For my EFA, I employed Principal Component Analysis with Varimax rotation. The KMO value was .864, and Bartlett’s test reached statistical significance, X2 (120)= 2458.122,...
For my EFA, I employed Principal Component Analysis with Varimax rotation. The KMO value was .864, and Bartlett’s test reached statistical significance, X2 (120)= 2458.122, p < .001, confirming that the dataset was well-suited for factor analysis.
A total of 16 items were included in the final analysis, and 3 factors were retained based on Eigenvalues greater than 1. This structure aligned perfectly with my theoretical framework. The items for 'Perceived Usefulness' and 'Ease of Use' loaded distinctly onto two separate factors. However, the items intended for 'Attitude Toward AI' showed a more complex pattern, with most items loading on a third factor. While the majority of items were retained due to high loadings (above .55), one item from the 'Ease of Use' scale was excluded due to a significant cross-loading, as it also showed moderate correlation with 'Perceived Usefulness'.