Firstly, to control if the sample was appropriate for explanatory factor, Kaiser-Meier-Olkin Test and Bartlett Test
When evaluating the behavior of two tests for the equality of variance matrices of k populations (multivariate Bartlett test
and its bootstrap version) with the use of a Monte Carlo simulation, in normal and non-normal populations, in combinations of the sample sizes (n), number of variables (p), correlations (r) and number of populations (k), SILVA et al.
8 for positive affect, negative affect and life satisfaction subscales; Bartlett test
is also highly significant (pless than .
We need to analyze the original variables by the Bartlett test
of sphericity and KMO before factor analysis.
The null hypothesis in Bartlett test
is that variables are correlated only with themselves.
Another important approach to determine the factorability of this study's data is to conduct the Bartlett test
The intent is to determine that data are uncorrelated prior to a PCA, and the Bartlett test
confirms that concept (Munro, 2005).
In all the three tests, we found significant differences among variances (for all the cases, the Cochran's C test and the Bartlett test
showed p = 0.
The Bartlett test
of sphericity was used to verify the null hypothesis, according to which the correlation matrix is an identity matrix.
The probability associated with the Bartlett test
for this research is p < 0.
D = Kolmogorov-Smirnov test statistic; W = Shapiro-Wilk test statistic; [chi square] = Bartlett test
In 1960, Levene proposed an alternative method to the Bartlett Test
(Bartlett, 1937) for testing the assumption of homogeneity of variance for the independent sample t-test and ANOVA designs.