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An Empirical Evaluation of the Predictive Mean Matching Method for Imputing Missing Values

Author

Listed:
  • LAWRENCE R. LANDERMAN

    (Duke University Medical Center)

  • KENNETH C. LAND

    (Duke University)

  • CARL F. PIEPER

    (Duke University Medical Center)

Abstract

This article reports empirical explorations of how well the predictive mean matching method for imputing missing data works for an often problematic variable—income—when income is used as an explanatory variable in a substantive regression model. It is found that the performance of the predictive mean method varies considerably with the predictive power of the imputation regression model and the percentage of cases with missing data on income. In comparisons of single-value with multiple-imputation methods, it also is found that the amount of bias and the loss of precision associated with single-value methods is considerably less than that associated with a weak imputation model. Situations in which using imputed data can lead to seriously biased estimates of regression coefficients (and related statistics) and situations in which the bias is so minimal as to be nonproblematic are identified.

Suggested Citation

  • Lawrence R. Landerman & Kenneth C. Land & Carl F. Pieper, 1997. "An Empirical Evaluation of the Predictive Mean Matching Method for Imputing Missing Values," Sociological Methods & Research, , vol. 26(1), pages 3-33, August.
    • Handle: RePEc:sae:somere:v:26:y:1997:i:1:p:3-33
    DOI: 10.1177/0049124197026001001
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    Cited by:

    1. 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.
    2. Harttgen, Kenneth & Klasen, Stephan, 2012. "A Household-Based Human Development Index," World Development, Elsevier, vol. 40(5), pages 878-899.
    3. Hu, Yanan & Yang, Yaqi & Wang, Chunyu & Tian, Maozai, 2017. "Imputation in nonparametric quantile regression with complex data," Statistics & Probability Letters, Elsevier, vol. 127(C), pages 120-130.
    4. Kim, HaeJung & Hopkins, Karen M., 2017. "The quest for rural child welfare workers: How different are they from their urban counterparts in demographics, organizational climate, and work attitudes?," Children and Youth Services Review, Elsevier, vol. 73(C), pages 291-297.
    5. Kristian Kleinke & Jost Reinecke, 2013. "Multiple imputation of incomplete zero-inflated count data," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 67(3), pages 311-336, August.
    6. Turney, Kristin, 2018. "Adverse childhood experiences among children of incarcerated parents," Children and Youth Services Review, Elsevier, vol. 89(C), pages 218-225.

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