Model uncertainty in education and social sciences is addressed using Bayesian stacking, a method that doesn't assume a true model exists. Bayesian hierarchical stacking improves on this by adding uncertainty to the model weights. In a comparison with other methods, Bayesian hierarchical stacking performs as well as conventional stacking when no true model is assumed.