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Values above 0.6 provide sufficient evidence for the factorability of the correlation matrix [9].
The Bartlett's test of sphericity and Kaiser-Meyer-Olkin (KMO) were used to determine the appropriateness of the data set for factor analysis [ 30]. Bartlett's test of sphericity was evaluated for the factorability of the correlation matrix (i.e., to determine whether the items could be classified into categories).
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The KMO values for the subscales ranged from 0.54 to 0.87, and Bartlett's Test of Sphericity reached statistical significance (p = 0.000) for all subscales, supporting the factorability of the correlation matrices.
In addition, the Barlett's Test of Sphericity (Barlett 1954) also reached statistical significance (p = 0.000), supporting the factorability of the correlation matrix.
Both supporting the factorability of the correlation matrix.
The Bartlett's Test of Spehericity (χ = 3969.47, p < 0.001) was statistically significant, supporting the factorability of the correlation matrix.
Bartlett's test of sphericity [ 22] was significant at p < 0.001, supporting the factorability of the correlation matrix.
When the Bartlett's Test of Spehericity is statistically significant, it supports the factorability of the correlation matrix [ 27].
The KMO value was 0.82, and Bartlett's Test of Sphericity showed statistical significance (p = 0.000), supporting the factorability of the correlation matrix.
The Kaiser-Meyer-Olkin value was 0.807 and Bartlett's test of sphericity reached statistical significance, supporting the sample adequacy and the factorability of the correlation matrix.
The KMO-value was.85, exceeding the recommended value of.6 [ 30]. Bartlett's test of sphericity was significant, supporting the factorability of the correlation matrix.
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