I have 48 items in my questionnaire that represent 8 constructs. After conducting an exploratory factor analysis (EFA) with a principal components extraction method and Varimax rotation method, 8 sets of factor scores (FAC_1 to 8) were computed and saved using the regression method.
I've read in academic papers that those factor scores can be used as variables in regression analysis, but the problem is when I do so, the regression test yields no result! (I receive only 0 values!).
Alternatively I computed the mean score of the items representing each construct (resulting in 8 new variables) and used them in the regression test. This method worked, but i'm not sure if it's a correct procedure or not.
Questions
- Why am I getting zero-values for regression coefficients when I use factor saved scores and meaningful regression coefficients when I use computed mean scores?
- Can the mean scores of each construct instead of its factor scores generated through EFA be used in multiple regression analysis?
Best Answer
Unlike factor analysis, you cannot just put eight variables into a "regression test" and treat them all equally. One variable has to be the response variable and the others explanatory variables.
Your eight factors have been specifically designed to be orthogonal to eachother. I suspect you have put seven of the factors into a regression as explanatory variables (sometimes called "independent variables") with the eighth as the response variable (sometimes called "dependent variable"). Certainly you would find a low $R^2$ value, and estimates of the coefficient parameters close to zero, in this case.
Using factors as explanatory variables in a regression is sometimes justifiable (although I have some qualms myself - see @whuber's answer here for one reason why it might be questionable). However, the response variable for the regression needs to have been kept out of the original factor analysis. So you can only use your eight factors in a regression if the intent is to explain a ninth variable, one that was not in the original factor analysis.