When to worry more: An empirical investigation of the effects of non-randomly missing data on regression analysis

TL;DRAbstract

Statistical remedies exist for most configurations of missing data, but these remedies require specific models and/or measures of nonresponse that are usually unavailable to the researcher. Consequently, the question of the conditions under which the threat to regression analysis posed by non-randomly missing data increases becomes relevant. This simulation study addresses that question empirically by assessing the effect of various configurations of non-randomly missing data on OLS regression analysis completed with different techniques for coping with the missing observations on samples drawn from varying populations. The configurations of missing data vary by which variable has missing observations, which variable drives the response mechanism, and the strength of the response mechanism. Five different techniques--listwise deletion, pairwise deletion, regression estimation without the addition of a residual, regression estimation with the addition of a residual, and EM estimation--f

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