Teacher candidate performance assessments represent a promising source of data for evidence-based program improvement. However, teacher preparation programs (TPPs) interested in reform face a crucial question: how to identify actionable evidence in performance-assessment data. To address this concern, we propose a two-pronged empirical framework that TPPs can use to analyze performance-assessment data. The first approach, latent class analysis, creates profiles of instructional practice by grouping candidates together based on similarities in their performance-assessment scores. This can help TPPs provide targeted supports to candidates. The second approach, predictive validity analyses, estimates relationships between candidates' performance-assessment scores and their performance as teachers-of-record. This can help TPPs identify programmatic elements significantly related to teacher outcomes. We illustrate this framework with Educative Teacher Performance Assessment (edTPA) data from a Partner University and contend that the impact of performance assessments can be amplified by these common strategies for analyzing performance-assessment data.