Exact(28)
Secondly, PCA, a factorization method, was utilized to extract factors explaining maximum variance of data.
There are multiple ways to extract factors.
A literature review was conducted to extract factors that may differentiate people with respect to human error-proneness.
First, exploratory factor analysis was used to extract factors from 33 items of the scale in its application to 149 management major students.
We used principle component method to analyze these eight items via maximum variance rotation, and to extract factors of general trust and particularized trust.
For the first study, to extract factors from the FLCAS utilizing exploratory factor analysis, management major students were asked to participate in this project.
Similar(32)
Principal Component Analysis (PCA) with Varimax rotation was used to extract factor structures.
Normalized varimax rotation was applied to the extracted factors.
Below, we present the profiles of the four extracted factors.
Table 5 displays the factor loadings of the 65 items under the extracted factors.
The 12 extracted factors accounted for 53.25% of the total variance.
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