Proportional Odds Logistic Regression. This is a problem when the data structure is sparse. The effect of an independent variable is constant for each increase in the level of the response.
For a primer on proportional-odds logistic regression see our post Fitting and Interpreting a Proportional. The key assumption in ordinal regression is that the effects of any explanatory variables are consistent or proportional across the different thresholds hence this is usually termed the assumption of proportional odds S PSS calls this the assumption of. Ordinal logistic regression is the assumption of proportional odds.
Log odds is the logarithm of the odds.
Hence the output of an ordinal logistic regression will contain an intercept for each level of the response except one and a single slope for each explanatory variable. By ordered we mean categories that have a natural ordering such as Disagree Neutral Agree or Everyday Some days Rarely Never. One way around the issue of non-proportional odds is to just fit the log-linear model. Cox Regression loghtX logh ot 1X 1 2X 2.
