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Area of Science:

  • Statistics
  • Biostatistics
  • Econometrics

Background:

  • Logit models are widely used for binary outcomes.
  • Interpretation of coefficients, particularly odds ratios, can be complex.
  • The standard deviation of the error term influences coefficient interpretation.

Purpose of the Study:

  • To clarify the interpretation of coefficients in logit models.
  • To examine the dependence of odds ratios on the error term's standard deviation and model specification.
  • To discuss alternatives to odds ratios for reporting results.

Main Methods:

  • Analysis of logit model coefficient interpretation.
  • Demonstration of odds ratio computation and its dependency on standard deviation (σ).
  • Evaluation of odds ratio sensitivity to varying model specifications.

Main Results:

  • Odds ratios are not absolute; they are conditional on data and model.
  • Comparison of odds ratios across different studies or models is unreliable.
  • The standard deviation (σ) of the error term is crucial for accurate interpretation.

Conclusions:

  • Average marginal effects are generally superior to odds ratios for reporting variable effects on binary outcomes.
  • Odds ratios may be appropriate in specific contexts, such as case-control studies.
  • Careful consideration of model specification is essential when using odds ratios.