IDEAS home Printed from https://ideas.repec.org/p/mts/wpaper/200709.html
   My bibliography  Save this paper

Estimating Missing Values from the General Social Survey: An Application of Multiple Imputation

Author

Listed:
  • David A. Penn

Abstract

Objectives. Most researchers who use survey data must grapple with the problem of how best to handle missing information. This article illustrates multiple imputation, a technique for estimating missing values in a multivariate setting. Methods. I use multiple imputation to estimate missing income data and update a recent study that examines the influence of parents’ standard of living on subjective well-being. Using data from the 1998 General Social Survey, two ordered probit models are estimated; one using complete cases only, and the other replacing missing income data with multiple imputation estimates. Results. The analysis produces two major findings: 1) parents’ standard of living is more important than suggested by the complete cases model, and 2) using multiple imputation can help to reduce standard errors. Conclusions. Multiple imputation allows a researcher to use more of the available data, thereby reducing biases that may occur when observations with missing data are simply deleted.

Suggested Citation

  • David A. Penn, 2007. "Estimating Missing Values from the General Social Survey: An Application of Multiple Imputation," Working Papers 200709, Middle Tennessee State University, Department of Economics and Finance.
    • Handle: RePEc:mts:wpaper:200709
    as

    Download full text from publisher

    File URL: http://capone.mtsu.edu/berc/working/GSS3.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    Citations

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


    Cited by:

    1. Stephanie E. Clark-Reyna & Sara E. Grineski & Timothy W. Collins, 2016. "Ambient Concentrations of Metabolic Disrupting Chemicals and Children’s Academic Achievement in El Paso, Texas," IJERPH, MDPI, vol. 13(9), pages 1-16, September.
    2. Sara E. Grineski & Timothy W. Collins & Jayajit Chakraborty & Marilyn Montgomery, 2017. "Hazard Characteristics and Patterns of Environmental Injustice: Household‐Level Determinants of Environmental Risk in Miami, Florida," Risk Analysis, John Wiley & Sons, vol. 37(7), pages 1419-1434, July.
    3. Grineski, Sara & Collins, Tim & Renteria, Roger & Rubio, Ricardo, 2021. "Multigenerational immigrant trajectories and children's unequal exposure to fine particulate matter in the US," Social Science & Medicine, Elsevier, vol. 282(C).
    4. Jayajit Chakraborty & Timothy W. Collins & Sara E. Grineski & Alejandra Maldonado, 2017. "Racial Differences in Perceptions of Air Pollution Health Risk: Does Environmental Exposure Matter?," IJERPH, MDPI, vol. 14(2), pages 1-16, January.
    5. David Penn, 2009. "Financial well-being in an urban area: an application of multiple imputation," Applied Economics, Taylor & Francis Journals, vol. 41(23), pages 2955-2964.
    6. Juana Sanchez & Sydney Noelle Kahmann, 2017. "R&D, Attrition and Multiple Imputation in BRDIS," Working Papers 17-13, Center for Economic Studies, U.S. Census Bureau.
    7. Timothy W. Collins & Young-an Kim & Sara E. Grineski & Stephanie Clark-Reyna, 2014. "Can Economic Deprivation Protect Health? Paradoxical Multilevel Effects of Poverty on Hispanic Children’s Wheezing," IJERPH, MDPI, vol. 11(8), pages 1-18, August.
    8. Sara E. Grineski & Timothy W. Collins & Paola Chavez-Payan & Anthony M. Jimenez & Stephanie Clark-Reyna & Marie Gaines & Young-an Kim, 2014. "Social Disparities in Children’s Respiratory Health in El Paso, Texas," IJERPH, MDPI, vol. 11(3), pages 1-17, March.

    More about this item

    Keywords

    subjective well-being; financial well-being; multiple imputation;
    All these keywords.

    JEL classification:

    • A10 - General Economics and Teaching - - General Economics - - - General
    • C42 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Survey 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:mts:wpaper:200709. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Benjamin Jansen (email available below). General contact details of provider: https://edirc.repec.org/data/efmtsus.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.