Eigenvalue is an important variable in deciding the number of factors in the

Eigenvalue is an important variable in deciding the

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number of variables was aimed. Eigenvalue is an important variable in deciding the number of factors in the factor analysis. The factors, whose eigenvalues are 1 and above 1, are considered as significant factors. The items in which there is a difference less than 0.10 between their factor loadings in two factors are called as common items in the exploratory factor analysis. It is suggested that common items be extracted from the scale [22]. As a result of the factor analysis, it was seen that there is a structure consisting of five sub-dimensions and explaining the 55.075% of the variance (Bartlett test =5054.075 (p<.01); KMO = .922). Yet, the factor analysis was repeated by extracting 6 items (items 4-20-2-2-26-22) as they were common items. Accordingly, it was observed that there is a structure composed of five sub-dimensions and explaining the 57.723% of the variance (Bartlett test = 3692.483 (p<.01); KMO = .901). Yet, as the 13 th item did not have the relevant factor loading, it was extracted, and the factor

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• Fall '09
• Pearson product-moment correlation coefficient, university students

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