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

Author

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  • 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
    DOI: 10.1080/07388940500339167
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    References listed on IDEAS

    as
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    4. Sartori, Anne E., 2003. "An Estimator for Some Binary-Outcome Selection Models Without Exclusion Restrictions," Political Analysis, Cambridge University Press, vol. 11(2), pages 111-138, April.
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