EKC {Rnest}R Documentation

Empirical Kaiser Criterion (EKC)

Description

Empirical Kaiser Criterion (EKC)

Usage

EKC(.data = NULL, n = NULL, nv = NULL, lowest.eig = 1, ...)

Arguments

.data

a data frame, a numeric matrix, covariance matrix or correlation matrix from which to determine the number of factors.

n

the number of cases (subjects, participants, or units) if a covariance matrix is supplied in .data.

nv

the number of variables if the critical values are required.

lowest.eig

minimal eigenvalues to retain. Default is Kaiser's suggestion of 1.

...

further argument for cor_nest().

Value

The number of factors to retain or the crititical eigenvalues.

Note

As Rnest version >= 1.2, a correction to EKC was done which was reported by Marcel van Assen (personnal communication, june 2025), which was found in Rnest and other packages as well. There was a confusion in the sample and critical eigenvalues in equation 2 (Braeken & van Assen, 2017, p. 454).

References

Braeken, J., & van Assen, M. A. L. M. (2017). An empirical Kaiser criterion. Psychological Methods, 22(3), 450–466. doi:10.1037/met0000074

Examples

EKC(ex_4factors_corr, n = 42)

[Package Rnest version 1.2 Index]