The purpose of this study was to determine if time intervals between various prospective student interactions with the institution during the admissions process are useful in predicting enrollment. With ever-changing prospective student behaviors, greater college choice, and the ease of online application and inquiry, institutions receive many more inquiries and applicants when compared with actual enrollments. For many institutions, the use of complex enrollment predictor models have served as a basis for determining which students are most likely to enroll and thus, significant research in the area of predictive enrollment has centered on finding specific variables or factors that can be tied to enrollment likelihood.

Research in enrollment prediction has focused on using the variables of gender, race, family income, parental educational obtainment, academic ability, achievement, tuition, financial aid, home location, and a variety of other similar demographic variables readily available on admissions applications and standardized test score reports. Although the importance of such variable types in the predictive process has been demonstrated in research, the current literature is void of significant research that seeks to understand an applicant's interactions with the institution as they maneuver through the admission process.

In the areas of commercial and retail consumer behavioral research, the measurement of such interactions has been studied specifically through the following areas: information acquisition activities, appreciable lag time between initiation of information search and purchase, and the purchase probability based on visits and purchases observed online. Using the empirical findings within consumer-based behavioral observation and purchase likelihood, the study attempted to translate these findings into a uniquely higher education setting that focuses on the applicant interactions with the institution and its value in predicting enrollment.

The design strategy for this study was a quantitative, ex post facto, nonexperimental design utilizing logistic regression analysis. To address the research problem, three research questions were asked: (1) Can the time intervals between inquiry and application be useful in predicting enrollment beyond what is predicted by current models? (2) Can the time intervals between application submission and admit decision be useful in predicting enrollment beyond what is predicted by current models? (3) Can the time intervals between various application supplemental submissions and the number of student initiated contacts be useful in predicting enrollment beyond what is predicted by current models?

The study observed the effect of time intervals of various applicant interactions between prospective students and the institution when combined with other traditional demographic and psychographic variables used in enrollment prediction. The traditional variables used in the study were race, gender, denominational affiliation, standardized test score, and state of residence. The time related variables used in this study were the date of application, date of inquiry, date of submission of transcripts, date of submission of a writing assessment, and the number of student initiated contacts with the institution. This study built on the current enrollment prediction literature by determining if the addition of time related variables to existing traditional variables within the predictor equation could better classify a chosen sample, than could otherwise be done with traditional variables alone.

The population consisted of 12,450 prospective students who applied for undergraduate admissions at a medium-sized, sectarian university located in the southeastern part of Virginia and the sample consisted of 4,098 applicants who applied between January 1, 2005, and December 31, 2007.

The studies findings confirmed that the time related variables of (a) number of days between date of inquiry and date of application, (b) number of days between date of application and date of admission, and (c) the number of days between the date of application and various supplemental application requirement submissions were all significant in predicting enrollment, p. <.05. The study also indicated that both the number of days between the date of application and the date of admission, and the number of student initiated contacts with the institution, were the variables with the strongest predictive capabilities as indicated by the Wald statistic.