstsof:45:i04 is not listed on IDEAS
    6. Satkartar K. Kinney & Jerome P. Reiter & Javier Miranda, 2014. "Improving The Synthetic Longitudinal Business Database," Working Papers 14-12, Center for Economic Studies, U.S. Census Bureau.
    7. Drechsler, Jörg & Reiter, Jerome P., 2011. "An empirical evaluation of easily implemented, nonparametric methods for generating synthetic datasets," Computational Statistics & Data Analysis, Elsevier, vol. 55(12), pages 3232-3243, December.
    8. Louis Anthony (Tony) Cox, Jr & Douglas A. Popken, 2008. "Overcoming Confirmation Bias in Causal Attribution: A Case Study of Antibiotic Resistance Risks," Risk Analysis, John Wiley & Sons, vol. 28(5), pages 1155-1172, October.
    9. David (David Patrick) Madden, 2012. "The relationship between low birthweight and socioeconomic status in Ireland," Working Papers 201214, School of Economics, University College Dublin.
    10. Klein Martin & Sinha Bimal, 2013. "Statistical Analysis of Noise-Multiplied Data Using Multiple Imputation," Journal of Official Statistics, Sciendo, vol. 29(3), pages 425-465, June.
    11. Upadhayay, Neha B. & Rocchetta, Silvia & Gupta, Shivam & Kamble, Sachin & Stekelorum, Rebecca, 2024. "Blazing the trail: The role of digital and green servitization on technological innovation," Technovation, Elsevier, vol. 130(C).
    12. McDonough, Ian K. & Millimet, Daniel L., 2017. "Missing data, imputation, and endogeneity," Journal of Econometrics, Elsevier, vol. 199(2), pages 141-155.
    13. repec:cup:judgdm:v:15:y:2020:i:5:p:798-806 is not listed on IDEAS
    14. Yajuan Si & Jerome P. Reiter, 2013. "Nonparametric Bayesian Multiple Imputation for Incomplete Categorical Variables in Large-Scale Assessment Surveys," Journal of Educational and Behavioral Statistics, , vol. 38(5), pages 499-521, October.
    15. Woodcock, Simon D. & Benedetto, Gary, 2009. "Distribution-preserving statistical disclosure limitation," Computational Statistics & Data Analysis, Elsevier, vol. 53(12), pages 4228-4242, October.
    16. Douadia Bougherara & Laurent Piet, 2018. "On the role of probability weighting on WTP for crop insurance with and without yield skewness," Working Papers hal-02790605, HAL.
    17. Simon Trimborn & Wolfgang Karl Härdle, 2015. "CRIX or evaluating Blockchain based currencies," SFB 649 Discussion Papers SFB649DP2015-048, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    18. Zhong, Hua & Hu, Wuyang, 2015. "Farmers’ Willingness to Engage in Best Management Practices: an Application of Multiple Imputation," 2015 Annual Meeting, January 31-February 3, 2015, Atlanta, Georgia 196962, Southern Agricultural Economics Association.
    19. Cain Polidano & Ha Vu, 2012. "Labour market impacts from disability onset," ANU Working Papers in Economics and Econometrics 2012-583, Australian National University, College of Business and Economics, School of Economics.
    20. repec:jss:jstsof:29:i09 is not listed on IDEAS
    21. Allen M. Featherstone & Timothy A. Park & Jeremy G. Weber, 2012. "Keeping ARMS relevant: extracting additional information from ARMS," Agricultural Finance Review, Emerald Group Publishing Limited, vol. 72(2), pages 233-246, July.
    22. Valentino Dardanoni & Giuseppe De Luca & Salvatore Modica & Franco Peracchi, 2012. "A generalized missing-indicator approach to regression with imputed covariates," Stata Journal, StataCorp LP, vol. 12(4), pages 575-604, December.
    23. Llano, Carlos & Pardo, Juan & Pérez-Balsalobre, Santiago & Pérez, Julián, 2023. "Estimating multicountry tourism flows by transport mode," Annals of Tourism Research, Elsevier, vol. 103(C).

    More about this item

    Keywords

    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.

    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 bibliographic 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.

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

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.