Dirty and Unknown: Statistical Editing and Imputation in the SCF
Arthur Kennickell
Federal Reserve Board
Prevention of errors in surveys must always be the highest ideal, but in such a complex process as a survey there are limits on what is achievable, because of cost, the absence of strong instruments for control or the emergence of unforeseen outcomes. Thus, effort must be devoted to identifying errors, remediating them, and designing better means of prevention or limitation where that is possible. Editing is typically a key instrument of identification and remediation. However, editing can consume very substantial resources and because the outcome is unlikely to be perfect, the very act itself introduces additional risks to data quality. For these reasons, it has been argued (e.g., de Waal, 2013) that a selective approach to editing, focused as squarely as possible on the core analytical goal of a survey may be more appropriate than detailed review of all survey observations. For surveys supporting multiple uses, particularly ones involving multivariate analysis, there may be a need for a somewhat broader focus, but a more efficient approach may still be possible in such cases. This paper evaluates various approaches to selective editing.