Scoring omitted and not-reached items: Practical data analysis implications

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.