An Examination of Discrepancies in Multiple Imputation Procedures Between SAS® and SPSS

Multiple imputation (MI) has become a feasible method to replace missing data due to the rapid development of computer technology over the past three decades. Nonetheless, a unique issue with MI hinges on the fact that different software packages can give different results. Even when one begins with...

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Bibliographic Details
Published in:The American statistician 2019-01, Vol.73 (1), p.80-88
Main Authors: Wang, Jianjun, Johnson, Dallas E.
Format: Article
Language:English
Subjects:
Algorithms
Default comparison
Missing data
Random numbers
Regression analysis
Result pooling
Software packages
Statistical methods
Statistics
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Summary:Multiple imputation (MI) has become a feasible method to replace missing data due to the rapid development of computer technology over the past three decades. Nonetheless, a unique issue with MI hinges on the fact that different software packages can give different results. Even when one begins with the same random number seed, conflicting findings can be obtained from the same data under an identical imputation model between SAS® and SPSS®. Consequently, as illustrated in this article, a predictor variable can be claimed both significant and not significant depending on the software being used. Based on the considerations of multiple imputation steps, including result pooling, default selection, and different numbers of imputations, practical suggestions are provided to minimize the discrepancies in the results obtained when using MI. Features of Stata® are briefly reviewed in the Discussion section to broaden the comparison of MI computing across widely used software packages.
ISSN:0003-1305
1537-2731
DOI:10.1080/00031305.2018.1437078