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Missing Data pp 111–131Cite as

Multiple Imputation and Analysis with SPSS 17-20

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Part of the book series: Statistics for Social and Behavioral Sciences ((SSBS))

Abstract

In this chapter, I provide step-by-step instructions for performing multiple imputation and analysis with SPSS 17-19. I encourage you to read Chap. 3 before reading this chapter.

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Notes

  1. 1.

    Please note that I am suggesting creating a dichotomous variable (xalc9) from the previously “continuous” variable, alc9. I suggest that here merely to illustrate the use of the logistic regression analysis with MI. For a variety of reasons, it is generally not acceptable simply to dichotomize continuous variables for this purpose, especially when the continuous variable has been imputed.

  2. 2.

    I do not consider myself to be an expert with binary logistic regression. This example is meant to be a simple example of using this procedure with multiply-imputed data sets.

  3. 3.

    Note that although the meaning of the R2 is the same in this context as it is for complete cases analysis (i.e., percent of variance accounted for), you should not use complete cases procedures for testing the significance of this R2 or R2-related quantities (e.g., R2-improvement).

References

  • Graham, J. W. (2009). Missing data analysis: making it work in the real world. Annual Review of Psychology, 60, 549–576.

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  • Graham, J. W., Olchowski, A. E., & Gilreath, T. D. (2007). How Many Imputations are Really Needed? Some Practical Clarifications of Multiple Imputation Theory. Prevention Science, 8, 206–213.

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  • Hansen, W. B., & Graham, J. W. (1991). Preventing alcohol, marijuana, and cigarette use among adolescents: Peer pressure resistance training versus establishing conservative norms. Preventive Medicine, 20, 414–430.

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  • Rubin, D.B. (1987). Multiple imputation for nonresponse in surveys. New York: Wiley.

    Book  Google Scholar 

  • von Hippel PT. 2004. Biases in SPSS 12.0 Missing Value Analysis. Am. Stat. 58:160–64

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Biobehavioral Health, The Pennsylvania State University, Health & Human Development Bldg. East, University Park, PA, USA

    John W. Graham

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  1. John W. Graham
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© 2012 Springer Science+Business Media New York

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Graham, J.W. (2012). Multiple Imputation and Analysis with SPSS 17-20. In: Missing Data. Statistics for Social and Behavioral Sciences. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-4018-5_5

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