Missing Data Theory

In this first chapter, I accomplish several goals. First, building on my 20+ years of work on missing data analysis, I outline a nomenclature or system for talking about the theory underlying the modern analysis of missing data. I intend for this nomenclature to be in plain English, but nevertheless to be an accurate representation of statistical theory relating to missing data analysis. Second, I describe many of the main components of missing data theory, including the causes or mechanisms of missingness. Two general methods for handling missing data, in particular multiple imputation (MI) and maximum-likelihood (ML) methods, have developed out of the missing data theory I describe here. And as will be clear from reading this book, I fully endorse these methods. For the remainder of this chapter, I challenge some of the commonly held beliefs relating to missing data theory and missing data analysis, and make a case that the MI and ML procedures, which have started to become mainstream in statistical analysis with missing data, are applicable in a much larger range of contexts that typically believed.

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