hornpa - Horn's (1965) Test to Determine the Number of Components/Factors
A stand-alone function that generates a user specified
number of random datasets and computes eigenvalues using the
random datasets (i.e., implements Horn's [1965, Psychometrika]
parallel analysis
<https://link.springer.com/article/10.1007/BF02289447>). Users
then compare the resulting eigenvalues (the mean or the
specified percentile) from the random datasets (i.e.,
eigenvalues resulting from noise) to the eigenvalues generated
with the user's data. Can be used for both principal components
analysis (PCA) and common/exploratory factor analysis (EFA).
The output table shows how large eigenvalues can be as a result
of merely using randomly generated datasets. If the user's own
dataset has actual eigenvalues greater than the corresponding
eigenvalues, that lends support to retain that
factor/component. In other words, if the i(th) eigenvalue from
the actual data was larger than the percentile of the (i)th
eigenvalue generated using randomly generated data, empirical
support is provided to retain that factor/component. Horn, J.
(1965). A rationale and test for the number of factors in
factor analysis. Psychometrika, 32, 179-185.
