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Analysis of Missing Data

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Missing Data

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

Abstract

In this chapter, I present older methods for handling missing data. I then turn to the major new approaches for handling missing data. In this chapter, I present methods that make the MAR assumption. Included in this introduction are the EM algorithm for covariance matrices, normal-model multiple imputation (MI), and what I will refer to as FIML (full information maximum likelihood) methods. Before getting to these methods, however, I talk about the goals of analysis.

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Notes

  1. 1.

    It is this random error that is missing from the data set imputed from the EM solution in the MVA module of SPSS (von Hippel 2004; this remains the case at least through version 20).

  2. 2.

    However, it is acceptable if variables are included in the imputation model that are not included in the analysis model.

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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). Analysis of Missing Data. In: Missing Data. Statistics for Social and Behavioral Sciences. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-4018-5_2

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