Missing data occur for a variety of reasons. Particularly problematic are t
hose items an individual skipped or could not reach in time. This article f
ocuses on the practical effects of using different statistical treatments w
ith omitted and not-reached items in an item response theory application. T
he particular strategy that is selected for scoring such items has a consid
erable impact on the interpretation of results. This impact is evident for
either individual results or group-level assessments.