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Factor analysis is a dimensionality reduction latent variable technique which replaces inter-correlating variables with a smaller number of continuous latent variables called factors. The factors are believed to be responsible for the inter-correlations. [For confirmatory factor analysis, please use the tag 'confirmatory-factor'. Also, the term "factor" of factor analysis should not be confused with "factor" as categorical predictor of a regression/ANOVA.]

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Deciding the Optimal Number of Factors [closed]

In practice, is there generally a difference between having 100 factors and 1000 factors in a model? Is there a well-researched 'upper-bound' to how many factors a given model should have?