Predicting Enrollment of New Criminal Justice Doctoral Programs

William E. Stone^1,

¹School of Criminal Justice, Texas State University San Marcos, Texas, USA

Cite this paper:
William E. Stone. Predicting Enrollment of New Criminal Justice Doctoral Programs. American Journal of Educational Research. 2015; 3(10):1208-1215. doi: 10.12691/education-3-10-1

Abstract

The study reports the results of an attempt to predict enrollment for a newly proposed doctoral program in criminology/criminal justice. The methodology used to create the enrollment projection was a differential equation utilizing a combination of survey data and existing archival data. The study compares the projection results to the first five years of actual enrollment in the program to validate the projection. While the enrollment projection was somewhat off in the first two years, in years three through five the projection was very successful. While this study focuses on a specific program, the methodology was successful and should be applicable to predicting enrollment in a wide range of programs where preexisting populations are not available to form a projection base.

Keywords:
population projection doctoral programs program justification

This work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

References:

[1]	Dickeson, R. C. (2010). Prioritizing Academic Programs and Services 2nd Ed. Jossey-Bass, Inc./John Wiley & Sons, Inc.
[2]	Clagett, C. (1992). “Enrollment Management.” In M. A. Whiteley, J. D. Porter, and R. H. Fenske (eds.), The Primer for Institutional Research. Tallahassee, Fla.: Association for Institutional Research.
[3]	Brinkman, P. T., & McIntyre, C. (1997). Methods and techniques of enrollment forecasting. In D.T. Layzell (Ed.) Forecasting and managing enrollment and revenue: an overview of current trends, issues, and methods (pp. 67-80)..
[4]	Lightfoot R. C., & Doerner W. G. (2008). “Student Success and Failure in a Graduate Criminology/Criminal Justice Program” American Journal of Criminal Justice. 33(1):113-129.
[5]	Weismann, J. (1994). “Enrollment Projections: Combining Statistics and Gut Feelings.” Journal of Applied Research in the Community College, 1994, 1 (2), 143-152.
[6]	Correa, H. (1967). A survey of mathematical models in educational planning. In Mathematical models in educational planning. http://files.eric.ed.gov/fulltext/ED024138.pdf#page=22.
[7]	Bush, R. R. & Mosteller, F. (1955). Stochastic Models of Learning. John Wiley & Son, New York, NY.
[8]	Restle, F. (1970). Theory of serial pattern learning: Structural trees. Psychological Review, 77,481-495.
[9]	Scandura, J. M. (1970). Development and evaluations of individualized materials for critical thinking based on logical inference. Rending Research. Acta Psychologica, 63, 301-345.
[10]	Bruggink, T. H., and Gambhir, V. (1996). Statistical models for college admission and enrollment: A case study for a selective liberal arts college. Research in Higher Education 37(2): 221-240.
[11]	Goenner, C.F., Pauls, K. (2006), “A predictive model of inquiry to enrollment”, Research in Higher Education, Vol. 47, No. 8, pp. 935-956.
[12]	Johnstone, J. N. (1974). Mathematical Models Developed for Use in Educational Planning, Review of Educational Research Spring pp. 177-201 American Educational Research Association.
[13]	Sisson, R. L. (1968). A hypothetical model of a school. Pennsylvania University, Fels Institute of Local and State Government. ERIC No. ED030978.
[14]	Pollard, A. H. (1970). Some Hypothetical models in systems of education. The Australian Journal of Statistics, 12, 79-81.
[15]	Kemeny, J. G., & Snell, J. L. Finite Markov chains. Princeton, New Jersey: D. Van Nostrand Co. Inc., 1960.
[16]	Kemeny, J. G., & Snell, J. L. Mathematical models in the social sciences. Boston, MA: Ginn Publishing Inc., 1962.
[17]	Harden, W. R. & Tcheng, M. T. (1971). Projection of enrollment distribution with enrollmentceilings by Markov processes. Socio-Economic Planning Sciences. 5, 467-473.
[18]	Dursun, D. (2012). Predicting Student Attrition with Data Mining Methods. Journal of College Student Retention, Vol. 13(1) 17-35, 2011-2012 Baywood Publishing Co., Inc.
[19]	Bowen, W. G., & Rudenstine, N. L. (1992). In pursuit of the Ph.D. Princeton, NJ: Princeton University Press.
[20]	Lightfoot R. C., & Doerner W. G. (2008). “Student Success and Failure in a Graduate Criminology/Criminal Justice Program” American Journal of Criminal Justice. 33(1):113-129.
[21]	Klyman, F. I. and Karman, T. A. (1974) A Perspective for Graduate-Level Education.Criminal Justice Crime & Delinquency 1974 20: 398.
[22]	Chen, K., Kennedy J. Kovacs, J. M. and Zhang C. (2007). A spatial perspective for predicting enrollm.ent in a regional pharmacy school. GeoJournal 70:133-143.