What software are you using? 

In R, you can just use the function cut()

first argument is the variable to cut, second argument is the number of bins you wish to group the data into. "labels" is an optional argument, as well as "ordered_result" (which I suggest to set to TRUE). 

Example: 

cut_variable

In Excel, the function to convert interval to ordinal is RANK.AVG or RANK.EQ, depending on how you want to deal with ties.

For example, if your interval data are in a list in column D between rows 10 and 16, you would use the formula

=RANK.AVG(D10,$D$10:$D$16)

and then copy this formula downwards.

There is nothing that makes a variable inherently either ordinal or interval, although you can certainly group the original interval level scores into fewer categories in just about any way that you want.

Could you explain more about what your goals are with regard to the SEM?

As a follow-up to David's question, do you believe there may be nonlinearity in the X->Y relations? That's the only reason I can think of offhand to do this. Depending on your sample size, there are other methods for modeling nonlinear relations that don't "throw away" information by categorizing.

Pat

I take that back -- really odd distributions, especially multimodal, can be another reason to do this, and one I've used. For example, "on how many days of the last 30 did you smoke cigarettes" typically gets mostly zeros, a smaller number of 1s, 2s, and 3s, and then very sparse responses until a second mode at 30. So sometimes I'll cut it as 0, 1-2, 3-29, 30, for example, depending on the actual distribution and sample size, reflecting qualitative differences between non-smokers, occasional smokers, and daily smokers.

Thanks for the answer. I really appreciate all of your help

Thank you Michael et al., I was a bit worried about how to combine interval data to ordinal data for using in SEM. Noted , I can use the excel formula.