Predicting MBA no-shows and graduation success with discriminate analysis

Gary Clayton and Tom Cate

International Advances in Economic Research, 2004, vol. 10, issue 3, 235-243

Abstract: This paper uses discriminate analysis to examine five years of MBA admission records in order to separate no-shows from the successful program graduates. The study used traditional numeric data such as age, length of time with current employer, undergraduate GPA and GMAT scores—as well as dummy variables for sex, full- or part-time status, race, the public or private nature of the undergraduate institution, and in-state tuition eligibility. The analysis correctly separated the no-shows with a 94.2 percent classification rate based entirely on the use of dummy variables. Unlike other studies, undergraduate GPAs, GMAT scores, and other numeric variables played no role in the final classification. The results suggest that more attention be given to the use of dummy variables when it comes to predicting the success of MBA program graduates. Copyright International Atlantic Economic Society 2004

Date: 2004
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://hdl.handle.net/10.1007/BF02296218 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:kap:iaecre:v:10:y:2004:i:3:p:235-243:10.1007/bf02296218

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11294

DOI: 10.1007/BF02296218

Access Statistics for this article

International Advances in Economic Research is currently edited by Katherine S. Virgo

More articles in International Advances in Economic Research from Springer, International Atlantic Economic Society Contact information at EDIRC.
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().