A Comparison of Item Selection Techniques for Testlets

Abstract

This study examined the performance of the maximum Fisher’s information, the maximum posterior weighted information, and the minimum expected posterior variance methods for selecting items in a computerized adaptive testing system when the items were grouped in testlets. A simulation study compared the efficiency of ability estimation among the item selection techniques under varying conditions of local-item dependency when the response model was either the three-parameter-logistic item response theory or the three-parameter-logistic testlet response theory. The item selection techniques performed similarly within any particular condition, the practical implications of which are discussed within the article.