The objective of this study is to investigate the relation between the number of items and attributes and to analyze the manner in which the different rates of missing data affect the model estimations based on the simulation data. A Q-matrix contains 24 items, and data are generated using four attributes. A dataset of n = 3,000 is generated by associating the first, middle, and final eight items in the Q-matrix with one, two, and three attributes, respectively, and 5%, 10%, and 15% of the data have been randomly deleted from the first, middle, and final eight-item blocks in the Q-matrix, respectively. Subsequently, imputation was performed using the multiple imputation (MI) method with these datasets, 100 replication was performed for each condition. The values obtained from these datasets were compared with the values obtained from the full dataset. Thus, it can be observed that an increase in the amount of missing data negatively affects the consistency of the DINA parameters and the latent class estimations. Further, the latent class consistency becomes less affected by the missing data as the number of attributes associated with the items increase. With an increase in the number of attributes associated with the items, the missing data in these items affect the consistency level of the g parameter (guess) less and the s parameter (slip) more. Furthermore, it can be observed from the results that the test developers using the cognitive diagnosis models should specifically consider the item-attribute relation in items with missing data.