General diagnostic models (GDMs) and Bayesian networks are mathematical frameworks that cover a wide variety of psychometric models. Both extend latent class models, and while GDMs also extend item response theory (IRT) models, Bayesian networks can be parameterized using discretized IRT. The purpose of this study is to examine similarities and differences between GDMs and Bayesian networks using both simulated data and real test data sets. The performances of the two frameworks in data generation and estimation under various possible conditions are investigated. Several indices for accuracy and precision are examined as well as the agreement between the GDM and Bayesian network for simulated data and a real data set in this study. Both have problems with identifiability and high-level proficiency variables. Bayesian network slightly better with small samples and can use correlations among proficiency variables to stabilize estimates for scales with few items. [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.]