Ordered-categorical measurements and censored measurements provide partial information about the value of an underlying continuous numeric variable. They provide more information than you would have if the measurement was missing, but less information than you would have if the numeric value was directly observed. In this sense, an ordered-categorical or censored measurement is somewhere between observed and missing.
Amos can impute a numerical value for an ordered-categorical or censored measurement in the same way that it can impute a numerical value for a missing measurement. The resulting completed dataset (also called an imputed dataset) can be used as input to other programs that require complete numerical data. In this way, non-numeric (ordered-categorical and censored) data can be converted to numeric data.