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Multiple Imputation for Missing Data

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  • PAUL D. ALLISON

    (University of Pennsylvania)

Abstract

Two algorithms for producing multiple imputations for missing data are evaluated with simulated data. Software using a propensity score classifier with the approximate Bayesian bootstrap produces badly biased estimates of regression coefficients when data on predictor variables are missing at random or missing completely at random. On the other hand, a regression-based method employing the data augmentation algorithm produces estimates with little or no bias.

Suggested Citation

  • Paul D. Allison, 2000. "Multiple Imputation for Missing Data," Sociological Methods & Research, , vol. 28(3), pages 301-309, February.
    • Handle: RePEc:sae:somere:v:28:y:2000:i:3:p:301-309
    DOI: 10.1177/0049124100028003003
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    Citations

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    Cited by:

    1. A.Y. Kombo & H. Mwambi & G. Molenberghs, 2017. "Multiple imputation for ordinal longitudinal data with monotone missing data patterns," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(2), pages 270-287, January.
    2. Qian, Hang, 2012. "Essays on statistical inference with imperfectly observed data," ISU General Staff Papers 201201010800003618, Iowa State University, Department of Economics.
    3. Katherine I. Tierney, 2019. "Abortion Underreporting in Add Health: Findings and Implications," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 38(3), pages 417-428, June.
    4. Juan C. Garibay, 2018. "Beyond Traditional Measures of STEM Success: Long-Term Predictors of Social Agency and Conducting Research for Social Change," Research in Higher Education, Springer;Association for Institutional Research, vol. 59(3), pages 349-381, May.
    5. Mossakowski, Krysia N., 2008. "Is the duration of poverty and unemployment a risk factor for heavy drinking?," Social Science & Medicine, Elsevier, vol. 67(6), pages 947-955, September.
    6. M. D. R. Evans & Jonathan Kelley & Clayton D. Peoples, 2010. "Justifications of Inequality: The Normative Basis of Pay Differentials in 31 Nations," Social Science Quarterly, Southwestern Social Science Association, vol. 91(s1), pages 1405-1431.
    7. Migali, Giuseppe & Zucchelli, Eugenio, 2017. "Personality traits, forgone health care and high school dropout: Evidence from US adolescents," Journal of Economic Psychology, Elsevier, vol. 62(C), pages 98-119.
    8. P. B. Kenfac Dongmezo & P. N. Mwita & I. R. Kamga Tchwaket, 2017. "Imputation Based Treatment Effect Estimators," Journal of Statistical and Econometric Methods, SCIENPRESS Ltd, vol. 6(3), pages 1-2.
    9. Pongelli, Claudia & Calabrò, Andrea & Basco, Rodrigo, 2019. "Family firms' international make-or-buy decisions: Captive offshoring, offshore outsourcing, and the role of home region focus," Journal of Business Research, Elsevier, vol. 103(C), pages 596-606.
    10. David A. Wagstaff & Ofer Harel, 2011. "A closer examination of three small-sample approximations to the multiple-imputation degrees of freedom," Stata Journal, StataCorp LP, vol. 11(3), pages 403-419, September.
    11. Lei Yang & Xianyi Wu, 2013. "Estimation of Dirichlet process priors with monotone missing data," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 25(4), pages 787-807, December.
    12. Neil G. Bennett & Hsien-Hen Lu & Younghwan Song, 2002. "Welfare Reform and Changes in the Economic Well-Being of Children," NBER Working Papers 9399, National Bureau of Economic Research, Inc.
    13. Humera Razzak & Christian Heumann, 2019. "Hybrid Multiple Imputation In A Large Scale Complex Survey," Statistics in Transition New Series, Polish Statistical Association, vol. 20(4), pages 33-58, December.
    14. Rulloni, Valeria & Bustos, Oscar & Flesia, Ana Georgina, 2012. "Large gap imputation in remote sensed imagery of the environment," Computational Statistics & Data Analysis, Elsevier, vol. 56(8), pages 2388-2403.

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