# KMO and Bartlett’s test of sphericity

The table below presents two different tests: the Kaiser-Meyer-Olkin (KMO) Measure of Sampling Adequacy and Bartlett’s test of Sphericity.

# KMO

KMO is a test conducted to examine the strength of the partial correlation (how the factors explain each other) between the variables. KMO values closer to 1.0 are consider ideal while values less than 0.5 are unacceptable. Recently,most scholars argue that a KMO of at least 0.80 are good enough for factor analysis to commence. Below is a tabular chart for your perusal.

From our result, we had a KMO value of .853. This indicates that the degree of information among the variables overlap greatly/the presence of a strong partial correlation. Hence, it is plausible to conduct factor analysis.

# Bartlett’s test of Sphericity

The Bartlett’s test of Sphericity is used to test the null hypothesis that the correlation matrix is an identity matrix. An identity correlation matrix means your variables are unrelated and not ideal for factor analysis. A significant statistical test (usually less than 0.05) shows that the correlation matrix is indeed not an identity matrix (rejection of the null hypothesis) as represented in the table below.