The effects of different types of factor analysis on error reduction

This study demonstrates that a commonly used type of factor analysis, principle components analysis, contributes to the amount of error in statistical analysis. In the study, which concerns email use, factor analyses were performed using several different factor analysis methods. The results show that using factors derived via principle components analysis as dependent variables substantially increased the amount of error in regression analyses, and in several cases reduced the amount of explained variance.