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PARAN: Stata module to compute Horn's test of principal components/factors

Author

Listed:
  • Alexis Dinno

    (Harvard School of Public Health)

Programming Language

Stata

Abstract

paran is an implementation of Horn's technique for evaluating the components or factors retained in a principal components analysis (PCA) or a common factor analysis. According to Horn, a common interpretation of non-correlated data is that they are perfectly non-collinear, and one would expect therefore to see eigenvalues equal to 1 in a PCA of such data. However, Horn notes that multi-colinearity occurs due to sampling error and least-squares "bias," even in uncorrelated data, and therefore actual PCAs of such data will reveal eigenvalues of components greater than and less than 1. His strategy is to contrast eigenvalues produced through a PCA on a random dataset (uncorrelated variables) with the same number of variables and observations as the experimental or observational dataset to produce eigenvalues for components or factors that are adjusted for the sample error-induced inflation.

Suggested Citation

  • Alexis Dinno, 2001. "PARAN: Stata module to compute Horn's test of principal components/factors," Statistical Software Components S420702, Boston College Department of Economics, revised 18 Mar 2009.
    • Handle: RePEc:boc:bocode:s420702
    as

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    File URL: http://fmwww.bc.edu/repec/bocode/p/paran.ado
    File Function: program code
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    File URL: http://fmwww.bc.edu/repec/bocode/p/paran.hlp
    File Function: help file
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    File URL: http://fmwww.bc.edu/repec/bocode/p/paran.do
    File Function: sample file
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