Institutional Research Data Analyst Clarkson University Potsdam, New York, United States
Session Abstract: What it covers: This poster presents a decision framework to estimate how institutional aid (as % of COA) affects both the probability of enrollment and expected tuition contribution, segmented by Expected Family Contribution (EFC) and program. Using a calibrated logistic modeling approach, we generate precise “what-if” probabilities for specified aid changes (e.g., ±5/10/15 pp) and translate them into clear, leadership-ready visualizations.
Why it matters: Aid policy is one of the few levers that directly moves headcount and net tuition—especially for tuition-dependent private institutions. The approach replaces rules of thumb with evidence-based scenario comparisons that support transparent, defensible allocation decisions.
Objectives: Enable attendees to (i) quantify segment-specific aid responsiveness, (ii) compare alternative aid scenarios on expected net tuition, and (iii) communicate findings to cabinet-level audiences with concise, decision-focused visuals.
