Using Predictive Modeling to Target Student Recruitment: Results of a Replication By Gillian Butler Student Affairs Research & Information University of California, Davis

Replication of Controlled Experiments at SUNY Stony Brook Thomas, Reznik & Dawes

Similarities Public, highly selective (63% vs. 56%) Multi-campus system One application, low marginal cost to apply to additional campuses ($40 vs. $30) Similar yield (approximately 30%)

Fence – Sitters vs. Hot Prospects

Goal: To identify which students are most responsive to targeted recruitment efforts Construct a predictive model to estimate individual admits’ probability of enrolling Design a controlled recruitment experiment Assess the results of the experiment

Logistic Regression Model

UC Davis Model Correct predictions = 72%

Predicted and Actual Enrollment by Probability Range, Fall 2000

Distribution of Admits by Probability Band

Mean Combined SAT1 Score by Probability Band UCD Fall 2000 Freshman Social Science Admits

SUNY at Stony Brook 1998 Treatment: Additional invite to visit Two additional mailings to parents Expedited financial aid packaging Contact in financial aid marathons (p) of enrollment 30% – 90% Control group: n=819Experimental Group: n=326

SUNY at Stony Brook 1999 Treatment: Three additional mailings One-half contacted by telephone (p) of enrollment20% – 100% Control group: n=2442Experimental Group: n=700

UC Davis 2001 Treatment: Personalized invitation to lunch w/faculty from major interest area Recruitment CD Note: Social Science Admits Only (p) of enrollment 0% - 60% Control group: n = 3383Experimental group: n = 400

What’s wrong? Theory is incorrect Model was misspecified Size of experimental group was inadequate Level of treatment was insufficient Type of treatment was ineffective