Mistakes in factor extraction may consist in extracting too few or too many factors. A comprehensive review of the state-of-the-art and a proposal of criteria for choosing the number of factors is presented in.<ref>{{Cite journal |last1=Iantovics |first1=Laszlo Barna |last2=Rotar |first2=Corina |last3=Morar |first3=Florica |date=2018-12-04 |title=Survey on establishing the optimal number of factors in exploratory factor analysis applied to data mining |url=https://doi.org/10.1002/widm.1294 |journal=WIREs Data Mining and Knowledge Discovery |volume=9 |issue=2 |doi=10.1002/widm.1294 |s2cid=69358367 |issn=1942-4787}}</ref>
When selecting how many factors to include in a model, researchers must try to balance [[Occam's razor|parsimony]] (a model with relatively few factors) and plausibility (that there are enough factors to adequately account for correlations among measured variables).<ref>{{cite book|last=Fabrigar|first=Leandre R.|title=Exploratory factor analysis|publisher=Oxford University Press|___location=Oxford|isbn=978-0-19-973417-7|author2=Wegener, Duane T.|date=2012-01-12}}</ref>
