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Primary factor analysis points to a two-principal-components model.
Primary factor analysis of item pool 1 pointed to a 5-factor solution.
Primary factor analysis of item pool pointed to a 7-factor solution, which would explain 60.4% of variance.
Primary factor analysis pointed to a 3-factor solution (which explains 69% of variance) with one 4-item factor (eigenvalue 2.9) and two 2-item factors (eigenvalues 1.5 and 1.1, respectively (Table 2).
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Primary component factor analysis revealed a single factor structure which would explain 39.8% of the variance (Table 2).
Subsequently, the underlying factor structure of the theoretical dimensions was investigated using primary component analysis.
Primary component analysis (PCA) with oblimin rotation was used to evaluate and extract the factors of each dimension.
This resulted in selection of 10 primary questions whose factor analysis results are shown in Table 3.
Kernel primary component analysis.
Sometimes, measures have been statistically reduced to a single component of variance, or primary factor, before meta-analysis (e.g., Olea & Ree, 1994).
To determine the mutual connections between the primary characteristics I used factor analysis.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com