I have a 3-category ordered outcome (food consumption: 1=no food, 2=less food, 3=more food) and a 3-category ordered predictor (food exposure: 3=no time, 2= less time, 1= more time- whereby 3=no time is taken as reference category in the ordinal regression model). I want to explore hypothesis that more food exposure is associated to more food consumption.
I want to know how I can interpret odds ratios less than 1. For example, I have OR= 0.62 for predictor category 2= less time. I have calculated OR as exp(coeff) in Excel, whereby OR of reference category no time is exp(0)=1.
Best Answer
Given the coeffcient is significant, it means that the cummulative odds for being in a higher food category are .62 times as high for people with less time than for people with no time. Put differently, having more time than no time decreases the odds (and also the probability) for consuming more food (across all categories of your dependent variable). I do not know whether this makes any sense theoretically.
Given that the coeffcient for category 1 is positive (OR>1), this suggests a nonlinear relationship across the categories of the iV. That is, the interpretation for catgory 1 is opposite of that for category 2, given the coefficient is significant.