Missing data bias: Exactly how bad is pairwise deletion?

When conducting social science research, missing data are a ubiquitous practical difficulty (Anseel, Lievens, Scollaert, & Choragwicka, 2010; Peugh & Enders, 2004; Roth, 1994). Under missing data conditions, it is necessary for the data analyst to choose from among several available missing data techniques, including (a) listwise deletion, (b) pairwise deletion, (c) single imputation, (d) maximum likelihood (ML), and (e) multiple imputation (MI) approaches. That is, when facing missing data, abstinence is not an option-one of these missing data techniques must be used, and all are imperfect. Thus, the issue of selecting a missing data technique is a matter of choosing the lesser of evils.