Item Selection Methods for Computer Adaptive Testing With Passages

Computer adaptive testing (CAT) has been shown to shorten the test length and increase the precision of latent trait estimates. Oftentimes, test takers are asked to respond to several items that are related to the same passage. The purpose of this study is to explore three CAT item selection techniques for items of the same passages and to provide recommendations and guidance for item selection methods that yield better latent trait estimates. Using simulation, the study compared three models in CAT item selection with passages: (a) the testlet-effect model (T); (b) the passage model (P); and (c) the unidimensional IRT model (U). For the T model, the bifactor model with testlet-effect or constrained multidimensional IRT model was applied. For each of the three models, three procedures were applied: (a) no item exposure control; (b) item exposure control of rate 0.2 ; and (c) item exposure control of rate 1. It was found that the testlet-effect model performed better than passage or unidimensional models. The P and U models tended to overestimate the precision of the theta or latent trait estimates.