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date: 16 December 2019

Abstract and Keywords

This chapter introduces the missing data methods currently in use and identifies the strengths and weaknesses of each of these methods. We first describe Rubin’s missing data theory and outline three missing data mechanisms: MCAR, MAR, and NMAR. Next, we describe a variety of missing data techniques and their requisite assumptions. These techniques included traditional missing data procedures such as deletion and single imputation as well as the modern missing data techniques of maximum likelihood and multiple imputation. We also discuss a few of the many options for an NMAR mechanism. In the final section, we discuss ways in which missing data may be purposefully incorporated into a research design to maximize available resources and minimize respondent burden.

Keywords: Missing data mechanisms, multiple imputation, maximum likelihood, planned missing data, missing at random, not missing at random

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