Bartlett's test

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Bartlett's test

[′bärt·ləts ‚test]
(statistics)
A method to test for the equalities of variances from a number of independent normal samples by testing the hypothesis.
McGraw-Hill Dictionary of Scientific & Technical Terms, 6E, Copyright © 2003 by The McGraw-Hill Companies, Inc.
References in periodicals archive ?
Another criterion, Bartlett's test of sphericity results revealed that the explanatory factor analysis was suitable for the studied data sets (p<0.01), which was in agreement with those (0.892 KMO and Bartlett test, p<0.01) reported by Eyduran et al.
Factor loadings, variances, Cronbach alpha values, Bartlett test result and KMO value are shown in Table 2.
In order to test whether the sample was appropriate for exploratory factor analysis or not, Kaiser-Meier-Olkin Test was used as the first statistical measure, followed by Bartlett Test. Exploratory factor analysis was performed by applying Varimax rotation, in congruence to main components method, and factors with eigenvalues equal to 1 and above were included in the analysis.
To test the null hypothesis that all correlation coefficients are zero or not significant, chi-square statistic is computed under Bartlett test. The value of approximate chi-square statistic is found to be 399.705 with 91 degrees of freedom, which is significant at 0.000 level of significance which is under the accepted range of level of significance (p-value) 0.05.
In each scenario, the adherence of data to normal distribution was tested by Lilliefors test and variance homogeneity among rows and columns by the Bartlett test.
Table 1 shows KMO value of.8 for positive affect, negative affect and life satisfaction subscales; Bartlett test is also highly significant (pless than .001) for each of the three subscales indicating that data is suitable for factor analysis.
We need to analyze the original variables by the Bartlett test of sphericity and KMO before factor analysis.
Also the importance and significance of correlation matrix is usually evaluated using Bartlett test. Factor analysis would be suitable for identification of structure (Factor Model) if the Bartlett significance level is less than 5% [23].
Another important approach to determine the factorability of this study's data is to conduct the Bartlett test of sphericity.
The intent is to determine that data are uncorrelated prior to a PCA, and the Bartlett test confirms that concept (Munro, 2005).
2A), appearing significant differences in the variances (Cochran's C test, p = 5.804 [10.sup.-9]; Bartlett test, p = 0.000) and among the medians at the 95.0% confidence level (Kruskal-Wallis test, p = 0.000).
The Bartlett test of sphericity was used to verify the null hypothesis, according to which the correlation matrix is an identity matrix.