IDEAS home Printed from https://ideas.repec.org/a/sae/somere/v41y2012i2p335-361.html
   My bibliography  Save this article

The Effects of Auxiliary Variables on Coefficient Bias and Efficiency in Multiple Imputation

Author

Listed:
  • Sarah Mustillo

Abstract

Current research on multiple imputation suggests that including auxiliary variables in the imputation model may increase the accuracy and efficiency of coefficient estimation, yet few studies have actually tested this principle for regression analysis. This article uses data from the 2008 General Social Survey to present results from simulations that vary in three respects: (a) three types of auxiliary variables (variables related to the mechanism of missingness, variables related to the variable/varaibles being imputed, and extraneous variables); (b) three levels of missing data (10 percent, 20 percent, and 30 percent missing); and (c) two assumptions of missing (missing completely at random and missing at random). Results show that the inclusion of any type of auxiliary variable does not appreciably impact the coefficient bias or efficiency in this simulation, regardless of the amount of missing data or the assumption of missing. Hence, the inclusion of auxiliary variables may not be necessary in many analytic situations.

Suggested Citation

  • Sarah Mustillo, 2012. "The Effects of Auxiliary Variables on Coefficient Bias and Efficiency in Multiple Imputation," Sociological Methods & Research, , vol. 41(2), pages 335-361, May.
    • Handle: RePEc:sae:somere:v:41:y:2012:i:2:p:335-361
    DOI: 10.1177/0049124112452392
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/0049124112452392
    Download Restriction: no
    File URL: https://libkey.io/10.1177/0049124112452392?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. Paul Zhang, 2005. "Multiple imputation of missing data with ante-dependence covariance structure," Journal of Applied Statistics, Taylor & Francis Journals, vol. 32(2), pages 141-155.
    2. Joseph L. Schafer, 2003. "Multiple Imputation in Multivariate Problems When the Imputation and Analysis Models Differ," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 57(1), pages 19-35, February.
    3. Horton N. J. & Lipsitz S. R., 2001. "Multiple Imputation in Practice: Comparison of Software Packages for Regression Models With Missing Variables," The American Statistician, American Statistical Association, vol. 55, pages 244-254, August.
    4. King, Gary & Honaker, James & Joseph, Anne & Scheve, Kenneth, 2001. "Analyzing Incomplete Political Science Data: An Alternative Algorithm for Multiple Imputation," American Political Science Review, Cambridge University Press, vol. 95(1), pages 49-69, March.
    5. Hakan Demirtas, 2004. "Simulation driven inferences for multiply imputed longitudinal datasets," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 58(4), pages 466-482, November.
    Full references (including those not matched with items on IDEAS)

    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. Christian Seiler, 2013. "Nonresponse in Business Tendency Surveys: Theoretical Discourse and Empirical Evidence," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 52.
    2. Ahmad R. Alsaber & Jiazhu Pan & Adeeba Al-Hurban, 2021. "Handling Complex Missing Data Using Random Forest Approach for an Air Quality Monitoring Dataset: A Case Study of Kuwait Environmental Data (2012 to 2018)," IJERPH, MDPI, vol. 18(3), pages 1-25, February.
    3. Scott Gehlbach & Konstantin Sonin & Ekaterina Zhuravskaya, 2010. "Businessman Candidates," American Journal of Political Science, John Wiley & Sons, vol. 54(3), pages 718-736, July.
    4. Ihle, R. & Amikuzuno, J. & von Cramon-Taubadel, S. & Zorya, S., 2010. "Grenzeffekte in der Marktintegration bei Mais in Ostafrika: Einsichten aus einem semi-parametrischen Regressionsmodell," Proceedings “Schriften der Gesellschaft für Wirtschafts- und Sozialwissenschaften des Landbaues e.V.”, German Association of Agricultural Economists (GEWISOLA), vol. 45, March.
    5. Matthew Blackwell & James Honaker & Gary King, 2017. "A Unified Approach to Measurement Error and Missing Data: Overview and Applications," Sociological Methods & Research, , vol. 46(3), pages 303-341, August.
    6. Vincent Bauer & Keven Ruby & Robert Pape, 2017. "Solving the Problem of Unattributed Political Violence," Journal of Conflict Resolution, Peace Science Society (International), vol. 61(7), pages 1537-1564, August.
    7. Paul Poast, 2013. "Issue linkage and international cooperation: An empirical investigation," Conflict Management and Peace Science, Peace Science Society (International), vol. 30(3), pages 286-303, July.
    8. Cohen, Joseph N, 2010. "Neoliberalism’s relationship with economic growth in the developing world: Was it the power of the market or the resolution of financial crisis?," MPRA Paper 24527, University Library of Munich, Germany.
    9. You, Jong-Sung & Khagram, Sanjeev, 2004. "Inequality and Corruption," Working Paper Series rwp04-001, Harvard University, John F. Kennedy School of Government.
    10. Sergei Guriev & Daniel Treisman, 2020. "The Popularity of Authoritarian Leaders: A cross-national investigation," Post-Print hal-03878626, HAL.
    11. Julia Cage & Yasmine Bekkouche, 2018. "The Price of a Vote: Evidence from France, 1993-2014," Sciences Po publications 12614, Sciences Po.
    12. Bruno Versailles, 2012. "Market Integration and Border Effects in Eastern Africa," Economics Series Working Papers WPS/2012-01, University of Oxford, Department of Economics.
    13. Antonio Filippin & Luca Nunziata, 2019. "Monetary effects of inequality: lessons from the euro experiment," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 17(2), pages 99-124, June.
    14. Robert Grafstein, 2009. "Antisocial Security: The Puzzle of Beggar‐Thy‐Children Policies," American Journal of Political Science, John Wiley & Sons, vol. 53(3), pages 710-725, July.
    15. Wurriehausen, Nadine & Ihle, Rico & Lakner, Sebastian, 2011. "The Integration of the Conventional and Organic Wheat Market," 2011 International Congress, August 30-September 2, 2011, Zurich, Switzerland 115784, European Association of Agricultural Economists.
    16. Sebastian Barfort & Nikolaj Harmon & Frederik Hjorth & Asmus Leth Olsen, 2015. "Dishonesty and Selection into Public Service in Denmark: Who Runs the World’s Least Corrupt Public Sector?," Discussion Papers 15-12, University of Copenhagen. Department of Economics.
    17. Manthos D. Delis & Iftekhar Hasan & Pantelis Kazakis, 2014. "Bank Regulations and Income Inequality: Empirical Evidence," Review of Finance, European Finance Association, vol. 18(5), pages 1811-1846.
    18. Alessandro Bitetto & Paola Cerchiello & Charilaos Mertzanis, 2021. "A data-driven approach to measuring epidemiological susceptibility risk around the world," DEM Working Papers Series 200, University of Pavia, Department of Economics and Management.
    19. Bekkouche, Yasmine & Cagé, Julia & Dewitte, Edgard, 2022. "The heterogeneous price of a vote: Evidence from multiparty systems, 1993–2017," Journal of Public Economics, Elsevier, vol. 206(C).
    20. Harris, J. Andrew & Posner, Daniel N., 2022. "Does decentralization encourage pro-poor targeting? Evidence from Kenya’s constituencies development fund," World Development, Elsevier, vol. 155(C).

    More about this item

    Keywords

    missing data; multiple imputation;

    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:sae:somere:v:41:y:2012:i:2:p:335-361. 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: SAGE Publications (email available below). General contact details of provider: .

    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.