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.

check_sphericity_bartlett(x, ...)



A dataframe.


Arguments passed to or from other methods.


A list of indices related to sphericity.


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


  • 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.


#> # 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).