Factor analysis used to investigate shared dimensions without fixing the complete loading pattern in advance. Factor count, estimator and factor rotation require justification. A resulting structure is a proposal for further study, not independent confirmation of itself.
An approach to selecting factor or component count by comparing observed eigenvalues with reference values obtained from uncorrelated random data of matching dimensions. The reference must match the intended extraction and correlation approach. John L. Horn proposed a random-variable procedure to assess sampling contributions to the roots.[ref] Use it with substantive interpretation and model assessment rather than as a universal guarantee of a correct factor count.
A common-factor extraction method based on iteratively estimating shared variance and the associated factor loadings. It differs from principal component analysis, whose components summarize total observed variation.
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