The incorporation of prerequisite skill structures into educational systems helps to identify the order in which concepts should be presented to students to optimize student achievement. Many skills have a causal relationship in which one skill must be presented before another, indicating a strong skill relationship. Knowing this relationship can help to predict student performance and identify prerequisite arches. Skill relationships, however, are not directly measurable; instead, the relationship can be estimated by observing differences of student performance across skills. Such methods of estimation, however, seem to lack a baseline model to compare their effectiveness. If two methods of estimating the existence of a relationship yield two different values, which is the more accurate result? In this work, we propose a method of comparing models that attempt to measure the strength of skill relationships. With this method, we begin to identify those student-level covariates that provide the most accurate models predicting the existence of skill relationships. Focusing on interactions of performance across skills, we use our method to construct models to predict the existence of five strongly-related and five simulated poorly-related skill pairs. Our method is able to evaluate several models that distinguish these differences with significant accuracy gains over a null model, and provides the means to identify that interactions of student mastery provide the most significant contributions to these gains in our analysis. [For the full proceedings, see ED592609.]