Parameter Estimates

The parameter estimates table summarizes the effect of each predictor. While interpretation of the coefficients in this model is difficult due to the nature of the link function, the signs of the coefficients for covariates and relative values of the coefficients for factor levels can give important insights into the effects of the predictors in the model.

For covariates, positive (negative) coefficients indicate positive (inverse) relationships between predictors and outcome. An increasing value of a covariate with a positive coefficient corresponds to an increasing probability of being in one of the "higher" cumulative outcome categories.
For factors, a factor level with a greater coefficient indicates a greater probability of being in one of the "higher" cumulative outcome categories. The sign of a coefficient for a factor level is dependent upon that factor level's effect relative to the reference category.

You can make the following interpretations based on the parameter estimates:

Those in lower age categories show greater support for the bill than those in the highest age category.
Those who drive less frequently show greater support for the bill than those who drive more frequently.
The coefficients for the variables gender and votelast, in addition to not being statistically significant, appear to be small compared to other coefficients.

The design effects indicate that some of the standard errors computed for these parameter estimates are larger than those you would obtain if you used a simple random sample, while others are smaller. It is vitally important to incorporate the sampling design information in your analysis because you might otherwise infer, for example, that the coefficient for the third level of Age category, [agecat=3], is significantly different from 0!