efa.ekc {semTools}

Empirical Kaiser criterion

Description

Identify the number of factors to extract based on the Empirical Kaiser Criterion (EKC). The analysis can be run on a data.frame or data matrix (data), or on a correlation or covariance matrix (sample.cov) and the sample size (sample.nobs). A data.frame is returned with two columns: the eigenvalues from your data or covariance matrix and the reference eigenvalues. The number of factors suggested by the Empirical Kaiser Criterion (i.e. the sample eigenvalues greater than the reference eigenvalues), and the number of factors suggested by the original Kaiser Criterion (i.e. sample eigenvalues > 1) is printed above the output.

Usage

efa.ekc(data = NULL, sample.cov = NULL, sample.nobs = NULL,
  missing = "default", ordered = NULL, plot = TRUE)

Arguments

data

A data.frame or data matrix containing columns of variables to be factor-analyzed.

sample.cov

A covariance or correlation matrix can be used, instead of data, to estimate the eigenvalues.

sample.nobs

Number of observations (i.e. sample size) if is.null(data) and sample.cov is used.

missing

If "listwise", cases with missing values are removed listwise from the data frame. If "direct" or "ml" or "fiml" and the estimator is maximum likelihood, an EM algorithm is used to estimate the unrestricted covariance matrix (and mean vector). If "pairwise", pairwise deletion is used. If "default", the value is set depending on the estimator and the mimic option (see details in lavCor).

ordered

Character vector. Only used if object is a data.frame. Treat these variables as ordered (ordinal) variables. Importantly, all other variables will be treated as numeric (unless is.ordered == TRUE in data). (see also lavCor)

plot

logical. Whether to print a scree plot comparing the sample eigenvalues with the reference eigenvalues.

Value

A data.frame showing the sample and reference eigenvalues.

The number of factors suggested by the Empirical Kaiser Criterion (i.e. the sample eigenvalues greater than the reference eigenvalues) is returned as an attribute (see Examples).

The number of factors suggested by the original Kaiser Criterion (i.e. sample eigenvalues > 1) is also printed as a header to the data.frame

Author(s)

Ylenio Longo (University of Nottingham; [e-mail])

Terrence D. Jorgensen (University of Amsterdam; [e-mail])

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

## Simulate data with 3 factors
model <- '
  f1 =~ .3*x1 + .5*x2 + .4*x3
  f2 =~ .3*x4 + .5*x5 + .4*x6
  f3 =~ .3*x7 + .5*x8 + .4*x9
'
dat <- simulateData(model, seed = 123)
## save summary statistics
myCovMat <- cov(dat)
myCorMat <- cor(dat)
N <- nrow(dat)

## Run the EKC function
(out <- efa.ekc(dat))

## To extract the recommended number of factors using the EKC:
attr(out, "nfactors")

## If you do not have raw data, you can use summary statistics
(x1 <- efa.ekc(sample.cov = myCovMat, sample.nobs = N, plot = FALSE))
(x2 <- efa.ekc(sample.cov = myCorMat, sample.nobs = N, plot = FALSE))