The preparation gap [Salehi et al., Phys. Rev. Phys. Educ. Res. 15, 020114 (2019)] refers to gaps in students' prior knowledge that can negatively affect their learning as they engage in introductory physics courses. To better characterize the gap, the current study distinguished the impact of various prior knowledge components on learning gains. Measured components came from within the course domain (e.g., energy and force, angular kinematics) and outside it (e.g., algebra, vectors, calculus, and scientific reasoning). We conducted the study in two different institutional contexts: An algebra-based course offered at a Northeastern State University (NESU) and a calculus-based course offered at a Midwestern Private University (MWPU). Furthermore, we defined three levels of physics learning outcome measures with increasing difficulty. Multiple regression analysis was used to predict learning gains with the various prior knowledge components as predictor variables. The results indicate that greater prior knowledge from both within and outside the domain predicted higher learning gains and explained 30%-50% of the variance in outcome measures. Predictive, in-domain prior knowledge was the same for both groups--i.e., prior knowledge of energy and force, as measured by the Mechanics Baseline Test [Hestenes and Wells, Phys. Teach. 30, 159 (1992)]. Predictive, outside-domain prior knowledge differed between the groups. Better scientific reasoning was highly predictive of learning in the NESU (algebra-based) group but did not predict learning in the MWPU (calculus-based) group. Math prior knowledge predicted learning in both groups, although different topics within the math domain. These results suggest that measuring distinguishable components of prior knowledge will better characterize the preparation gap in ways that can be informative to educators. Specifically, measuring multiple, distinct types of prior knowledge can indicate "which types" are leading to a preparation gap for some students, putting them at a disadvantage for learning, whereas measuring a single type of prior knowledge or measuring prior knowledge too coarsely (without distinguishing among types) cannot provide sufficient diagnostic power.