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With the exception of Revelle and Rocklin's (1979) very simple structure criterion, model comparison techniques, and Velicer's (1976) minimum average partial, all other procedures rely on the analysis of eigenvalues. The ''eigenvalue'' of a factor represents the amount of variance of the variables accounted for by that factor. The lower the eigenvalue, the less that factor contributes to explaining the variance of the variables.<ref name =Norris/>
The paper <ref>{{Cite journal|last=Iantovics|first=Laszlo Barna|last2=Rotar|first2=Corina|last3=Morar|first3=Florica|date=2019-03-XX|title=Survey on establishing the optimal number of factors in exploratory factor analysis applied to data mining|url=http://doi.wiley.com/10.1002/widm.1294|journal=Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery|language=en|volume=9|issue=2|pages=e1294|doi=10.1002/widm.1294}}</ref> published in 2018 presents a comprehensive survey on establishing the optimal number of factors in exploratory factor analysis. It was outlined the importance of the evaluation in some research, based on the research specificity, the total cumulative variance that should be explained by the selected optimal number of factors that are extracted.
A short description of each of the nine procedures mentioned above is provided below.
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