1
$\begingroup$

I need to interpret odds ratios from a multiple logistic regression predicting self esteem ("SelfEsteemHigh")from 2 predictor variables (agreeableness & conscientiousness).

> exp(coef(mlog2))
(Intercept)               agreeableness 
        0.1999042         1.0607966 
conscientiousness 
        1.2209377

    > exp(confint(mlog2))
                               2.5 %       97.5 %
(Intercept)              0.007521561        4.390917
agreeableness            0.787426218        1.440916
conscientiousness       0.875694846        1.735260

These are the outcomes for odds ratios & 95% CI, the example for interpretation that was given to us was "A logistic regression predicting high stress from selfesteem (high / low) showed that students who had low selfesteem were expected to have 1.27 odds of being high stress, [95% CI 0.58 - 2.87]. Being in the high vs low self esteem group was associated with 0.08 times the odds of being high stress, [95% CI 0.02 - 0.30], p < .001. A graph of these results follows"

The issue is that my two predictor variables are continuous (not low/high) So I am unsure how to adapt my results to fit this interpretation example. please help!

$\endgroup$
0

1 Answer 1

0
$\begingroup$

Your coefficients (or more specifically, the exponentiation of your coefficients) are the ratios of the odds at X and X+1, or the ratio of odds per unit change of the predictor variable. Mathematically,

$\frac{Odds(Y=1|X=x+1)} {Odds(Y=1|X=x )}=e^{\beta_i}$

$\endgroup$

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.