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In many high-stakes testing programs, testlets are used to increase efficiency. Since responses to items belonging to the same testlet not only depend on the latent ability, but also on correct reading, understanding, and interpretation of the stimulus, the assumption of Local Independence does not hold. Testlet Response Theory models have been developed to deal with this dependency. For both logit and probit testlet models, a random testlet effect is added to the standard logit and probit IRT models. Even though this testlet effect might make the IRT models more realistic, application of these models in practice leads to new questions, for example in automated test assembly. In many test assembly models, goals have been formulated for the amount of information the test should provide about the candidates. The amount of Fisher Information is often maximized or it has to meet a pre-specified target. Since Testlet Response Theory models have a random testlet effect, Fisher Information contains a random effect as well. The question arises as to how this random effect in automated test assembly should be dealt with. A method based on robust optimization techniques for dealing with uncertainty in test assembly due to random testlet effects is presented. The method is applied in the context of a high-stakes testing program and the impact of this robust test assembly method is studied. Results are discussed, advantages of the use of robust test assembly are mentioned, and recommendations about the use of the new method are given.

Robust Automated Test Assembly for Testlet-Based Tests: An Illustration with Analytical Reasoning Items