EKC {latentFactoR}

Estimate Number of Dimensions using Empirical Kaiser Criterion

Description

Estimates the number of dimensions in data using Empirical Kaiser Criterion (Braeken & Van Assen, 2017). See examples to get started

Usage

EKC(data, sample_size)

Arguments

data

Matrix or data frame. Either a dataset with all numeric values (rows = cases, columns = variables) or a symmetric correlation matrix

sample_size

Numeric (length = 1). If input into data is a correlation matrix, then specifying the sample size is required

Value

Returns a list containing:

dimensions

Number of dimensions identified

eigenvalues

Eigenvalues

reference

Reference values compared against eigenvalues

Author(s)

Alexander P. Christensen <[e-mail]>, Hudson Golino <[e-mail]>, Luis Eduardo Garrido <[e-mail]>

References

Braeken, J., & Van Assen, M. A. (2017). An empirical Kaiser criterion. Psychological Methods, 22(3), 450–466.

Examples

# Generate factor data
two_factor <- simulate_factors(
  factors = 2, # factors = 2
  variables = 6, # variables per factor = 6
  loadings = 0.55, # loadings between = 0.45 to 0.65
  cross_loadings = 0.05, # cross-loadings N(0, 0.05)
  correlations = 0.30, # correlation between factors = 0.30
  sample_size = 1000 # number of cases = 1000
)

# Perform Empirical Kaiser Criterion
EKC(two_factor$data)