Hostname: page-component-586b7cd67f-t7fkt Total loading time: 0 Render date: 2024-11-22T02:37:41.183Z Has data issue: false hasContentIssue false
  • English
  • Français

Compression and Conditional Effects: A Product Term Is Essential When Using Logistic Regression to Test for Interaction*

Published online by Cambridge University Press:  26 November 2015

Carlisle Rainey
Get access Rights & Permissions [Opens in a new window]

Abstract

Previous research in political methodology argues that researchers do not need to include a product term in a logistic regression model to test for interaction if they suspect interaction due to compression alone. I disagree with this claim and offer analytical arguments and simulation evidence that when researchers incorrectly theorize interaction due to compression, models without a product term bias the researcher, sometimes heavily, toward finding interaction. However, simulation studies also show that models with a product term fit a broad range of non-interactive relationships surprisingly well, enabling analysts to remove most of the bias toward finding interaction by simply including a product term.

Type
Original Articles
Information
Political Science Research and Methods , Volume 4 , Supplement 3 , September 2016 , pp. 621 - 639
Copyright
© The European Political Science Association 2015 

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.)

Footnotes

*

Carlisle Rainey is Assistant Professor of Political Science in the Texas A&M University, 2010 Allen Building, College Station, TX 77843 ([email protected]). The author thanks Kenneth Benoit, Bill Berry, Scott Clifford, Justin Esarey, and two anonymous reviewers for helpful comments on earlier versions of this manuscript. The author also thanks John Oneal and Bruce Russet for making their data available, the Center for Computational Research at the University at Buffalo for providing support for the simulations. Code and data necessary to replicate the simulations and empirical analysis is available at http://www.carlislerainey.com/research and at http://thedata.harvard.edu/dvn/dv/PSRM. To view supplementary material for this article, please visit http://10.1017/psrm.2015.59

References

Ai, Chunrong, and Norton, Edward C.. 2003. ‘Interaction Terms in Logit and Probit Models’. Economics Letters 80(1):123–129.Google Scholar
Berry, Frances Stokes, and Berry, William D.. 1990. ‘State Lottery Adoptions as Policy Innovations: An Event History Analysis’. American Political Science Review 84(2):395–415.Google Scholar
Berry, Frances Stokes, and Berry, William D.. 1991. ‘Specifying a Model of State Policy Innovation’. American Political Science Review 85(2):573–579.Google Scholar
Berry, William, DeMeritt, Jacqueline H. R., and Esarey, Justin. 2010. ‘Testing for Interaction in Binary Logit and Probit Models: Is a Product Term Essential?’. American Journal of Political Science 54:248–266.Google Scholar
Berry, William, DeMeritt, Jacqueline H. R., and Esarey, Justin. 2015. ‘Bias and Overconfidence in Parametric Models of Interactive Processes’. American Journal of Political Science.Google Scholar
Berry, William, Golder, Matt, and Milton, Daniel. 2012. ‘Improving Tests of Theories Positing Interaction’. Journal of Politics 77(3):653–671.CrossRefGoogle Scholar
Bowen, Harry P. 2012. ‘Testing Moderating Hypotheses in Limited Dependent Variable and Other Nonlinear Models: Secondary Versus Total Interaction’. Journal of Management 38(3):860–889.Google Scholar
Brambor, Thomas, Clark, William Roberts, and Golder, Matt. 2006. ‘Understanding Interaction Models: Improving Empirical Analyses’. Political Analysis 14:63–82.Google Scholar
Bremer, Stuart A. 1992. ‘Dangerous Dyads: Conditions Affecting the Likelihood of Interstate War, 1816–1965’. Journal of Conflict Resolution 36(2):309–341.Google Scholar
Clark, William Roberts, Gilligan, Michael J., and Golder, Matt. 2006. ‘A Simple Multivariate Test for Asymmetric Hypotheses’. Political Analysis 14(3):311–331.Google Scholar
Diehl, Paul F. 1991. ‘Geography and War: A Review and Assessment of the Empirical Literature’. International Interactions 17(1):11–27.Google Scholar
Frant, Howard. 1991. ‘Specifying a Model of State Policy Innovation’. American Political Science Review 85(2):571–573.Google Scholar
Friedrich, Robert. 1982. ‘In Defense of Multiplicative Terms in Multiple Regression Equations’. American Journal of Political Science 26:797–833.Google Scholar
Greene, William. 2010. ‘Testing Hypotheses About Interaction Terms in Nonlinear Models’. Economics Letters 107(2):291–296.CrossRefGoogle Scholar
Hanmer, Michael, and Kalkan, Kerem Ozan. 2013. ‘Behind the Curve: Clarifying the Best Approach to Calculating Predicted Probabilities and Marginal Effects from Limited Dependent Variable Models’. American Journal of Political Science 57(1):263–277.Google Scholar
Huang, Chi, and Shields, Todd G.. 2000. ‘Interpretation of Interaction Effects in Logit and Probit Analyses’. American Politics Research 28(1):80–95.Google Scholar
Kam, Cindy D., and Franzese, Robert. 2007. Modeling and Interpreting Interactive Hypotheses in Regression Analysis. Ann Arbor: University of Michigan Press.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 44:341–355.CrossRefGoogle Scholar
Nagler, Jonathan. 1991. ‘The Effect of Registration Laws and Education on U.S. Voter Turnout’. American Journal of Political Science 85:1393–1405.Google Scholar
Nagler, Jonathan. 1994. ‘Scobit: An Alternative Estimator to Logit and Probit’. American Journal of Political Science 38(1):230–255.Google Scholar
Norton, Edward C., Wang, Hua, and Ai, Chunrong. 2004. ‘Computing Interaction Effects and Standard Errors in Logit and Probit Models’. The Stata Journal 4(2):154–167.Google Scholar
Oneal, John R., and Russet, Bruce. 2001. Triangulating Peace: Democracy, Interdependence, and International Organizations. New York: W. W. Norton.Google Scholar
Ray, James Lee. 1998. ‘Does Democracy Cause Peace?’. Annual Review of Political Science 1(1):27–46.Google Scholar
Tomz, Michael R., and Weeks, Jessica L. P.. 2013. ‘Public Opinion and the Democratic Peace’. American Political Science Review 107(4):849–865.Google Scholar
Train, Kenneth E. 2009. Discrete Choice Models with Simulation. New York: Cambridge University Press.Google Scholar
Tsai, Tsung-han, and Gill, Jeff. 2013. ‘Interaction in General Linear Models: Theoretical Issues and An Application to Personal Vote Earning Attributes’. Social Sciences 2(2):91–113.Google Scholar
Wolfinger, Raymond E., and Rosenstone, Steven J.. 1980. Who Votes? New Haven: Yale University Press.Google Scholar
Supplementary material: Link

Rainey Dataset

Dataset

http://dx.doi.org/10.7910/DVN/ASSC0Y
Link
Supplementary material: PDF

Rainey supplementary material

Appendix

Download Rainey supplementary material(PDF)
PDF 270.2 KB
Supplementary material: File

Rainey supplementary material

Rainey supplementary material

Download Rainey supplementary material(File)
File 21 KB