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Transformation-Induced Bias: Unbiased Coefficients Do Not Imply Unbiased Quantities of Interest

  • Carlisle Rainey
Abstract

Political scientists commonly focus on quantities of interest computed from model coefficients rather than on the coefficients themselves. However, the quantities of interest, such as predicted probabilities, first differences, and marginal effects, do not necessarily inherit the small-sample properties of the coefficient estimates. Indeed, unbiased coefficient estimates are neither necessary nor sufficient for unbiased estimates of the quantities of interest. I characterize this transformation-induced bias, calculate an approximation, illustrate its importance with two simulation studies, and discuss its relevance to methodological research.

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Corresponding author
* Email: crainey@tamu.edu
Footnotes
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Author’s note: All computer code necessary for replication is available at https://github.com/carlislerainey/transformation-induced-bais and dx.doi.org/10.7910/DVN/CYXFB8 (Rainey 2017).

Contributing Editor: R. Michael Alvarez

Footnotes
References
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Casella, George, and Berger, Roger L.. 2002. Statistical inference , 2nd edn. Pacific Grove, CA: Duxbury.
King, Gary. 1998. Unifying political methodology: The likelihood theory of statistical inference . Ann Arbor: Michigan University Press.
King, Gary, Tomz, Michael, and Wittenberg, Jason. 2000. Making the most of statistical analyses: Improving interpretation and presentation. American Journal of Political Science 44(2):341355.
Lacina, Bethany. 2006. Explaining the severity of civil wars. Journal of Conflict Resolution 50(2):276289.
Long, J. Scott. 1997. Regression models for categorical and limited dependent variables. In Advanced quantitative techniques in the social sciences , Thousand Oaks, CA: Sage.
Nagler, Jonathan. 1994. Scobit: An alternative estimator to logit and probit. American Journal of Political Science 38(1):230255.
Nieman, Mark David. 2015. Statistical analysis of strategic interaction with unobserved player actions: Introducing a strategic probit with partial observability. Political Analysis 23(3):429448.
Peduzzi, Peter, Concato, John, Kemper, Elizabeth, Holford, Theodore R., and Feinstein, Alvan R.. 1996. A simulation study of the number of events per variable in logistic regression analysis. Journal of Clinical Epidemiology 49(12):13731379.
Rainey, Carlisle. 2017. Replication Data for: Transformation-Induced Bias. doi:10.7910/DVN/CYXFB8. Harvard Dataverse, V1, UNF:6:XDVZ8wD2BMxScpCoFcCLYg==.
Wooldridge, Jeffrey M. 2013. Introductory econometrics: A modern approach , 5th edn. Mason, OH: South-Western Cengage Learning.
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Political Analysis
  • ISSN: 1047-1987
  • EISSN: 1476-4989
  • URL: /core/journals/political-analysis
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