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

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  • 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 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," Social Science Quarterly, Southwestern Social Science Association, vol. 88(2), pages 573-584, June.
    • Handle: RePEc:bla:socsci:v:88:y:2007:i:2:p:573-584
    DOI: 10.1111/j.1540-6237.2007.00472.x
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    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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).
    7. 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.
    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

    JEL classification:

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

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