The measurement efficiency of a multidimensional computerized adaptive testing (MCAT) can be improved by taking the correlations between the dimensions into account during the item selection and latent-trait estimation procedures (Segall, 1996; Wang & Chen, 2004). Although a multidimensional computerized classification test (MCCT), which was based on a multidimensional itemresponse model, was previously found more efficient than its unidimensional counterpart, the difference was negligible (Seitz & Frey, 2013); the researchers had adopted a sequential probability ratio test (SPRT) as the termination criterion in this MCCT study. To make a classification decision on each dimension, which is called a grid classification (Wang et al., 2019), only items that loaded on that dimension were used to calculate the likelihood ratio, which squandered the available information of the correlations between the dimensions. The current study utilizes such useful information to improve the measurement efficiency of the MCCT by applying a conditional distribution of the latent-trait estimates and then including all the administered items to calculate the likelihood ratio in the SPRT. The performance of this newly proposed method was evaluated through a series of simulation studies. The results showed that the proposed method can sizably improve the measurement efficiency of an MCCT by saving 1% to 32% of the test length in comparison with the SPRT when the two test dimensions are at least moderately correlated. The findings and further applications of this study are discussed.
Keywords: Conditional latent trait distribution; Multidimensional computerized classification test; Sequential probability ratio test.
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