How Performing PCA and CFA on the Same Data Equals Trouble 论文

摘要

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 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?