In just about all models for systems development, the systems analysis and design phases are the most important segments of the development process that eventually leads to the creation of a required product, system, or service. In these two phases, conceptual modeling, which includes both data modeling and process modeling, is the most effective technique for depicting and sharing an understanding of the functional capabilities and limitations of the product/system/service design. Given that a high percentage of reported failures of developed systems can be linked to functional requirements errors, it is extremely important for analysts to ensure high quality of the conceptual models they develop in the early stages of systems development process. The quality of conceptual models depends on a number of factors. This research focused on attributes of the modeler and specifically examined how an individual’s cognitive style, task self-efficacy, and knowledge of application domain impact the quality of two types of conceptual models: data models and process models. Results of the research revealed that an individual’s cognitive style may relate to conceptual model quality. In addition, the research showed that self-efficacy may be a determinant of model quality. Application domain knowledge did not appear to play a role in quality of models produced by the participants in this study. This study was limited to novice conceptual modelers and therefore the results may not be generalizable to other groups of individuals.