Relative importance of predictors - Standardized coefficients in Ordinal Logistic Regression I am a 4th year psychology student. I need some help in understanding the coefficients in ORDINAL logistic regression. According to Williams (2009) "Using Heterogeneous Choice Models To Compare Logit and Probit Coefficients Across Groups", the predictor variables and residuals are already standardized to the logit distribution (variance = π*π/3 ), and, therefore, so are the reported coefficients in SPSS. Therefore, in order to compare the relative predictive strength of my variables in the model, I should just be able to directly compare the coefficients (or the odds ratios). However, how do I account for the differences in CI/standard error in my comparisons? 
For example,
variable 1: B=.021, std error = .0068,  Exp(B) = 1.022, 95% CI = 1.008 to 1.035
variable 2: B=.051, std error = .0174,  Exp(B) = 1.052, 95% CI = 1.017 to 1.089
From comparison of Bs - Variable 2 is the stronger predictor, but the std error and CI are much larger. So, what conclusion can I make?
 A: In general, if your predictors are on different metrics, then the subjective assessment of variable importance can not be easily made by simply comparing the raw sizes of the odds ratios.
If all your predictors are continuous, then I think converting the variables to z-scores would be useful for getting a sense of their relative importance. You mention that you have a skewed numeric predictor. I don't think changes anything too much for whether z-scores are appropriate. Ultimately, you have a separate issue of whether you want to apply a shape transformation (z-scores just change mean and variance). If your variable is highly skewed, then consider a transformation, and then z-score the transformed variable.
Some times, you have binary predictors. In that case, the 0-1 scoring is quite intuitive, especially if you have a few such variables.
A: If i understand you correctly, and if you have two models to choose from with relatively different Betas, the important is too choose the model based on its accuracy rather than merely strength of Betas... I suggest you compare the models based on other fit indicators such as AIC, BIC as well as the classification accuracy.
The confidence interval and std errors of Individual Betas, are not a good indicator when it comes to what model to choose.
But if you are speaking about two Beta's within the same model, the predictive power is measured through it's coefficient and it's P- value rather than its CI or Std Error of betas.
Also there's a good reponse to similar question of yours here: How to interpret coefficient standard errors in linear regression?
