Ignoring the Non-ignorables? Missingness and Missing Positions

Abstract

Missing or incomplete data on actors’ positions can cause significant problems in political analysis. Research on missing values suggests the use of multiple imputation methods rather than case deletion, but few studies have yet considered the non-ignorable problem - positions that are hidden for strategic purposes. We examine this problem and discuss the advantages and drawbacks of (i) multiple imputation as implemented in AMELIA; (ii) a computationally easy but, in the context of spatial modelling, straightforward measure of indifference and (iii) a conditional averaging algorithm, LDM, which seeks to reasonably fix actors’ positions in the policy space pre- and post-imputation. The analysis suggests that actors biased by the status quo strategically hide their more supportive positions. Although none of the existing methods - which produce quite different results - is perfectly suited for imputing hidden positions, LDM has the highest hit rate for the conjectured more supportive position.