01341nas a2200145   4500008003900000245008200039210006900121300001200190490000700202520087300209100001601082700001901098700002101117856005701138       2004                          d00aAdaptive Testing With Regression Trees in the Presence of Multidimensionality0 aAdaptive Testing With Regression Trees in the Presence of Multid  a293-3160 v293 a<p>It is unrealistic to suppose that standard item response theory (IRT) models will be appropriate for all the new and currently considered computer-based tests. In addition to developing new models, we also need to give attention to the possibility of constructing and analyzing new tests without the aid of strong models. Computerized adaptive testing currently relies heavily on IRT. Alternative, empirically based, nonparametric adaptive testing algorithms exist, but their properties are little known. This article introduces a nonparametric, tree-based algorithm for adaptive testing and shows that it may be superior to conventional, IRT-based adaptive testing in cases where the IRT assumptions are not satisfied. In particular, it shows that the tree-based approach clearly outperformed (one-dimensional) IRT when the pool was strongly two-dimensional.</p>
1 aYan, Duanli1 aLewis, Charles1 aStocking, Martha  uhttp://jeb.sagepub.com/cgi/content/abstract/29/3/293