Asymmetry in fixed-precision M-CAT: Multidimensional selection versus marginal stoppping

Description

Standard implementations of a Multidimensional Computerized Adaptive Testing (M‐CAT) algorithm have item selection rules that are searching for items that optimize the Fisher information volume. A variable‐length M‐CAT would usually include a stopping rule requiring all dimensions being measured with a fixed minimum precision. In contrast to the inherently multidimensional selection rule, this stopping rule is defined at the marginal levels of the latent traits distribution: standard error smaller than a pre‐determined threshold value for each dimension. This asymmetry between selection rule and stopping rule leads to side‐effects that might not always be anticipated at first glance. We will first revisit and discuss the issue from a distribution and practical perspective, subsequently propose some work‐ arounds in the form of alternative selection rules, and elaborate on their effectivity to tackle the issue in practice.

Period	11-Jul-2018
Event title	International meeting of the Psychometric Society 2018
Event type	Conference
Location	New York, United StatesShow on map
Degree of Recognition	International