Abstract
Sir Francis Galton (1885) introduced the idea of “regression” to the research community in a study examining the relationship of fathers’ and sons’ heights. In his study he observed that sons do not tend toward their fathers’ heights but instead “regress to” the mean of the population. He thus formulated the idea of “regression toward mediocrity”, and with the development of the method of least squares procedures by Carl Friedrich Gauss (Myers, 1990), multiple regression analysis using ordinary least squares procedures (OLS) has become one of the most common statistical techniques for investigating and modeling relationships among variables. Applications of regression occur in almost every field, and one can hardly pick up an issue of a higher education journal without running across at least one study in which OLS regression was the methodology of choice. Similarly, there is a plethora of work presented at the Association for the Study of Higher Education, Division J of the American Educational Research Association, and the Association for Institutional Research utilizing multiple regression techniques. Such widespread use of this powerful technique encourages us to revisit the basic principles underlying this “workhorse” of higher education research in an effort to identify ways in which it can be used to deliver more refined analyses, thereby further enhancing the credibility of our research.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Astin, A. W., (1993). What matters in college. San Francisco: Jossey-Bass.
Allison, P D. (1985). Event history analysis: Regression for longitudinal event data. Thousand Oaks: Sage Publications, Inc.
Belsey, D. A., Kuh, E. & Welsch, R. E. (1980). Regression diagnostics: identifying influential data and sources of multicollinearity. New York: Wiley.
Bohrnstedt, G. W. (1983). Measurement. In P. H. Rossi, J. D. Wright, & A. B. Anderson (Eds.) Handbook of survey research (pp. 69–121). New York: Academic Press.
Bryk, A. S. & Raudenbush, S. W. (1992). Hierarchical linear models. Newbury Park: SAGE Publications, Inc.
Chatterjee, S. & Price, B. (1977). Regression analysis by example. New York: Wiley.
Cohen, J. (1977). Statistical power analysis for the behavioral sciences (Rev. ed.). New York: Academic Press.
Cohen, J., and Cohen, P. (1983). Applied multiple regression/correlation analysis for the behavioral sciences, second edition. Hillsdale, NJ: Lawrence Erlbaum Associates.
Duncan, O. D. (1975). Introduction to structural equation models. New York: Academic Press.
Ethington, C. A. (1997). A Hierarchical linear modeling approach to studying college effects. In J. Smart (ed.), Higher education: Handbook of theory and research, XII (pp. 165–194). New York: Agathon Press.
Ezekiel, M., & Fox, K. A. (1959). Methods of correlation and regression analysis. New York: John Wiley & Sons.
Galton, F. (1885). Regression toward mediocrity in heredity stature. Journal of the Anthropological Institute, 15, 246-263.
Gordon, R. A. (1968). Issues in multiple regression. The American Journal of Sociology, 73, 592–616.
Hardy, M. A. (1993). Regression with dummy variables. Thousand Oaks, CA: Sage Publications, Inc.
Heck, R. H. & Thomas, S.L. (2000). An introduction to multilevel modeling. New Jersey: Erlbaum & Associates.
Hinkle, D. E., Wiersma, W., & Jurs, S. G. (1998). Applied statistics for the behavioral sciences, fourth edition. Boston: Houghton Mifflin.
Kerlinger, F. N. (1973). Foundations of behavioral research. New York: Holt, Rinehart and Winston.
Lewis-Beck, M. S. (1980). Applied regression: an introduction. Beverly Hills: Sage Publications, Inc.
Long, J. S. (1997). Regression models for categorical and limited dependent vanables. Thousand Oaks, CA: Sage Publications.
Lord, F. M. & Novick, M. R. (1968). Statistical theories of mental test scores. Reading, MA: Addison-Wesley.
Montgomery, D. C, & Peck, E. A. (1992). Introduction to linear regression analysis, second edition. New York: John Wiley & Sons.
Myers, R. H. (1990). Classical and modern regression with applications, second edition. Boston: PWS-KENT Publishing Co.
Neter, J., Wasserman, W., & Kutner, M. H. (1985). Appliedlinear statistical models: Regression, analysis of variance, and experimental designs (2nd Ed.). Homewood, IL: Richard D. Irwin.
Nunnally, J. (1978). Psychometric theory (2nd ed.). New York: McGraw-Hill.
Ostrom, C. W. (1990). Time series analysis regression techniques. Thousand Oaks, CA: Sage Publications, Inc.
Pedhazur, E.J. (1982). Multiple regression in behavioral research: Explanation and prediction. New York: Holt, Rinehart & Winston.
Stevens, J. (1996). Applied multivariate statistics for the social sciences, third edition. Mahwah, NJ: Lawrence Erlbaum Associates.
Thomas, S. L. & Heck, R.H. (2001). Analysis of large-scale secondary data in higher education: potential perils associated with complex sample designs. Research in Higher Education, 42, 517–540.
Thompson, B. (1995). Stepwise regression and stepwise discriminant analysis need not apply here: A guidelines editorial. Educational and Psychological Measurement, 55, 525–534.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2002 Springer Science+Business Media Dordrecht
About this chapter
Cite this chapter
Ethington, C.A., Thomas, S.L., Pike, G.R. (2002). Back to the Basics: Regression as It Should Be. In: Smart, J.C., Tierney, W.G. (eds) Higher Education: Handbook of Theory and Research. Higher Education: Handbook of Theory and Research, vol 17. Springer, Dordrecht. https://doi.org/10.1007/978-94-010-0245-5_6
Download citation
DOI: https://doi.org/10.1007/978-94-010-0245-5_6
Publisher Name: Springer, Dordrecht
Print ISBN: 978-0-87586-137-1
Online ISBN: 978-94-010-0245-5
eBook Packages: Springer Book Archive