The Exploratory Factor Analysis (EFA) was conducted using Principal Component Analysis (PCA) as the extraction method and Varimax rotation to achieve a clearer and more ...
The Exploratory Factor Analysis (EFA) was conducted using Principal Component Analysis (PCA) as the extraction method and Varimax rotation to achieve a clearer and more interpretable factor structure. Varimax was selected because it simplifies factor loadings by maximizing the variance of each factor, making item groupings easier to interpret.
The Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy was 0.84, indicating that the data are suitable for factor analysis (values above 0.6 are considered acceptable, and above 0.8 are good). Additionally, Bartlett’s Test of Sphericity was significant (χ² = 356.27, p < .001), confirming that the correlation matrix is not an identity matrix and that the variables are sufficiently correlated for EFA.
After analysis, a total of 12 items were retained, loading onto three factors. The decision to retain three factors was based on eigenvalues greater than 1, as well as inspection of the scree plot, which showed a clear inflection point after the third factor. Furthermore, only items with factor loadings ≥ .50 were retained to ensure practical significance.
A few items were removed during the process due to low loadings (< .50) or cross-loadings on multiple factors, which could reduce the clarity and validity of the factor structure. The final solution demonstrates a clear and interpretable structure, with each factor representing a distinct construct.
In summary, the EFA results indicate that the dataset is appropriate for factor analysis, and the retained items form a coherent three-factor model supported by both statistical criteria and theoretical expectations.