  In statistical survey analysis, (partial) non-responders are integral
elements during data acquisition. Treating missing values during data
preparation and data analysis is therefore a non-trivial underpinning. Focusing
on different data sets from the Federal Statistical Office of Germany
(DESTATIS), we investigate various imputation methods regarding their
imputation accuracy. Since the latter is not uniquely determined in theory and
practice, we study different measures for assessing imputation accuracy: Beyond
the most common measures, the normalized-root mean squared error (NRMSE) and
the proportion of false classification (PFC), we put a special focus on
(distribution) distance- and association measures for assessing imputation
accuracy. The aim is to deliver guidelines for correctly assessing
distributional accuracy after imputation. Our empirical findings indicate a
discrepancy between the NRMSE resp. PFC and distance measures. While the latter
measure distributional similarities, NRMSE and PFC focus on data
reproducibility. We realize that a low NRMSE or PFC seem not to imply lower
distributional discrepancies. Although several measures for assessing
distributional discrepancies exist, our results indicate that not all of them
are suitable for evaluating imputation-induced differences.
