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Thread: Interpreting odds ratio in ordinal logistic regression

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    Interpreting odds ratio in ordinal logistic regression




    Hi All,

    My data is from an educational programme for students. Questionnaire data was collected pre-intervention and post-intervention. We are looking at the change between pre-intervention and post-intervention.

    There are three levels for the dependent variable: positve outcome, no change, negative outcome.

    To determine a difference between gender, I have performed an ordinal logistic regression, and acquired the odds ratio between gender. However, I am not sure how to interpret such odds ratio. I have only worked with dependent variables with two levels.

    Odds ratio: 1.66
    P-value: 0.032

    Is it correct to say this: The male students are 1.66 times as likely to show a positive change than female students after the intervention (p=0.032).


    Thanks to all who have read this post.

  2. #2
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    Re: Interpreting odds ratio in ordinal logistic regression

    Hi, an important assumption in ordinal logistic regression is the "proportional odds assumption" which means basically, that the relationship between each pair of outcome groups is the same. "...In other words, ordinal logistic regression assumes that the coefficients that describe the relationship between, say, the lowest versus all higher categories of the response variable are the same as those that describe the relationship between the next lowest category and all higher categories, etc..." (c.f. http://www.ats.ucla.edu/stat/r/dae/ologit.htm ). Thus, I think that your interpretation is right, if your order of the dependent variable is: negative -- no change -- positive. In this case, a positive change means changing from "negative" to "no change", from "no change" to "positive", or from "negative" to "positive", and all probabilities should be the same. You should prove if this proportionality-assumption hold, the graphical technique is explained in the link above

  3. #3
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    Re: Interpreting odds ratio in ordinal logistic regression


    Thank you mmercker for your prompt reply!

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