Hostname: page-component-cd9895bd7-gbm5v Total loading time: 0 Render date: 2024-12-26T05:36:06.634Z Has data issue: false hasContentIssue false
  • English
  • Français

What to Do (and Not Do) with Multicollinearity in State Politics Research

Published online by Cambridge University Press:  25 January 2021

Kevin Arceneaux  and
Gregory A. Huber
Kevin Arceneaux
Affiliation:
Temple University
Gregory A. Huber
Affiliation:
Yale University
Get access Rights & Permissions [Opens in a new window]

Abstract

State politics scholars often confront data situations where the explanatory variables in a model are highly related to each other. Such multicollinearity (“MC”) makes it difficult to identify the independent effect that each of these variables has on the outcome of interest. In an effort to circumvent MC, researchers sometimes drop collinear variables from the regression model. Using simulated data, we demonstrate the implications that MC has for statistical estimation and the potential for introducing bias that the omitting-variables approach generates. We also discuss MC in the context of multiplicative interaction models, using research on the influence of the initiative on policy responsiveness as an applied example. We conclude with advice for researchers faced with MC in their datasets.

Type
The Practical Researcher
Information
State Politics & Policy Quarterly , Volume 7 , Issue 1 , Spring 2007 , pp. 81 - 101
Copyright
Copyright © 2007 by the Board of Trustees of the University of Illinois

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.)

References

Aiken, Leona S., and West, Stephen G.. 1991. Multiple Regression: Testing and Interpreting Interactions. Newbury Park, CA: Sage.Google Scholar
Arceneaux, Kevin. 2002. “Direct Democracy and the Link between Public Opinion and State Abortion Policy.” State Politics and Policy Quarterly 2:372–87.CrossRefGoogle Scholar
Brambor, Thomas, Clark, William Roberts, and Golder, Matt. 2006. “Understanding Interaction Models: Improving Empirical Analyses.” Political Analysis 14:63–82.CrossRefGoogle Scholar
Braumoeller, Bear F. 2004. “Hypothesis Testing and Multiplicative Interaction Terms.” International Organization 58: 807–20.CrossRefGoogle Scholar
Burden, Barry C. 2005. “Institutional Features and Policy Representation in the States.” State Politics and Policy Quarterly 5:373–93.CrossRefGoogle Scholar
Francia, Peter L., and Herrnson, Paul S.. 2004. “The Synergistic Effect of Campaign Effort and Electoral Reform on Voter Turnout in State Legislative Elections.” State Politics and Policy Quarterly 4:74–93.CrossRefGoogle Scholar
Friedrich, Robert. 1982. “In Defense of Multiplicative Terms in Multiple Regression Equations.” American Journal of Political Science 26:797–833.CrossRefGoogle Scholar
Gerber, Alan S., Green, Donald P., and Nickerson, David. 2001. “Testing for Publication Bias in Political Science.” Political Analysis 9:385–92.CrossRefGoogle Scholar
Gerber, Elisabeth. 1996. “Legislative Response to the Threat of Popular Initiatives.” American Journal of Political Science 40:99–128.CrossRefGoogle Scholar
Greene, William H. 2000. Econometric Analysis. 4th ed. Upper Saddle River, NJ: Prentice Hall.Google Scholar
Gujarati, Damodar. 1992. Essentials of Econometrics. New York: McGraw-Hill.Google Scholar
Hagen, Michael G., Lascher, Edward L., and Camobreco, John F.. 2001. “Estimating the Effect of Ballot Initiatives on Policy Responsiveness.” Journal of Politics 63:1257–63.CrossRefGoogle Scholar
Kam, Cindy, and Franzese, Robert. 2003. “Modeling and Interpreting Interactive Hypotheses in Regression Analysis: A Brief Refresher and Some Practical Advice.” University of Michigan. Typescript.Google Scholar
King, Gary, Tomz, Michael, and Wittenberg, Jason. 2000. “Making the Most of Statistical Analyses: Improving Interpretation and Presentation.” American Journal of Political Science 44:347–61.CrossRefGoogle Scholar
Matsusaka, John G. 2001. “Problems with a Methodology Used to Evaluate the Voter Initiative.” Journal of Politics 63:1250–6.CrossRefGoogle Scholar
Smith, Kent W., and Sasaki, M.S.. 1979. “Decreasing Multicollinearity: A Method for Models with Multiplicative Functions.” Sociological Methods and Research 8:35–56.CrossRefGoogle Scholar
Supplementary material: File

Arceneaux and Huber supplementary material

Arceneaux and Huber supplementary material

Download Arceneaux and Huber supplementary material(File)
File 9.7 KB