This dissertation is aimed at investigating the impact of missing data and evaluating the performance of five selected methods for handling missing responses in the implementation of Cognitive Diagnostic Models (CDMs). The five methods are: a) treating missing data as incorrect (IN), b) person mean imputation (PM), c) two-way imputation (TW), d) response-function imputation (RF), and e) expectation-maximization algorithm imputation (EM). Using the deterministic inputs, noisy "and" gate (DINA) model, Study 1 examines the methods' performance across three studied missing data mechanisms missing at random (MAR), missing not at random (MNAR), and the MIXED mechanism along with design factors (missing rate, item discrimination power, and Q-matrix size). Study 2 extends the design of Study 1 and investigates the performance of the methods in the presence of Q-matrix misspecification. Results of Study 1 reveal that the selected methods performed differently in terms of the accuracy of item parameters and attribute profiles. With respect to item parameters, it is indicated that a) IN was not optimal across conditions; b) methods PM, RF, TW, and EM performed similarly to each other, and they all were superior to IN when the missing rate was low; c) TW, RF and EM performed similarly, and they were optimal in most conditions; d) PM showed unstable performance at high missing rates. With respect to attribute profiles, results revealed that EM performed better than the other methods, especially at a higher missing rate; whereas, PM performed no better or worse than the other methods across conditions, especially with a higher missing rate. Results of Study 2 reveal similar patterns of the methods' performance in the presence of Q-matrix misspecification except the fact that the performance of PM is greatly improved in conditions with high item discrimination due to the inflation effect. [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.]