Probably the most popular factor retention criterion. Kaiser and Guttman suggested to retain as many factors as there are sample eigenvalues greater than 1. This is why the criterion is also known as eigenvalues-greater-than-one rule.
KGC(
x,
eigen_type = c("PCA", "SMC", "EFA"),
use = c("pairwise.complete.obs", "all.obs", "complete.obs", "everything",
"na.or.complete"),
cor_method = c("pearson", "spearman", "kendall", "poly", "tetra"),
n_factors = 1,
...
)An object of class efa_retention (see print.efa_retention() and
plot.efa_retention() for the print and plot methods). Its main fields are:
A named numeric vector with the suggested number of factors
for each requested eigenvalue type ("PCA", "SMC", and/or "EFA").
A list with one record per eigenvalue type, each holding the eigenvalues and the retained solution used for printing and plotting.
A list of the settings used.
data.frame or matrix. Dataframe or matrix of raw data or matrix with correlations.
character. On what the eigenvalues should be found. Can be either "PCA", "SMC", or "EFA", or some combination of them. If using "PCA", the diagonal values of the correlation matrices are left to be 1. If using "SMC", the diagonal of the correlation matrices is replaced by the squared multiple correlations (SMCs) of the indicators. If using "EFA", eigenvalues are found on the correlation matrices with the final communalities of an exploratory factor analysis solution (default is principal axis factoring extracting 1 factor) as diagonal.
character. Passed to stats::cor() if raw
data is given as input. Default is "pairwise.complete.obs".
character. Correlation computed from raw data: "pearson",
"spearman", or "kendall" (passed to stats::cor()), or "poly" /
"tetra" for polychoric / tetrachoric correlations of ordinal / binary data
(a two-step estimator with no empty-cell continuity correction).
Default is "pearson".
numeric. Number of factors to extract if "EFA" is included in
eigen_type. Default is 1.
Additional arguments passed to EFA(). For example,
to change the extraction method (PAF is default).
Originally, the Kaiser-Guttman criterion was intended for the use
with principal components, hence with eigenvalues derived from the original
correlation matrix. This can be done here by setting eigen_type to
"PCA". However, it is well-known that this criterion is often inaccurate and
that it tends to overestimate the number of factors, especially for unidimensional
or orthogonal factor structures (e.g., Zwick & Velicer, 1986).
The criterion's inaccuracy in these cases is somewhat addressed if it is
applied on the correlation matrix with communalities in the diagonal, either
initial communalities estimated from SMCs (done setting eigen_type to
"SMC") or final communality estimates from an EFA (done setting eigen_type
to "EFA"; see Auerswald & Moshagen, 2019). However, although this variant
of the KGC is more accurate in some cases compared to the traditional KGC, it
is at the same time less accurate than the PCA-variant in other cases, and it
is still often less accurate than other factor retention methods, for
example parallel analysis (PARALLEL()), the Hull method
HULL(), or sequential \(chi^2\) model tests (SMT();
see Auerswald & Moshagen, 2019).
The KGC function can also be called together with other factor
retention criteria in the N_FACTORS() function.
N_FACTORS() as a wrapper function for this and the other factor
retention criteria.
Other factor retention criteria:
CD(),
EKC(),
HULL(),
MAP(),
NEST(),
PARALLEL(),
SCREE(),
SMT()
KGC(test_models$baseline$cormat, eigen_type = c("PCA", "SMC"))
Run the code above in your browser using DataLab