Log Odds and the Interpretation of Logit Models.

ABSTRACT:

Objective

We discuss how to interpret coefficients from logit models, focusing on the importance of the standard deviation (σ) of the error term to that interpretation.

Study design

We show how odds ratios are computed, how they depend on the standard deviation (σ) of the error term, and their sensitivity to different model specifications. We also discuss alternatives to odds ratios.

Principal findings

There is no single odds ratio; instead, any estimated odds ratio is conditional on the data and the model specification. Odds ratios should not be compared across different studies using different samples from different populations. Nor should they be compared across models with different sets of explanatory variables.

Conclusions

To communicate information regar

SUBMITTER: Norton EC 

PROVIDER: S-EPMC5867187 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature