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Let's Put Garbage-Can Regressions and Garbage-Can Probits Where They Belong

Author

Listed:
  • Christopher H. Achen

    (Department of Politics Princeton University Princeton, New Jersey, USA, achen@princeton.edu)

Abstract

Many social scientists believe that dumping long lists of explanatory variables into linear regression, probit, logit, and other statistical equations will successfully “control†for the effects of auxiliary factors. Encouraged by convenient software and ever more powerful computing, researchers also believe that this conventional approach gives the true explanatory variables the best chance to emerge. The present paper argues that these beliefs are false, and that without intensive data analysis, linear regression models are likely to be inaccurate. Instead, a quite different and less mechanical research methodology is needed, one that integrates contemporary powerful statistical methods with deep substantive knowledge and classic data—analytic techniques of creative engagement with the data.

Suggested Citation

  • Christopher H. Achen, 2005. "Let's Put Garbage-Can Regressions and Garbage-Can Probits Where They Belong," Conflict Management and Peace Science, Peace Science Society (International), vol. 22(4), pages 327-339, September.
  • Handle: RePEc:sae:compsc:v:22:y:2005:i:4:p:327-339
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    10. Rickard, Stephanie J. & Caraway, Teri L., 2018. "International demands for austerity: examining the impact of the IMF on the public sector," LSE Research Online Documents on Economics 86636, London School of Economics and Political Science, LSE Library.
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    18. Fang-Yi Chiou & Lawrence S. Rothenberg, 2016. "Presidential unilateral action: partisan influence and presidential power," Public Choice, Springer, vol. 167(1), pages 145-171, April.
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    20. Ilya Lokshin, 2015. "Whatever Explains Whatever: The Duhem-Quine Thesis And Conventional Quantitative Methods In Political Science," HSE Working papers WP BRP 23/PS/2015, National Research University Higher School of Economics.

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