To reduce the cost of item writing and to enhance the flexibility of item presentation, items can be generated by item-cloning techniques. An important consequence of cloning is that it may cause variability on the item parameters. Therefore, a multilevel item response model is presented in which it is assumed that the item parameters of a three-parameter logistic model describing response behavior are sampled from a multivariate normal distribution associated with a parent item. In this approach to item calibration, only distributions of item parameters are estimated. Therefore, the savings in item calibration costs for the item cloning model are potentially enormous. A marginal maximum likelihood and a Bayesian item calibration procedure are formulated. Further, a two-stage item selection procedure for computerized adaptive testing is presented. First, a set of items cloned from the same parent item is selected to be optimal at the ability estimate. Second, a random item from this set is administered. Simulation studies illustrate the accuracy of the item pool calibration and ability estimation procedures. An appendix describes Bayes model estimates for the item cloning model. (Contains 21 references.) (Author/SLD)