The continuous effect loglinear diagnostic classification model (CE-LCDM) is an attempt to combine elements of Item Response Theory with Diagnostic Classification Modeling. This would theoretically allow models to both scale and classify students while providing more accurate classification main effects. The model will be compared against the standard LCDM and evaluated using a simulation study and an empirical analysis. The simulation will analyze the effects of number of attributes, sample size, continuous effect weight, and number of items per attribute. The LCDM is a very robust model that maintained better accuracy and reliability throughout; however, model selection criteria tended to favor the CE-LCDM. In the empirical analysis, all the criteria favored the CE-LCDM. These findings suggest that there may actually be a continuous effect present in most real data that can and should be taken into consideration for real-world application.