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Multiple Imputation Using SAS Software

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  • Yuan, Yang

Abstract

Multiple imputation provides a useful strategy for dealing with data sets that have missing values. Instead of filling in a single value for each missing value, a multiple imputation procedure replaces each missing value with a set of plausible values that represent the uncertainty about the right value to impute. These multiply imputed data sets are then analyzed by using standard procedures for complete data and combining the results from these analyses. No matter which complete-data analysis is used, the process of combining results of parameter estimates and their associated standard errors from different imputed data sets is essentially the same. This process results in valid statistical inferences that properly reflect the uncertainty due to missing values. This paper reviews methods for analyzing missing data and applications of multiple imputation techniques. This paper presents the SAS/STAT MI and MIANALYZE procedures, which perform inference by multiple imputation under numerous settings. PROC MI implements popular methods for creating imputations under monotone and nonmonotone (arbitrary) patterns of missing data, and PROC MIANALYZE analyzes results from multiply imputed data sets.

Suggested Citation

  • Yuan, Yang, 2011. "Multiple Imputation Using SAS Software," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 45(i06).
    • Handle: RePEc:jss:jstsof:v:045:i06
    DOI: http://hdl.handle.net/10.18637/jss.v045.i06
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    Cited by:

    1. Joost Ginkel & Pieter Kroonenberg, 2014. "Using Generalized Procrustes Analysis for Multiple Imputation in Principal Component Analysis," Journal of Classification, Springer;The Classification Society, vol. 31(2), pages 242-269, July.
    2. Gago, Cristina M. & Jurkowski, Janine & Beckerman-Hsu, Jacob P. & Aftosmes-Tobio, Alyssa & Figueroa, Roger & Oddleifson, Carly & Mattei, Josiemer & Kenney, Erica L. & Haneuse, Sebastien & Davison, Kir, 2022. "Exploring a theory of change: Are increases in parental empowerment associated with healthier weight-related parenting practices?," Social Science & Medicine, Elsevier, vol. 296(C).
    3. Cheryl Zlotnick & Laura Dryjanska & Suzanne Suckerman, 2020. "The Association Between Acculturation Variables and Life Satisfaction Among Israeli Immigrants from Four English-Speaking Countries," Journal of Happiness Studies, Springer, vol. 21(4), pages 1427-1444, April.
    4. repec:jss:jstsof:45:i01 is not listed on IDEAS
    5. Lu Wang & Amy van Grieken & Junwen Yang-Huang & Eline Vlasblom & Monique P L'Hoir & Magda M Boere-Boonekamp & Hein Raat, 2018. "Relationship between socioeconomic status and weight gain during infancy: The BeeBOFT study," PLOS ONE, Public Library of Science, vol. 13(11), pages 1-15, November.
    6. Berk Benlioğlu & Fatih Demirel & Aras Türkoğlu & Kamil Haliloğlu & Hamdi Özaktan & Sebastian Kujawa & Magdalena Piekutowska & Tomasz Wojciechowski & Gniewko Niedbała, 2024. "Insights into Drought Tolerance of Tetraploid Wheat Genotypes in the Germination Stage Using Machine Learning Algorithms," Agriculture, MDPI, vol. 14(2), pages 1-18, January.

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