Skip to main content
Log in

Does High School Matter? An Analysis of Three Methods of Predicting First-Year Grades

  • Published:
Research in Higher Education Aims and scope Submit manuscript

Abstract

This research evaluated the usefulness of 3 approaches for predicting college grades: (a) traditional regression models, (b) high-school-effects models, and (c) hierarchical linear models. Results of an analysis of the records of 8,764 freshmen at a major research university revealed that both the high-school-effects model and the hierarchical linear model were more accurate predictors of freshman GPA than was the traditional model, particularly for lower ability students. Counter to expectations, the hierarchical linear model was not more accurate than the high school effects model.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

REFERENCES

  • Adelman, C. (1999). Answers in the Tool Box: Academic Intensity, Attendance Patterns, and Bachelor's Degree Attainment. Washington, DC: U. S. Government Printing Office.

    Google Scholar 

  • Alwin, D. F., and Otto, L. B. (1977). High school context effects on aspirations. Sociology of Education 50: 259-273.

    Google Scholar 

  • American Council on Education (1996). Remedial Education: An Undergraduate Student Profile. Washington, DC: Author.

    Google Scholar 

  • Baird, L. L. (1984). Predicting predictability: the influence of student and institutional characteristics on the prediction of grades. Research in Higher Education 21: 261-278.

    Google Scholar 

  • Baird, L. L. (1985). Do grades and tests predict adult accomplishment? Research in Higher Education 23: 3-85.

    Google Scholar 

  • Bloom, B. S., and Peters, F. R. (1961). The Use of Academic Prediction Scales for Counseling and Selecting College Entrants. New York: The Free Press of Glencoe.

    Google Scholar 

  • Bryk, A. S., and Raudenbush, S. W. (1992). Hierarchical Linear Models: Applications and Data Analysis Methods. Newbury Park, CA: Sage.

    Google Scholar 

  • Burstein, L. (1980a). The analysis of multilevel data in educational research and evaluation. In D. Berliner (ed.), Review of Research in Education, Vol. 8, pp. 158-233. Washington, DC: American Educational Research Association.

    Google Scholar 

  • Burstein, L. (1980b). The role of levels of analysis in the specification of education effects. In R. Dreeben and J. A. Thomas (eds.), The Analysis of Educational Productivity, Volume I: Issues in Microanalysis, pp. 119-190. Cambridge, MA: Ballinger Publishing.

    Google Scholar 

  • Cabrera, A. F., Nora, A., and Castañeda, M. B. (1993). College persistence: structural modeling of an integrated model of student retention. Journal of Higher Education 64: 123-139.

    Google Scholar 

  • Creaser, J. W. (1965). Predicting college success from equated high school ranks: a cross-validation study. College and University 41: 96-100.

    Google Scholar 

  • Eimers, M. T., and Pike, G. R. (1997). Minority and nonminority adjustment to college: differences or similarities? Research in Higher Education 38: 77-97.

    Google Scholar 

  • Eno, D., McLaughlin, G. W., Sheldon, P., and Brozovsky, P. (1999). Predicting freshman success based on high school record and other measures. AIR Professional File, No. 72: 1-9.

  • Ethington, C. A. (1997). A hierarchical linear modeling approach to studying college effects. In J. Smart (ed.), Higher Education: Handbook of Theory and Research, Vol. 12, pp. 165-194. New York: Agathon.

    Google Scholar 

  • Ethington, C. A., Thomas, S. L., and Pike, G. R. (in press). Back to the basics: regression as it should be. In J. Smart (ed.), Higher Education: Handbook of Theory and Research, Vol. 17. New York: Agathon.

  • Evans, W. N., and Schwab, R. M. (1995). Finishing high school and starting college: do Catholic schools make a difference? Quarterly Journal of Economics 110: 941-974.

    Google Scholar 

  • Ewell, P. T., and Jones, D. P. (1991, July). Assessing and Reporting Student Progress: A Response to the "New Accountability." Denver, CO: National Center for Higher Education Management Systems.

    Google Scholar 

  • Fishman, J. A. (1957). 1957 Supplement to College Board Scores, No. 2. New York: College Entrance Examination Board.

    Google Scholar 

  • Kellogg Commission on the Future of State and Land-Grant Universities (1997, April). Returning to Our Roots: The Student Experience. Washington, DC: National Association of State Universities and Land-Grant Colleges.

    Google Scholar 

  • Kulik, C., Kulik, J., and Shwalb, B. (1983). College programs for high-risk and disadvantaged students: a meta-analysis of findings. Review of Educational Research 53: 397-414.

    Google Scholar 

  • Lee, V. E., and Bryk, A. S. (1989). A multilevel model of the social distribution of high school achievement. Sociology of Education 62: 172-192.

    Google Scholar 

  • Lee, V. E., Bryk, A. S., and Smith, J. B. (1993). The organization of effective secondary schools. In L. Darling-Hammond (ed.), Review of Research in Education, Vol. 19, pp. 171-267. Washington, DC: American Educational Research Association.

    Google Scholar 

  • Lee, V. E., Smerdon, B. A., Alfeld-Liro, C., and Brown, S. L. (2000). Inside large and small high schools: curriculum and social relations. Educational Evaluation and Policy Analysis 22: 147-172.

    Google Scholar 

  • Marsh, H. W. (1987). The big-fish little-pond effect on academic self-concept. Journal of Educational Psychology 79: 280-295.

