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  • Cited by 2
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    This chapter has been cited by the following publications. This list is generated based on data provided by CrossRef.

    Dunning, Thad 2016. Transparency, Replication, and Cumulative Learning: What Experiments Alone Cannot Achieve. Annual Review of Political Science, Vol. 19, Issue. 1, p. S1.

    Dunning, Thad 2016. Transparency, Replication, and Cumulative Learning: What Experiments Alone Cannot Achieve. Annual Review of Political Science, Vol. 19, Issue. 1, p. 541.

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  • Print publication year: 2009
  • Online publication date: June 2012

13 - Randomization Does Not Justify Logistic Regression

Summary

Abstract. The logit model is often used to analyze experimental data. However, randomization does not justify the model, so the usual estimators can be inconsistent. A consistent estimator is proposed. Neyman's non-parametric setup is used as a benchmark. In this setup, each subject has two potential responses, one if treated and the other if untreated; only one of the two responses can be observed. Beside the mathematics, there are simulation results, a brief review of the literature, and some recommendations for practice.

Introduction

The logit model is often fitted to experimental data. As explained below, randomization does not justify the assumptions behind the model. Thus, the conventional estimator of log odds is difficult to interpret; an alternative will be suggested. Neyman's setup is used to define parameters and prove results. (Grammatical niceties apart, the terms “logit model” and “logistic regression” are used interchangeably.)

After explaining the models and estimators, we present simulations to illustrate the findings. A brief review of the literature describes the history and current usage. Some practical recommendations are derived from the theory. Analytic proofs are sketched at the end of the chapter.

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Statistical Models and Causal Inference
  • Online ISBN: 9780511815874
  • Book DOI: https://doi.org/10.1017/CBO9780511815874
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