Bartlett's (1951) test of sphericity tests whether a matrix (of correlations) is significantly different from an identity matrix. The test provides probability that the correlation matrix has significant correlations among at least some of the variables in a dataset, a prerequisite for factor analysis to work. In other words, before starting with factor analysis, one needs to check whether Bartlett’s test of sphericity is significant.

## Usage

check_sphericity_bartlett(x, ...)

## Arguments

x

A dataframe.

...

Arguments passed to or from other methods.

## Value

A list of indices related to sphericity.

## Details

This function is strongly inspired by the cortest.bartlett() function in the psych package (Revelle, 2016). All credit goes to its author.

## References

• Revelle, W. (2016). How To: Use the psych package for Factor Analysis and data reduction.

• Bartlett, M. S. (1951). The effect of standardization on a Chi-square approximation in factor analysis. Biometrika, 38(3/4), 337-344.

## Examples

library(parameters)
check_sphericity_bartlett(mtcars)
#> # Test of Sphericity
#>
#> Bartlett's test of sphericity suggests that there is sufficient significant correlation in the data for factor analysis (Chisq(55) = 408.01, p < .001).