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.¶

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? PCA with Varimax rotation recovered the hypothesized four-factor structure with substantial explained variance. → 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

PCA with Varimax rotation on the validation sample. Four factors with eigenvalues above 1.00 accounted for "65.421% of the total variance", exceeding the 50% criterion.

"a total of four factors with feature values greater than 1.00 were extracted, and accounting for 65.421% of the total variance. This cumulative variance exceeds the generally accepted criterion of 50 %"

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). — a narrower finding that bears on this claim
• 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). — 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
• Exploratory factor analysis in the pilot stage supported a four-factor solution, with three items removed (a weak loading, a cross-loading, and a low item–total correlation with duplicated content) — related