Computerized Adaptive Testing

Compensatory vs. Non-Compensatory

Within multidimensional models, different dimension of latent traits can be compensatory or non-compensatory. In a compensatory model, the probability of a response depends on a linear combination of the latent traits. A high level on one latent trait can compensate for a low level on one of the other latent traits. In non-compensatory models, the probability of a response depends on the product or some nonadditive functions of the latent traits. A high level on one latent trait cannot compensate for a low level on one of the other latent traits.
It seems hard to say the which one is right since there are chances exist for both of them. For example, in GRE Verbal part, sometimes even we don't know the meaning of one sentence, we can still find the right answer since there is strong logical relation among different parts of one sentence. However, if one Chinese students are asked to answer one calculus equation in English, he will have much trouble if he can't understand the English even maybe he is very strong in calculus. Therefore, whether we choose compensatory or non-compensatory depends on the real condition. Also, the weight of different latent traits need be considered too. If the dimension of high priority is low, it will have critical influence on all final response.

posted by Shunkai Fu @ 8:17 PM