Factor analysis and one-way anova I run factor analysis with likert data and I took 3 new scale variables with positive and negative values (save as variables in spss...). When I used one of them to run an one-way anova with a categorical variable (3 levels) I was obliged to compare opposite means (Bonferroni test shew statistical significance between two groups. But the first mean was positive and the other negative). Which is bigger? The positive or negative one?

You cannot tell this a priori because it depends on the interpretation of the factors themselves.
The first thing you have to do, thus, is to find a reasonable interpretation of the factors (or factor if you have only one common factor) by looking at the loadings, and perhaps trying different rotation methods (varimax, promax, etc.). Then estimate the rotated factor scores. Now you can tell what it means to have higher (lower) values on the factor scores in terms of your original variables by looking at the signs of the loadings.
For instance, if you find out that the loadings for the first factor on the relevant variables are all of the same sign, then this means that those original variables are positively correlated with that factor. This is because the sign of the loadings determines the direction of the correlation between factors and original variables. All this implies that individuals with higher (lower) values on this factor will have higher (lower) values on the associated original variables.
