Table 11. Hierarchical logistic regression analyses: Predicting pass rates for basic courses based on K-PAT subset score & PARE score

요인	하위검사	1단계						2단계
		B	SE	Wald	p	df	OR	B	SE	Wald	p	df	OR
PARE	PARE	0.11***	.01	122.69	<.001	1	1.118	0.11***	.01	96.48	<.001	1	1.112
K-PAT	인지영역
	도형회전							0.08	.11	0.48	.487	1	1.079
	도형전개도							0.26*	.11	5.28	.022	1	1.291
	척도판독							0.04	.11	0.14	.710	1	1.042
	계기판독							0.34**	.11	8.97	.003	1	1.406
	배관미로							-0.35**	.11	10.68	.001	1	0.705
	기계원리							0.1	.11	0.81	.370	1	1.104
	의사결정							-0.1	.11	0.88	.348	1	0.906
	시각변별							0.05	.1	0.24	.625	1	1.050
	기억							-0.25	.14	3.28	.070	1	0.782
	정보처리영역
	수표해독							0.09	.09	1.1	.295	1	1.096
	속도추정							-0.21	.14	2.53	.112	1	0.807
	추적/회피							-0.41**	.16	7.12	.008	1	0.662
	멀티태스킹(이동)							0.04	.11	0.17	.680	1	1.045
	멀티태스킹(기억)							0.18	.1	3.39	.066	1	1.199
	멀티태스킹(청각)							0.29**	.1	9.06	.003	1	1.333
Model χ2		160.962 (df = 1, p<.001)						235.592 (df = 16, p<.001)
Nagelkerke R2(Δ)		.258						.361(.103)
분류정확도		78.4%						81.0%

주: p<.05,
p<.01,
p<.001.