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Research in Higher Education

, Volume 34, Issue 1, pp 41–54 | Cite as

An examination of freshmen to senior general education gains across a national sample of institutions with different general education requirements using a mixed-effect structural equation model

  • William E. Knight
AIR Forum Issue

Abstract

This study investigated differences in freshmen to senior student general education gains across institutions with varying patterns of general education requirements using a mixed-effect structural equation model. The subjects were 6,409 students at 34 nation-wide colleges and universities. Students attending institutions where less than 40 percent of undergraduate curricular requirements were devoted to general education and where there was not equal distribution of general education courses within the requirement were found to have significantly higher general education gains than did students who attended institutions where 40 percent or more of the undergraduate curriculum was devoted to general education and there was equal distribution of courses within the general education requirement.

Keywords

Equation Model Structural Equation Structural Equation Model Education Research General Education 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. Alwin, D.F., and Jackson, D.J. (1981). Applications of simultaneous factor analysis to the issues of factorial invariance. In D.J. Jackson and E.F. Borgatta (eds.),Factor Analysis and Measurement in Sociological Research: A Multi-dimensional Perspective (pp. 249–279). Studies in International Sociology, No. 21. Beverly Hills: Sage.Google Scholar
  2. Bagozzi, R.P., and Yi, Y. (1991). On the use of structural equation models in experimental designs: Two extensions.International Journal of Research in Marketing 8:125–140.Google Scholar
  3. Baird, L.L. (1988). Value added: Using student gains as yardstocks of learning. In C. Adelman (ed.),Performance and Judgement: Essays on Principles and Practice in the Assessment of College Student Learning (pp. 205–216). Washington, DC: United States Government Printing Office.Google Scholar
  4. Dressel, P., and Mayhew, L. (1954).General Education: Explorations in Evaluation. Westport, CT: Greenwood Press.Google Scholar
  5. Ecob, R. (1987). Applications of structural equation modeling to longitudinal educational data. In P. Cuttance and R. Ecob (eds.),Structural Equation Modeling by Example: Applications in Educational, Sociological, and Behavioral Research (pp. 138–159). Cambridge: Cambridge University Press.Google Scholar
  6. Forrest, A. (1982).Increasing Student Competence and Persistence: The Best Case for General Education. Iowa City, IA: American College Testing Program.Google Scholar
  7. Hayduk, L.A. (1987).Structural Equation Modeling with LISREL: Essentials and Advances. Baltimore: Johns Hopkins University Press.Google Scholar
  8. Hendrickson, L., and Jones, B. (1987). A study of longitudinal causal models comparing gain score analysis with structural equation approaches. In P. Cuttance and R. Ecob (eds.),Structural Equation Modeling by Example: Applications in Educational, Sociological, and Behavioral Research (pp. 86–106). Cambridge: Cambridge University Press.Google Scholar
  9. James, L. R., Mulaik, S. A., and Brett, J. M. (1982).Causal Analysis: Assumptions, Models, and Data. Beverly Hills, CA: Sage Publishers.Google Scholar
  10. Joreskog, K. G. (1971). Statistical analysis of sets of congeneric tests.Psychometrika 36:109–133.Google Scholar
  11. Joreskog, K. G., and Sorbom, D. (1976). Statistical models and methods for test-retest situations. In D. N. M. DeGruijter and L. J. van der Kamp (eds.),Advances in Psychological and Educational Measurement (pp. 135–157). New York: Wiley.Google Scholar
  12. Joreskog, K. G., and Sorbom, D. (1989).LISREL 7: A Guide to the Program and Applications (2nd ed.). Chicago: SPSS.Google Scholar
  13. Long, J. S. (1983).Covariance Structure Models: An Introduction to LISREL. Beverly Hills, CA: Sage.Google Scholar
  14. Marsh, H. W., Balla, J. R., and McDonald, R. P. (1988). Goodness-of-fit indices in confirmatory factor analysis: The effect of sample size.Psychological Bulletin 103:91–410.Google Scholar
  15. Marsh, H. W., and Grayson, D. (1990). Public/Catholic differences in the high school and beyond data: A multigroup structural equation modeling approach to testing mean differences.Journal of Educational Statistics 15:199–236.Google Scholar
  16. Muthén, B. O. (1989).Analysis of Longitudinal Data Using Latent Variable Models with Varying Parameters. Unpublished manuscript. Los Angeles: University of California Los Angeles, Graduate School of Education.Google Scholar
  17. Pace, C. (1984).Measuring the Quality of College Student Experiences. Los Angeles: University of California, Higher Education Research Institute.Google Scholar
  18. Pace, C. (1990).The Undergraduates: A Report on Their Activities and Progress in College in the 1980s. Los Angeles: University of California, Center for the Study of Evaluation.Google Scholar
  19. Pascarella, E. T. (1989). Methodological issues in assessing the outcomes of college. In C. Fincher (ed.),Assessing Institutional Effectiveness: Issues, Methods, and Management (pp. 19–32). Athens, GA: University of Georgia Press.Google Scholar
  20. Pascarella, E. T., and Terenzini, P. T. (eds.) (1991).How College Affects Students: Findings and Insights from Twenty Years of Research. San Francisco: Jossey-Bass.Google Scholar
  21. Pike, G. R. (1991). Using mixed-effect structural equation models to study student growth and development.Research in Higher Education 32: 499–524.Google Scholar
  22. Pike, G. R., Phillippi, R. H., Banta, T. W., Bensey, M. W., Milbourne, C. C., and Columbus, P. J. (1991, May).Freshmen to Senior Gains at the University of Tennessee, Knoxville. Paper presented at the Association for Institutional Research Forum, San Francisco.Google Scholar
  23. Sorbom, D. (1974). A general method for studying differences in factor means and factor structure between groups.British Journal of Mathematical and Statistical Psychology 28: 229–239.Google Scholar
  24. Winter, D., McClelland, D., and Stewart, A. (1981).A New Case for the Liberal Arts: Assessing Institutional Goals and Student Development. San Francisco: Jossey-Bass.Google Scholar

Copyright information

© Human Sciences Press, Inc. 1993

Authors and Affiliations

  • William E. Knight
    • 1
  1. 1.Center for the Study of Higher and Postsecondary EducationThe University of MichiganAnn Arbor

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