With 9 levels, you can probably treat the indicators as continuous unless there is significant kurtosis in them. If you are modeling the variables as ordinal, then it doesn't matter how you collapse (i.e., if you are preserving symmetry); the ordinal nature of the data means that it's the order of the categories that matters, not the label assigned. You throw away information when you collapse categories, but as long as there is enough information about the participants in other times, your inference should be approximately correct no matter how the binning is done. You don't need ll the indicators to b eon the same scale; some can more and different categories than others. It's not the category labels that matter, is its the relative ordering of units for each item.

With 9 levels, you can probably treat the indicators as continuous unless there is significant kurtosis in them. If you are modeling the variables as ordinal, then it doesn't matter how you collapse (i.e., if you are preserving symmetry); the ordinal nature of the data means that it's the order of the categories that matters, not the label assigned. You throw away information when you collapse categories, but as long as there is enough information about the participants in other items, your inference should be approximately correct no matter how the binning is done. You don't need all the indicators to be on the same scale; some can have more and different categories than others. It's not the category labels that matter, it's the relative ordering of units for each item.