title: Forecasting veterinary school admission probabilities for undergraduate student profiles.
abstract: Increased competition for veterinary school admission has created a need to determine whether individual students are likely to be successful candidates for veterinary school admission early in their undergraduate careers. Students invest considerable time and money in pre-veterinary courses of study, hoping for acceptance into professional veterinary school. A forecasting model was developed to predict the likelihood of students with particular characteristics gaining acceptance. Characteristics such as gender, age, size of high school, and ACT, are known upon entrance into college and can be used to determine the likelihood of an individual's being accepted. Data were gathered from the Louisiana State University College of Veterinary Medicine (LSU-CVM) admissions for all students applying to veterinary school for the classes of 2006 through 2008 from the top two agricultural programs in the state in terms of quantity of applicants to veterinary school: Louisiana State University and Louisiana Tech University. A one-way ANOVA was used to examine whether there were any statistical differences between known demographic and performance variables and acceptance into veterinary school. A logit forecasting model was then estimated to predict the likelihood of gaining acceptance into veterinary school based only on variables known early in the student's undergraduate career. Age, gender, and ACT scores were determined to be important variables in determining the likelihood of gaining admission. Overall, the forecasting model is of use in assigning probabilities of acceptance into veterinary school for specific student profiles, which can assist in one-on-one assistance from advisor to student.
authors.affiliation: Department of Agricultural Sciences, Louisiana Tech University, Ruston, LA 71272, USA. swatson@latech.edu
authors.forename: William H
authors.initials: WH
authors.lastname: Green
authors.forename: Susan E
authors.initials: SE
authors.lastname: Watson
authors.forename: Gary A
authors.initials: GA
authors.lastname: Kennedy
authors.forename: Claire A
authors.initials: CA
authors.lastname: Miceli
authors.forename: Joseph
authors.initials: J
authors.lastname: Taboada
mesh_terms: D000704:Analysis of Variance; D004520:Education, Veterinary; D004521:Educational Measurement; D005544:Forecasting; D006801:Humans; D016013:Likelihood Functions; D008145:Louisiana; D012570:School Admission Criteria / Q000706:statistics & numerical data / Q000639:trends; D012582:Schools, Veterinary / Q000592:standards / Q000706:statistics & numerical data*; D013334:Students / Q000523:psychology* / Q000706:statistics & numerical data*