In many large-scale testing programs, examinees are frequently categorized into different performance levels. These classifications are then used to make high-stakes decisions about examinees in contexts such as in licensure, certification, and educational assessments. Numerous approaches to estimating the consistency and accuracy of this classification process have been developed under the CTT and UIRT frameworks. However, the multidimensional framework, particularly on the composite theta score metric, remains less explored. This dissertation was designed to explore the estimation of classification consistency and accuracy indices for composite summed and theta scores within the SS-MIRT framework. To achieve this goal, five prevalent approaches from the UIRT framework have been extended to the SS-MIRT context, including the Lee, Rudner, Guo, Bayesian EAP, and Bayesian MCMC approaches. These adapted approaches were then applied to two real data sets under various conditions. Further, a simulation study was conducted to evaluate the performance of the first four approaches under diverse testing scenarios, considering factors such as dimensionality, test length, and cut score location. The principal findings of this investigation include: (1) All five adapted approaches demonstrated commendable performance, with the estimation of classification indices exhibiting significant consistency in magnitude and pattern across different conditions; (2) Approaches using the MLE estimator generally showed higher ABIAS and RMSE, but lower SE compared to those employing the EAP estimator; (3) Approaches applied to the composite summed score metric typically resulted in higher ABIAS, SE, and RMSE than those for the composite theta score metric; (4) The D and M methods, assuming a multivariate standard normal distribution, yielded nearly identical outcomes, whereas the P method showed variations; (5) An increase in the correlation between dimensions and test length generally led to a decrease in ABIAS, SE, and RMSE. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com.bibliotheek.ehb.be/en-US/products/dissertations/individuals.shtml.]