Research Article
Structural Model of Students’ Interest and Self-Motivation to Learning Mathematics
Table 3
Rotated component matrix.
| Variables | Component | 1 | 2 | 3 | 4 | 5 |
| I1 | 0.704 | | | | | I5 | 0.636 | | | | | I23 | 0.631 | | | | | I27 | 0.606 | | | | | I30 | 0.59 | | | | | C21 | | 0.712 | | | | C25 | | 0.701 | | | | C28 | | 0.673 | | | | C32 | | 0.658 | | | | C36 | | 0.65 | | | | M11 | | | 0.619 | | | M13 | | | 0.604 | | | M16 | | | 0.6 | | | M37 | | | 0.549 | | | M38 | | | 0.504 | | | A8 | | | | 0.575 | | A14 | | | | 0.568 | | A18 | | | | 0.556 | | A31 | | | | 0.549 | | A34 | | | | 0.482 | | U12 | | | | | 0.615 | U15 | | | | | 0.606 | U24 | | | | | 0.599 | U33 | | | | | 0.552 | U35 | | | | | 0.517 |
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Extraction method: principal component analysis. Rotation method: varimax with Kaiser normalization. Rotation converged in 6 iterations.
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