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?
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?