In many applications of high- and low-stakes ability tests, a non-negligible amount of respondents may fail to reach the end of the test within the specified time limit. Since for respondents that ran out of time some item responses will be missing, this raises the question of how to best deal with these missing responses for the purpose of obtaining an optimal assessment of ability. Commonly, researchers consider three general solutions: ignore the missing responses, treat them as being incorrect, or treat the responses as missing but model the missingness mechanism. This paper approaches the issue of dealing with not reached items from a measurement perspective, and considers the question what the operationalization of ability should be in maximum performance tests that work with effective time limits. We argue that the target ability that the test attempts to measure is maximum performance when operating at the test-indicated speed, and that the test instructions should be taken to imply that respondents should operate at this target speed. The phenomenon of the speed-ability trade-off informs us that the ability that is measured by the test will depend on this target speed, as different speed levels will result in different levels of performance on the same set of items. Crucially, since respondents with not reached items worked at a speed level lower than this target speed, the level of ability that they have been able to display on the items that they did reach is higher than the level of ability that they would have displayed if they had worked at the target speed (i.e., higher than their level on the target ability). Thus, statistical methods that attempt to obtain unbiased estimates of the ability as displayed on the items that were reached will result in biased estimates of the target ability. The practical implications are studied in a simulation study where different methods of dealing with not reached items are contrasted, which shows that current methods result in biased estimates of target ability when a speed-ability trade-off is present. The paper concludes with a discussion of ways in which the issue can be resolved.
This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 14 June 2018.
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Outputs of similar age from Frontiers in Psychology
#387
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Altmetric has tracked 23,073,835 research outputs across all sources so far. This one is in the 42nd percentile – i.e., 42% of other outputs scored the same or lower than it.
So far Altmetric has tracked 30,422 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 12.5. This one has gotten more attention than average, scoring higher than 58% of its peers.
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We're also able to compare this research output to 674 others from the same source and published within six weeks on either side of this one. This one is in the 42nd percentile – i.e., 42% of its contemporaries scored the same or lower than it.
