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===Parallel analysis===
Compute the eigenvalues for the correlation matrix and plot the values from largest to smallest and then plot a set of random eigenvalues. The number of eigenvalues before the intersection points indicates how many factors to include in your model. <ref>{{cite journal|last=Horn|first=John L.|title=A rationale and test for the number of factors in factor analysis|journal=Psychometrika|date=1 June 1965|volume=30|issue=2|pages=179–185|doi=10.1007/BF02289447}}</ref> <ref>{{cite journal|last=Humphreys|first=L. G.|coauthors=Ilgen, D. R.|title=Note On a Criterion for the Number of Common Factors|journal=Educational and Psychological Measurement|date=1 October 1969|volume=29|issue=3|pages=571–578|doi=10.1177/001316446902900303}}</ref> <ref name=Humphreys>Humphreys, L. G. & Montanelli, R. G., Jr. 1975. An investigation of the parallel analysis criterion for determining the number of common factors. Multivariate Behavioral Research, 10(2): 193-205.</ref> This procedure can be somewhat artbitrary (i.e. a factor just meeting the cutoff will be included and one just below will not).<ref name =Fabrigar/>
===Kaiser criterion===
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