Affiliations: Research and Statistics, Federal Reserve Board, Mail Stop 153, Washington, DC 20551, USA. E-mail: Arthur.Kennickell@frb.gov
Abstract: Prevention of errors in survey data must always be among out highest ideals,
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
preventing or limiting there, 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 [4]) 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, using various
combinations of fully edited and unedited data from the 2010 Survey of
Consumer Finances (SCF). The paper also explores the potential importance of
contamination of the imputation process under selective editing. While
editing has its direct effect on individual data items, it also alters the
set of information used in imputing the missing values that result from the
unwillingness or inability of respondents to provide answers or from the
resetting of values to missing during the editing process. The results of
the paper support a selective approach to editing and they indicate that any
resulting contamination of imputation is relatively minor in the case of the
SCF.
