Page 822 - ISC PROCEEDINGS 21.4

Table 2. Rotated component matrix a

Component
1 2 3 4 5 6
DC3 .831
DC4 .815
DC2 .797

DC5 .688
DC1 .640

SUP3 .873
SUP4 .869
SUP1 .850

SUP2 .752
TEC1 .850
TEC3 .849

TEC2 .814
TEC4 .782
NIF1 .896

NIF2 .879
NIF3 .836

CUR1 .821
CUR3 .815
CUR2 .732

COL2 .787
COL1 .641

COL3 .632
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a. Rotation converged in 6 iterations.
Source: Results of data analysis utilising SPSS
4.2. Regression analysis
The authors employed a multivariate regression analysis, utilising SPSS 26 software, to
evaluate the hypotheses and address the research issue. The analysis was conducted with
the dependent variable identified as innovation in accounting education (INN) at institutions.
The findings indicated that the coefficient of determination R² attained a value of 0.883,
demonstrating the model's substantial explanatory capacity. Due to the presence of
numerous independent variables, the adjusted coefficient of determination (Adjusted R²)
was employed to evaluate the model's fit with greater precision. The adjusted R² value
attained 0.877 and variance inflation factor (VIF) <2, signifying that the regression model is
suitable and the independent variables elucidate 87.7% of the variance in innovation in
accounting teaching methodologies. Other factors not included in the model account for the
remaining 12.3% of the variation in the dependent variable.

821