Stephen L. DesJardins

An event history model of student departure

Abstract This study uses a modeling technique often used in economics and other disciplines but rarely applied to educational research. The technique, known as event history modeling, is used to examine the temporal dimensions of student departure from a large research university. This approach allows researchers to remedy analytic problems often found when standard statistical procedures are used to study longitudinal events such as student departure from college. The study confirms most of the substantive findings of earlier research but we document that key explanatory variables have differential effects over time. Pinpointing the times at which students are at risk of leaving college will permit more efficient intervention strategies which could reduce the social, institutional, and individual costs often associated with leaving college before degree completion.

Adding a Timing Light to the “Tool Box”

This study uses the National Center for Education Statistics postsecondary transcript file of the High School and Beyond/Sophomore Cohort to replicate the findings of Adelmans Answers in the Tool Box study. We use event history modeling to provide additional information about how a number of factors affect time to bachelors degree attainment. Using a technique specifically designed to study temporal processes allows us to fully utilize the longitudinal nature of the transcript file data. We concur with many of Adelmans findings, although in several important instances our results differ from his.

ASSESSING THE EFFECTS OF CHANGING INSTITUTIONAL AID POLICY

Since 1994, enrollments of nonresident students have been declining at The University of Iowa. Since nonresident students pay over three times as much tuition as resident students, have historically comprised 35 to 40% of each falls incoming class, and the enrollment declines are disproportionately among high-ability students, this trend is particularly troubling. In an attempt to reverse this decline, administrators decided to offer a new scholarship to selected nonresident students. This article demonstrates how the effects of this policy proposal were modeled in an attempt to provide decision makers with timely and useful information.

Artificial Neural Networks: A New Approach to Predicting Application Behavior.

In this article we examine how predictive modeling can be used to study application behavior. We apply a relatively new technique, artificial neural networks, to help us predict which students are likely to apply to a large Research I institution in the Midwest. We compare the results of these new techniques to the traditional analysis tool, logistic regression modeling. The addition of artificial intelligence models is an exciting new area and this article encourages other institutional researchers to use this technique to explore the complex processes found in our educational institutions.

Understanding and Using Efficiency and Equity Criteria in the Study of Higher Education Policy

Policy makers often have to make decisions about how resources are to be used in furthering societal objectives. Because resources are scarce, one of the objectives that decision-makers strive for is to allocate these resources among alternative uses in an efficient manner. Another objective that public policymakers often pursue is to distribute scarce resources among individuals and groups in an equitable manner. In their analysis and deliberation, policy makers often view “efficiency” in allocation and “equity” in distribution as criteria that guide them in their decision-making.2