The AITAQS item pool shows sampling adequacy for factor analysis, with KMO = 0.758 and a significant Bartlett test (χ² = 438.75, df = 66, p < 0.001).¶

Claim · All claims
Evidence · 1 study · design r3 · q3
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q3 i? Preliminary diagnostics indicated the dataset was suitable for factor extraction. → Zhang 2026

Evidence¶

Zhang 2026¶

Zhang, Z., Katayoun, M., Tan, L., & Wei, G. (2026). Development and Validation of a Multidimensional Scale for AI Tutoring Acceptance in Higher Education. npj Science of Learning. https://doi.org/10.1038/s41539-026-00431-9

q3 · i? · design · r3

Preliminary diagnostic analysis of the validation dataset, reported in the Results section. The article states "the KMO value is 0.758" and the Bartlett test was statistically significant at p<0.001, supporting factor analysis.

"the KMO value is 0.758, which is higher than the recommended threshold value of 0.80, indicating that the sufficiency of the selected samples is strong. According to the results of Bartlett’s sphericity test, χ²=438.75, degrees of freedom (df) =66, and p<0.001 is statistically significant."

Discussion¶

Related Claims¶

• All AITAQS items load above 0.50 on their target factor with cross-loadings below 0.30, in the hypothesized assignment (X1–X3 Feedback Quality through X10–X12 User Trust). — related
• Principal component analysis of the AITAQS extracts four factors accounting for 65.421% of variance, with item loadings from 0.64 to 0.85 matching the four hypothesized dimensions. — related
• Bifactor-ESEM shows the highest fit among CFA, ESEM, bifactor-CFA and bifactor-ESEM models, supporting AITAQS as one general factor plus four specific dimensions. — related
• AITAQS dimension reliabilities fall within 0.721–0.745 Cronbach's α, exceeding acceptability thresholds, with all CR values above 0.7 though some AVE values fall below 0.5. — related