Table 9. Hierarchical logistic regression analyses: Predicting pass rates for screen, basic and advanced courses based on K-PAT total score & PARE score

과정	요인	1단계						2단계
		B	SE	Wald	p	df	OR	B	SE	Wald	p	df	OR
입문	PARE	0.09***	.014	44.48	<.001	1	1.095	0.08***	.014	32.22	<.001	1	1.083
	K-PAT							0.03**	.009	7.537	.006	1	1.026
	Model χ2	160.962 (df = 1, p<.001)						53.787 (df = 2, p<.001)
	Nagelkerke R2	.258						.145(ΔR2 = -.113)
	분류정확도	78.4%						91.7%
기본	PARE	0.11***	.010	122.69	<.001	1	1.118	0.10***	.010	92.09	<.001	1	1.106
	K-PAT							0.05***	.007	40.62	<.001	1	1.046
	Model χ2	45.988 (df = 1, p<.001)						206.064 (df = 2, p<.001)
	Nagelkerke R2	.125						.322(ΔR2 = .197)
	분류정확도	91.4%						80.7%
고등	PARE	0.03	.031	0.78	.378	1	1.027	0.03	.032	0.73	.394	1	1.028
	K-PAT							0.08***	.021	15.4	<.001	1	1.084
	Model χ2	.764 (df = 1, p = .382)						17.541 (df = 2, p<.001)
	Nagelkerke R2	.005						.120(ΔR2 = .115)
	분류정확도	96.0%						96.0%

주: p<.05,
p<.01,
p<.001.