# Does it make sense to use criteria from PCA to select the numbers of factors in a factor analysis?

Looking at both the practice of colleagues and also the practices instantiated in popular programs (e.g. SPSS, and commonly used syntax for SPSS), it seems common to use criteria based on a PCA to select the number of factors in a factor analysis.

I am not just talking here about the Kaiser-Guttman rule and scree plots but also better-regarded methods such parallel analysis, and the MAP test.

Would it not make more sense to conduct parallel analysis and the MAP test on factors, if the goal is to select the number of factors?

Is this a major problem, or is it basically OK to use the criteria from PCA as part of a guide to selecting the number of factors?

• I too am confused why PCA criteria are being used everywhere in factor analysis. It seems that factor analysis has very natural criteria to analyze, e.g. the mean uniqueness (a form of mean squared error), which is the same as 1 minus the common var explained. Plotting this as a function of the number of factors leads to a production possibilities frontier, and then arguments regarding tradeoffs between explanatory power and the number of factors can be meaningfully discussed. (relatedly, I'm not sure why PCA scree plots use eigenvectors rather than the cumulative sum of eigenvectors) Nov 5, 2020 at 3:42
• You can do parallel analysis on FA results as well. For instance, the psych R package offers both ways (PCA and/or FA). It's just that even as a rough approximation PCA works quite well in this case. If you want to learn more about the subtle distinction between those two approaches, look for some of @ttnphns's nice answers related to this topic.
– chl
Nov 5, 2020 at 7:47
• It's worth recognizing that FA & PCA perform the same arithmetic over a nearly identical matrix. So while the theory is quite different, and the interpretations are supposed to be different, the output isn't necessarily all that different in practice. As a result, it may be 'good enough for government work'. Nov 6, 2020 at 20:32
• A related thread stats.stackexchange.com/q/241032/3277, especially look in the comments. Nov 7, 2020 at 2:25
• @RichardDiSalvo, Throughout my numerous comments to this current as well as the other Q, I expressed reasons for my feeling that preliminary criteria (1) Kaiser's, (2) Cattell's scree, and possibly (3) Parallel analysis - should be based on PCA rather than on EFA itself. My main reasons were two: (i) eigenvalues or explained variances corresponding to the m+1, m+2... factors are not "real" or "existing" because only m factors were modeled in the FA; Nov 9, 2020 at 22:08

It is basically OK to use the criteria from PCA as part of a guide to selecting the number of factors. Most of the times, FA and PCA results will be in agreement.

Parallel analysis and Velicer's minimum average partial (MAP) are the most reliable and accurate techniques to assess the number of components or factors to retain, according to Zwick & Velicer.1 The fact that we use PCA instead of FA is motivated by historical reasons, and was more or less disputed in the last 20 years. Most research has focused on PCA but Velicer and coll. discussed some of the adaptations that were developed for principal axes factor analysis, which is widely used in factor analysis, and they concluded that "no satisfactory alternatives are available within factor analysis."2

Sidenote:

Horn's parallel analysis aims to provide an idea of the distribution of eigenvalues on randomly perturbed observations, from the same dataset, hence it gives an idea of the "residual" variance in your data, i.e. what would be expected if there were no signal in your dataset. Despite being rarely used in practice, this resampling-based approach is certainly a better criterion than Kayser's rule (eigenvalues > 1). Other aspects of parallel analysis are discussed in this related thread: How to correctly interpret a parallel analysis in exploratory factor analysis?. Some statistical packages, like paran available in R and Stata, even offer a way to correct for bias arising from colinearity due to sampling error (which, of course, is not accounted for when using Kayser's rule), whereby eigenvalues of PCA components might be greater than and less than 1, and other subtleties, like how to generate random samples (e.g., standardized values, using same rank for the simulated dataset as the observed response matrix, or drawing random variates from a distribution close to the empirical one).3 Many of Velicer's papers and recent publications on parallel analysis contrast PCA and FA approaches in determining the number of factors to retain, so you will likely find additional information on how un-rotated PCA and FA models are able to recover the proper factor structure of a given dataset.