As the others point out, it is possible to incorporate an ordinal variable. It also seems you are interested in whether you can treat it as continuous and that is another matter. There are several considerations.

1) Continuous scales assume equivalent differences between intervals on the sale. This might not be valid if all you have is the rank order from the ordinal scale. For example, can you reasonably assume that the distance between "agree" and "strongly agree" is the same for all respondents to your questionnaire?

2) I would also consider the purpose of the study. How high are the stakes in your results?

3) How has the scale been treated in your discipline? It is very common in social science research to treat ordinal scales as continuous. What about the scale you are using? How have others treated it?

In the strictest sense you have an ordinal scale. On the other hand, what are typically truly ordinal scales are often treated as continuous in social science research. Though it's not often explicitly discussed, I think one should be explicit about the treatment of the scale as continuous if it is novel in your field. Knowing nothing about your research, to be precise I would treat the variable as ordinal and, if you are curious about that particular scale, conduct research on how reasonable it is to treat it as continuous if such research doesn't already exist for your scale and population. Stevens (1946) is a starting point on this issue.

Best wishes

You can perform it in AMOS-SEM

You need to watch videos on group difference, moderating group difference on youtube by James.

Regards

A moderating approach would require running 4 separate models to test for their equivalence.

You might look into the program M+ to see if it can handle an ordinal-level predictor.

As the others point out, it is possible to incorporate an ordinal variable. It also seems you are interested in whether you can treat it as continuous and that is another matter. There are several considerations.

1) Continuous scales assume equivalent differences between intervals on the sale. This might not be valid if all you have is the rank order from the ordinal scale. For example, can you reasonably assume that the distance between "agree" and "strongly agree" is the same for all respondents to your questionnaire?

2) I would also consider the purpose of the study. How high are the stakes in your results?

3) How has the scale been treated in your discipline? It is very common in social science research to treat ordinal scales as continuous. What about the scale you are using? How have others treated it?

In the strictest sense you have an ordinal scale. On the other hand, what are typically truly ordinal scales are often treated as continuous in social science research. Though it's not often explicitly discussed, I think one should be explicit about the treatment of the scale as continuous if it is novel in your field. Knowing nothing about your research, to be precise I would treat the variable as ordinal and, if you are curious about that particular scale, conduct research on how reasonable it is to treat it as continuous if such research doesn't already exist for your scale and population. Stevens (1946) is a starting point on this issue.

Best wishes

There is a crucial difference between a scale that is composed of several ordinal-level variables, versus a single item that has an ordinal-level set of responses. For SEM, multi-item scales are routinely treated as producing interval-level information, but single items are not.