Hostname: page-component-7b7567c8c-7pwfn Total loading time: 0 Render date: 2026-11-15T10:55:19.869Z Has data issue: false hasContentIssue false
  • English
  • Français

Adding Meaning to Regression

Published online by Cambridge University Press:  01 January 2026

Rein Taagepera
Rein Taagepera*
Affiliation:
Institute of Government, University of Tartu, Tartu EE-50090, EstoniaSchool of Social Sciences, University of California, Irvine CA 92697, USA
*
E-mail: rtaagepe@uci.edu
Get accessRights & Permissions [Opens in a new window]

Abstract

In any data analysis we should look for ability to predict and for connections to a broader comparative context. Our equations must not predict absurdities, even under extreme circumstances, if we want to be taken seriously as scientists. Poorly done linear regression analysis often does lead to absurd predictions. Fixed exponent and exponential patterns seem more prevalent in social nature than linear patterns. Before applying regression to two variables, graph them against each other, showing the borders of the conceptually allowed space and possible logical anchor points. Transform the data until anchor points and data points do fit a straight line which does not pierce conceptual ceilings or floors. During regression, consider symmetric regression, because Ordinary Least Squares y-on-x and x-on-y differ from each other and their slopes depend on the degree of scatter. After regression, look at the numerical values of parameters and ask what they tell us in a comparative context. When considering multivariable regression, pay more than lip service to Occam's Razor.

Keywords

allowed areasanchor pointsgraphinglinear regressionOccam's Razorsymmetric regression

Information

Type
Research
Information
European Political Science , Volume 10 , Issue 1 , March 2011, pp. 73 - 85
Copyright
Copyright © 2011 European Consortium for Political Research

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

Coleman, S., (2007) Testing theories with qualitative and quantitative predictions, European Political Science, 6(2): 124–133.CrossRefGoogle Scholar
Colomer, J.M., (2007) What other sciences look like, European Political Science, 6(2): 134–142.CrossRefGoogle Scholar
Dalton, R.J. and Shin, D.C. (2006), Citizens, Democracy, and Markets Around the Pacific Rim: Congruence Theory and Political Culture, Oxford: Oxford University PressCrossRefGoogle Scholar
Grofman, B., (2007) Toward a science of politics?, European Political Science, 6(2): 143–155.CrossRefGoogle Scholar
Kvålseth, T.O., (1985) Cautionary note about R 2, The American Statistician, 39: 279–285.Google Scholar
Levine, J.H. (1993), Exceptions Are the Rule: An Inquiry into Methods in the Social Sciences, Boulder, CO: Westview PressGoogle Scholar
Lijphart, A. (1999), Patterns of Democracy: Government Forms and Performance in Thirty-Six Countries, New Haven, CT: Yale University PressGoogle Scholar
Taagepera, R., (2007a) Why political science is not scientific enough: a symposium, European Political Science, 6(2): 111–113.CrossRefGoogle Scholar
Taagepera, R., (2007b) Predictive versus postdictive models, European Political Science, 6(2): 114–123.CrossRefGoogle Scholar
Taagepera, R. (2008), Making Social Sciences More Scientific: The Need for Predictive Models, Oxford: Oxford University PressCrossRefGoogle Scholar
Taagepera, R. (2009) ‘Logical models in social sciences: how to begin’, http://www.psych.ut.ee/stk/Beginners_Logical_Models.pdf.Google Scholar