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

References

• Acock, A. C. (2005), “SAS, Stata, SPSS: A Comparison,” Journal of Marriage and Family, 67, 1093–1095.
   Web of Science ®Google Scholar
• Albright, J., and Marinova, D. (2010), “Estimating Multilevel Models Using SPSS, Stata, SAS, and R,” available at http://www.indiana.edu/∼statmath/stat/all/hlm/hlm.pdf.
   Google Scholar
• Allison, P. (2000), “Multiple Imputation for Missing Data: A Cautionary Tale,” available at http://www.ssc.upenn.edu/∼allison/MultInt99.pdf.
   Google Scholar
• ——— (2002), Missing Data, Thousand Oaks, CA: Sage.
   Google Scholar
• Barnard, J., and Rubin, D. (1999), “Small-Sample Degrees of Freedom with Multiple Imputation,” Biometrika, 86, 948–955.
   Web of Science ®Google Scholar
• Brand, J. P. L. (1999), “Development, Implementation, and Evaluation of Multiple Imputation Strategies for the Statistical Analysis of Incomplete Data Sets,” Ph.D. dissertation, Erasmus University, Rotterdam, The Netherlands.
   Google Scholar
• Gebregziabher, M. (2017), “Missing Data Course,” available at http://people.musc.edu/∼gebregz/BMTRY748/ MI http://%20for%20school%20performance%20data.sas.
   Google Scholar
• IBM (no date), “Does IBM SPSS Statistics Offer Barnard & Rubin's Corrected Small-Sample Degrees of Freedom Estimates with Multiple Imputation?” available at http://www-01.ibm.com/support/docview.wss?uid=swg21486054.
   Google Scholar
• ——— (2016), “SPSS Statistics 24 Algorithms,” available at ftp://public.dhe.ibm.com/software/analytics/…/IBM_SPSS_Statistics_Algorithms.pdf.
   Google Scholar
• ——— (no date), “IBM SPSS Statistics 24 Command Syntax Reference,” available at ftp://public.dhe.ibm.com/software/analytics/spss/documentation/statistics/24.0/en/client/Manuals/IBM_SPSS_Statistics_Command_Syntax_Reference.pdf.
   Google Scholar
• Institute for Digital Research and Education (2017), “Multiple Imputation in SAS Part 1,” available at https://stats.idre.ucla.edu/sas/seminars/multiple-imputation-in-sas/mi_new_1/.
   Google Scholar
• Kromme, J. (2017), “Python & R vs. SPSS & SAS,” available at http://www.theanalyticslab.nl/2017/03/18/python-r-vs-spss-sas/.
   Google Scholar
• Li, Q. (2017), “Mosaic Missing Data Workshop,” available at http://math2.uncc.edu/∼lqi/research.html.
   Google Scholar
• Li, K. H., Raghunathan, T. E., and Rubin, D. B. (1991), “Large-Sample Significance Levels from Multiply Imputed Data Using Moment-Based Statistics and an F Reference Distribution,” Journal of the American Statistical Association, 86, 1065–1073.
   Web of Science ®Google Scholar
• Little, R. J. A., and Rubin, D. B. (2002), Statistical Analysis with Missing Data (2nd ed.), Hoboken, NJ: Wiley.
   Google Scholar
• Meng, X. L., and Xie, X. (2014), “I Got More Data, My Model Is More Refined, But My Estimator Is Getting Worse! Am I Just Dumb?” Econometric Reviews, 33, 218–250.
   Web of Science ®Google Scholar
• Muenchen, R. (2013), “The Popularity of Data Analysis Software,” available at http://www.immagic.com/eLibrary/ARCHIVES/GENERAL/BLOGS/R130203M.pdf.
   Google Scholar
• Multiple Imputation in SPSS (2017), available at https://docgo.org/multiple-imputation-multiple-imputation-in-spss.
   Google Scholar
• Nguyen, C. D., Carlin, J. B., and Lee, K. J. (2017), “Model Checking in Multiple Imputation: An Overview and Case Study,” Emerging Themes in Epidemiology, 14, DOI: 10.1186/s12982-017-0062-6.
   PubMed Web of Science ®Google Scholar
• Rubin, D. B. (1987), Multiple Imputation for Nonresponse in Surveys, New York: Wiley.
   Google Scholar
• SAS Institute (2015), SAS/STAT 14.1 User's Guide, Gary, NC: Author.
   Google Scholar
• SPSS (2016), IBM SPSS Statistics 24 Algorithms, Armonk, NY: IBM Corporation.
   Google Scholar
• Stata (2015), Stata Multiple Imputation Reference Manual (Release 14), College Station, TX: Author.
   Google Scholar
• ——— (2017), Stata Multiple Imputation Reference Manual (Release 15), College Station, TX: Author.
   Google Scholar
• Van Buuren, S. (2007), “Multiple Imputation of Discrete and Continuous Data by Fully Conditional Specification,” Statistical Methods in Medical Research, 16, 219–242.
   PubMed Web of Science ®Google Scholar
• Van Ginkel, J. (2010), “Unexpected t-test df Value Following Multiple Imputation with PASW 18,” available at http://spssx-discussion.1045642.n5.nabble.com/unexpected-t-test-df-value-following-multiple-imputation-with-PASW-18-td3214135.html.
   Google Scholar
• Von Hippel, P. (2016), “The Number of Imputations Should Increase Quadratically with the Fraction of Missing Information,” available at https://arxiv.org/ftp/arxiv/papers/1608/1608.05406.pdf.
   Google Scholar
• Yuan, Y. C. (2005), Multiple Imputation for Missing Data: Concepts and New Developments, Rockville, MD: SAS Institute. available at https://www.researchgate.net/publication/228574397_Multiple_Imputation_for_Missing_Data_Concepts_and_New_Development.
   Google Scholar
• Zhang, P. (2003), “Multiple Imputation: Theory and Method,” International Statistical Review, 71, 581–592.
   Web of Science ®Google Scholar
• Zhou, X., and Reiter, J. (2010), “A Note on Bayesian Inference After Multiple Imputation,” available at http://www2.stat.duke.edu/∼jerry/Papers/tas10.pdf.
   Google Scholar