Research in Higher Education

, Volume 36, Issue 1, pp 73–88 | Cite as

Segmenting student markets with a student satisfaction and priorities survey

  • Victor M. H. Borden
Article

Abstract

A market segmentation analysis was conducted on students at a large midwestern urban university using two forms of hierarchical cluster analysis on student characteristics: an agglomerative procedure using a matching-type association measure and a divisive chi-square-based automatic interaction detection (CHAID). The resulting segments were compared for their ability to distinguish among students according to six satisfaction scales and measures of students' priorities for college study derived from a general satisfaction survey. As expected, the CHAID clusters discriminated better among students according to their several measures of satisfaction, one of which was the criterion variable for the analysis. However, both procedures produced differences across only two of six satisfaction scales. The matching-type measure clusters resulted in significant differences on 11 of 18 college study priority items compared to only 6 of 18 for the CHAID clusters. Final discussion describes the usefulness of market segmentation strategies for planning, evaluating, and improving academic and student support programs.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Aldenderfer, M. S., and R. K. Blashfield (1984).Cluster Analysis. Quantitative Applications in the Social Sciences, No. 44. Beverly Hills: Sage.Google Scholar
  2. Astin, A. W. (1985).Achieving Educational Excellence. San Francisco: Jossey-Bass.Google Scholar
  3. Beder, H. (1986). Basic concepts and principles of marketing. In H. Beder (ed.).Marketing Continuing Education. New Directions for Continuing Education, No. 31, pp. 3–17.Google Scholar
  4. Bonoma, T. V., and B. P. Shapiro (1983).Segmenting the Industrial Market. Lexington, MA: D. C. Heath & Company.Google Scholar
  5. Borden, V. M. H., and K. Gentemann (1993). Campus community and student priorities at a metropolitan university. Paper presented at the 33rd Annual Forum of the Association for Institutional Research. Chicago, Illinois, May 16–19 (ERIC Document Reproduction Service No. ED 360 290).Google Scholar
  6. Cowles, D., and F. Franzak (1991). Divide and conquer: Applying the marketing concept of “segmentation” to the placement function.Journal of Career Planning and Employment 51(3): 59–63.Google Scholar
  7. Dillon, W. R., and M. Goldstein (1984).Multivariate Analysis: Methods and Applications. New York: Wiley.Google Scholar
  8. Goldgehn, L. A. (1989). Admissions standards and the use of key marketing techniques by United States colleges and universities.College and University 65(1): 44–55.Google Scholar
  9. Grabowski, S. M. (1981).Marketing in Higher Education. AAHE-ERIC Research Report, no. 5. Washington, DC.Google Scholar
  10. Grunig, J. E. (1990). Focus on your audience.Currents 16(2): 36–39.Google Scholar
  11. Hartigan, J. A. (1975).Clustering Algorithms. New York: John Wiley & Sons.Google Scholar
  12. Jacoby, B. (1990). Adapting the institution to meet the needs of commuter students.Metropolitan Universities, Summer 1990, pp. 61–71.Google Scholar
  13. Lay, R. S., and J. J. Maguire (1983). Computer aided segmentation analysis: New software for college admissions marketing.Journal of College Admissions 101: 32–36.Google Scholar
  14. Merante, J. A. (1982). Successful student recruitment using direct marketing and information technology.CAUSE-EFFECT 5(1): 18–22.Google Scholar
  15. Muffo, J. A. (1987). Market segmentation in higher education: A case study.Journal of Student Financial Aid 17(3): 31–40.Google Scholar
  16. Rickman, C. A., and G. Green (1993). Market segmentation differences using factors of college selection.College and University 8(1): 32–37.Google Scholar
  17. Sokal, R., and P. Sneath (1963).Principles of Numerical Taxonomy. San Francisco: W. H. Freeman.Google Scholar
  18. Sonquist, J. A. and J. N. Morgan (1964).The Detection of Interaction Effects. Monograph No. 35, Institute for Social Research, University of Michigan.Google Scholar
  19. Tinto, V. (1975). Dropout from higher education: A theoretical synthesis of recent research.Review of Educational Research 45: 89–125.Google Scholar
  20. Wakstein, J. (1987). Identifying market segments. In R. S. Lay and J. J. Endo (eds).Designing and Using Market Research, New Directions for Institutional Research, No. 54. San Francisco Jossey-Bass, pp. 91–101.Google Scholar
  21. Ward, J. (1963). Hierarchical grouping to optimize an objective function.Journal of the American Statistical Association 58: 236–244.Google Scholar
  22. Zemsky, R., and P. Oedel (1983).The Structure of College Choice. New York: College Entrance Examination Board.Google Scholar

Copyright information

© Human Sciences Press, Inc. 1995

Authors and Affiliations

  • Victor M. H. Borden
    • 1
  1. 1.Office of Information Management and Institutional ResearchIndianapolis

Personalised recommendations