Bringing balance and technical accuracy to reporting odds ratios and the results of logistic regression analyses

By Jason W. Osborne. 2006

125 citations recorded by the source corpus.

Logistic regression and odds ratios (ORs) are powerful tools recently becoming more common in the social sciences. Yet few understand the technical challenges of correctly interpreting an odds ratio, and often it is done incorrectly in a variety of different ways. The goal of this brief note is to review the correct interpretation of the odds ratio, how to transform it into the more easily understood and intuitive relative risk (RRs) estimate, and a suggestion for dealing with odds ratios or relative risk estimates that are below 1.0 so that perceptually their magnitude is equivalent of Ors or RRs greater than 1.0.

Subfields: Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/8a9e6025f81860af4d41d35f53fa005dc1f11188

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In Statistics and Probability

1. Max Smith
2. J. Gore

Citations in the corpus

• Socioeconomic status and the career aspirations of Australian school students: Testing enduring assumptions (2015, result): “While growing in popularity as an analytical approach, the results obtained through logistic regression are notoriously difficult to interpret and frequently misunderstood (Osborne 2006).”