Table 3.

Study 1. Rotated principal-components matrix: New measures plus fluid-intelligence tests ^a.

	Component
	1	2
Hypotheses (New)	0.73	−0.09
Experiments (New)	0.82	0.17
Conclusions (New)	0.79	0.00
Letter Sets	0.02	0.83
Number Series	0.02	0.84
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a Rotation converged in 3 iterations.

Notes: Two principal components had Eigenvalues greater than 1. Component 1 had an Eigenvalue of 1.86, accounting for 37.1% of the variance in the data. Component 2 had an Eigenvalue of 1.41, accounting for 28.2% of the variance in the data. Cumulative percent variance accounted for was 65.3%.