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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94 The Development of the Digital Addiction Scale for the University Students: Reliability and Validity Study analysis made again. According to the renewed factor analysis, it was found out that there is a structure with 5 sub-dimensions which explains the 58.875% of the variance (Bartlett test =3492.027 (p<.01); KMO = .899). Similarly, as it was seen that the 7 th item did not have the relevant factor loading, it was extracted, and the factor analysis was made third time. It was revealed that the DAS explains the 59.510% of the total variance and has a five-dimensional structure in the last factor analysis (Bartlett test = 3203.754 (p<.01); KMO = .899). The factor loadings of each item remaining in the scale are above 0.45. The value is 0.45 or above 0.45 is considered as a good criterion [25]. The five items gathered in the first factor called as Overuse . It explains the 13.522% of the variance. The second factor consisting of 3 items and called as Non-restraint explains the 12,273% of the variance. The third factor composed of 4 items explains the 11.840% of the variance. It was named as Inhibiting the Flow of Life . The forth factor called as Emotional State consists of 4 items and explains the 11.361% of the variance. The fifth dimension composed of 3 items and explaining the 10.512% of the variance was called as Dependence . The findings obtained as a result of the exploratory factor analysis were shown in Table 1. 3.2. Confirmatory Factor Analysis (CFA) Confirmatory Factor Analysis (CFA) was made in order to test whether the DAS would be confirmed as a model or not. CFA made with LISREL is an advanced technique used to test the theories related to the latent variables [26]. The structural model in Figure 1 was tested with LISREL. In Figure 1, the standardized path coefficients showing the item-factor relation are above the arrows drown from the latent variable (the sub-dimensions in this study) to the measurement variables (the scale items in this study). All of the path coefficients were seen to be statistically significant. This means that the items made significant contributions to the the sub-dimensions they belong to.
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  • Fall '09
  • Pearson product-moment correlation coefficient, university students

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