Table 4.

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

Factor
	1	2
Hypotheses (New)	0.52	−0.05
Experiments (New)	0.80	0.16
Conclusions (New)	0.63	0.01
Letter Sets	0.03	0.57
Number Series	0.02	0.73
Extraction Method: Principal Axis Factoring.
Rotation Method: Varimax with Kaiser Normalization.
a Rotation converged in 3 iterations.

Notes: Two principal factors had Eigenvalues greater than 1. Factor 1 had an Eigenvalue of 1.86, accounting for 37.1% of the variance in the data. Factor 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%.