Item Selection Methods for Computer Adaptive Testing With Passages - Yao, Lihua (Frontiers in Psychology, 2019)
Computer adaptive testing (CAT) has been shown to shorten the test length and increase the precision of latent trait estimates. Oftentimes, a test taker are asked to respond to several items that are related to the same passage. This study is to explore three CAT item selection techniques for items of same passages and to provide recommendations and guidance for item selection methods that yield better latent trait estimates. It used simulation and compared three models in CAT item selection with passages: a) testlet-effect model (T); b) passage model(P); and c) unidimensional IRT model (U). For T model, the bifactor model with testlet-effect or constrained multidimensional IRT model is applied. For each of the three models, three procedures are 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 testlet-effect model perform better than passage or unidimensional model. The P and U model tended to overestimate the precision of the theta or latent trait estimates.