    Google Scholar 

  • Marsh, H. W. (1991). Failure of high-ability high schools to deliver academic benefits commensurate with their students' ability levels. American Educational Research Journal 28: 445-480.

    Google Scholar 

  • Mathiasen, R. L. (1984). Predicting college academic achievement: a research review. College Student Journal 18: 380-386.

    Google Scholar 

  • Mouw, J., and Khanna, R. (1993). Prediction of academic success: a review of the literature and some recommendations. College Student Journal 27: 328-336.

    Google Scholar 

  • Neal, D. (1997). The effect of Catholic secondary schooling on educational attainment. Journal of Labor Economics 15: 98-123.

    Google Scholar 

  • Noble, J., Davenport, M., Schiel, J., and Pommerich, M. (1999). Relationships Between Noncognitive Characteristics, High School Coursework and Grades, and Test Scores of ACT-Tested Students. ACT Research Report Series, 99-4. Iowa City, IA: American College Testing Program.

    Google Scholar 

  • Noble, J., and Sawyer, R. (1987). Predicting Grades in Specific College Freshman Courses from ACT Test Scores and Self-Reported High School Grades. ACT Research Report Series, 87-20. Iowa City, IA: American College Testing Program.

    Google Scholar 

  • Noble, J., and Sawyer, R. (1997). Alternative methods for validating admission and course placement criteria. AIR Professional File, No. 63: 1-9.

  • Odell, C. W. (1927). Attempt at predicting success in freshman year at college. School and Society 25: 702-706.

    Google Scholar 

  • Pascarella, E. T., and Terenzini, P. T. (1991). How College Affects Students: Findings and Insights from Twenty Years of Research. San Francisco: Jossey-Bass.

    Google Scholar 

  • Pike, G. R. (1991). The effect of background, coursework, and involvement on students' grades and satisfaction. Research in Higher Education 32: 15-30.

    Google Scholar 

  • Porter, S. R., and Umbach, P. D. (2001). Analyzing faculty workload data using multilevel modeling. Research in Higher Education 42: 171-196.

    Google Scholar 

  • Ramist, L., Lewis, C., and McCamley, L. (1990). Implications of using freshman GPA as the criterion for the predictive validity of the SAT. In W. Willingham, C. Lewis, R. Morgan, and L. Ramist (eds.), Predicting College Grades: An Analysis of Institutional Trends Over Two Decades, pp. 253-288. Princeton, NJ: Educational Testing Service.

    Google Scholar 

  • Ramist, L., Lewis, C., and McCamley-Jenkins, L. (1993). Student Group Differences in Predicting College Grades: Sex, Language, and Ethnic Groups. College Board Report 93-1. New York: The College Board.

    Google Scholar 

  • Raudenbush, S. W., and Bryk, A. S. (1988). Methodological advances in analyzing the effects of schools and classrooms on student learning. In E. Rothkopf (ed.), Review of Research in Education, Vol. 15, pp. 423-477. Washington, DC: American Educational Research Association.

    Google Scholar 

  • Reitz, W. (1934). Prediction college achievement with marks and ranks adjusted for inter-high school variability. Bulletin of the American Association of College Registrars 10: 162-181.

    Google Scholar 

  • Rummel, R. J. (1970). Applied Factor Analysis. Evanston, IL: Northwestern University Press.

    Google Scholar 

  • Sander, W. (2000). Catholic high schools and homework. Educational Evaluation and Policy Analysis 22: 299-311.

    Google Scholar 

  • Sander, W., and Krautmann, A. C. (1995). Catholic high schools, dropout rates, and educational attainment. Economic Inquiry 33: 217-233.

    Google Scholar 

  • Segal, D. (1934). Prediction of Success in College (Bulletin no. 15, U. S. Office of Education). Washington, DC: U. S. Government Printing Office.

    Google Scholar 

  • Stapleton, L. M., and Lissitz, R. W. (1999, March). Evaluating faculty salary equity using hierarchical linear modeling. Paper presented at the annual meeting of the American Educational Research Association, Montreal, Canada.

  • Tinto, V. (1975). Dropout from higher education: a theoretical synthesis of recent research. Review of Educational Research 45: 89-125.

    Google Scholar 

  • Toops, H. A. (1933). The transmutation of marks. Ohio College Association Bulletin, No. 88: 1093-2000.

  • Travers, R. M. W. (1949). Significant research on the prediction of academic success. In W. T. Donahue, C. H. Coomb, and R. M. W. Travers (eds.), The Measurement of Student Adjustment and Achievement, pp. 147-190. Ann Arbor: University of Michigan Press.

    Google Scholar 

  • Tucker, L. R. (1963). Formal Models for a Central Prediction System (Psychometric Monograph no. 10). Richmond, VA: William Byrd Press.

    Google Scholar 

  • Williford, L. E. (1996, October). The freshman year: How do personal factors influence academic success and persistence? 1996 SAIR Annual Report: Charting the Course in a Changing Environment. Mobile, AL: Southern Association for Institutional Research and Southern Region of the Society for College and University Planning.

    Google Scholar 

  • Willingham, W. (1985). Success in College: The Role of Personal Qualities and Academic Ability. New York: The College Board.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Gary R. Pike.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Pike, G.R., Saupe, J.L. Does High School Matter? An Analysis of Three Methods of Predicting First-Year Grades. Research in Higher Education 43, 187–207 (2002). https://doi.org/10.1023/A:1014419724092

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1023/A:1014419724092

Navigation