As described in Data options, fac= defines factors when the numbers in the data are not to be used (for example, the data might be multicoded) whereas inc= reads the data from the column and uses that as the factor for each row. Use fac= where possible, because it is more efficient than inc=.

The respondents have been asked to say how much they agree or disagree with a particular statement. If they agree very much, they have a code ‘1’ in c210. If they agree somewhat, they have a ‘2’; if they neither agree nor disagree, they have a ‘3’; if they disagree somewhat, they have a ‘4’; and if they disagree very much, they have a ‘5’. People who refuse to answer are coded as c210’&’.

If you obtain a numerical mean value of these opinions using factors of +2 for ‘agrees very much’ down to –2 for ‘disagrees very much’, these are not the same as the codes representing these responses in the data. Enter them with fac=. People who refused to answer appear in the table, but are not included in the mean.

You can write this axis using n01s or a col statement, both are equally correct:

The second example uses the nofac option on the Refused element because it uses fac=2-1 to specify automatic factoring. If it specified the factor separately for each element, the nofac option would not be necessary.

The mean is the mean of the factors, calculated as:

n is the number of respondents having an opinion — c210’1/5’. If there were 25 people who agreed very much, 19 who agreed somewhat, 7 who neither agreed nor disagreed, 15 who disagreed somewhat, 3 who disagreed very much, and 6 who refused to answer, the calculation would be:

yielding a mean value of 0.70. Notice that the 6 people who refused to answer were ignored completely.