Based on your response, you could handle the problem with classical test theory or item response theory. The easiest and fastest way to do this would be to calculate a total score for each subject, then calculate the average total score for each category of the rating scale. Your data will have to be converted from wide to long format to accomplish this, but that is easy to do. If the data are somewhat normally distributed, then you could calculate the mean and standard error of the total scores awarded for each rating scale category. The means should step up as the as the categories increase, and there should be a statistically significant difference between the category means. If there is not a significant difference, then the raters cannot tell the difference between the categories and they should be collapsed. This is better with item response theory as the ordinal values for total scores will be converted to true linear measures and distances between values will be more meaningful. IRT also provides wonderful graphs for this analysis.
That being said, if your data are in long format, you can do comparative boxplots with whiskers and notches. Visually the median values should step from lower to higher, evenly across scale and the notches between the boxes should not overlap (the notches are 95% confidence intervals. Example here.
Hope this helps.
Let me know if there is anything else.