Modern Sequential Analysis and Its Applications to Computerized Adaptive Testing
The classical procedure for classifying which category examinees belong to by the sequential probability ratio test is to compute the ratio of likelihood of indifference region. To obtain upper bound and lower bound (A and B) by pre-specified type I error and type II error. Then, the ratio aforementioned is compared with A and B. Thus, the decision can be determined. However, the nominal value alpha only can be reached when the test-lengthen is variable. Therefore, such situation of a test to have no more than N items, which is so called “truncated SPRT (TSPRT)”, would not meet the nominal value we specified previously. To resolve above problem, equation (3), (4) and (6) have to be revised and the values of A, B, and C have to be determined by Monte Carlo simulation. Two scenarios (with/without content balance and exposure control) and three procedures (TSPRT, modTSPRT, and modHP: the first one is traditional procedure, and the other two procedures are modified one) were adopted in a series of simulations. The results illustrated that modified SPRT can sustain the nominal value and modHP has the best performance on average test-length.
Comments, Questions and Future Study
1) The present paper is to apply modified SPRT on computerized adaptive testing and it also involves classification (decision making). In past studies, researchers usually adopted constant cut-point ( θ 0 ) on SPRT, here the author proposed to use estimated theta to replace constant θ 0 . Indeed, it’s a good idea to adopt estimated for item selection and improving inflation of type one error. However, it seems that estimated theta works much better with the ACI procedure. Hence, using such combination we don't worry that the problem of inflation of type error.
Reference
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Chang, Y. (2005). Application of sequential interval estimation to adaptive mastery testing. Psychometrika, 70, 685-713.
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