How Performing PCA and CFA on the Same Data Equals Trouble
Marjolein Fokkema, Samuel Greiff · European Journal of Psychological Assessment · 2017
We regularly receive papers at EJPA where a principal component analysis (PCA) or an exploratory factor analysis (EFA) 1 is performed, followed by a confirmatory factor analysis (CFA) on the same (or partially overlapping) data.On the one hand, we are thankful for these submissions as they simplify the often tedious editorial task, by providing good grounds for on-desk rejection (see also Greiff & Ziegler, 2017).But when such grounds for rejection are all too regularly employed, they may instill a feeling of unease in the editor: Am I turning into a sour, nitpicking bureaucrat?Am I too strict and stuck with my own ideas of what good science is? Can we not let the data speak for itself?To confront such feelings of unease, we wanted to see whether the consequences of performing PCA and CFA on the same dataset are indeed so dire and justify rejection.To this end, we ran an experiment with simulated data and would like to share the results in this editorial.With the results of the simulation in mind, we will give some editorial advice on how authors can avoid trouble coming along with combining PCA and CFA.The R code and output for the experiment are provided in the online supplementary material.1 We fully agree with readers who take offense in the confusion of principal component analysis and exploratory factor analysis.The two are different techniques, involving different assumptions, estimation methods, and different interpretations of the results.We will discuss the differences in the section ''Some Nitpicking All the Same.''