Table 3.

Regression analyses for comprehensive diagnostic score as outcome

Predictor	b	ß	ß 95% CI	p	Model test and fit
Model 1					F(3, 102) = 17.21, p < 0.001
Intercept	− 2.30***			< 0.001	R2 = 0.34
Hypothesis generation	1.09**	0.26	[0.10, 0.42]	0.002	Adj. R2 = 0.32
Evidence generation	2.40***	0.40	[0.24, 0.56]	< 0.001
Evidence evaluation	1.76**	0.26	[0.10, 0.42]	0.002
Model 2a					F(2, 103) = 12.55, p < 0.001
Intercept	− 1.42***			< 0.001	R2 = 0.20
Conceptual knowledge	1.07	0.19	[− 0.02, 0.40]	0.074	Adj. R2 = 0.18
Strategic knowledge	1.66**	0.31	[0.10, 0.52]	0.005
Model 2b					F(5, 100) = 14.05, p < 0. 001
Intercept	− 2.92***			< 0.001	R2 = 0.41
Conceptual knowledge	1.07*	0.19	[0.01, 0.37]	0.043	Adj. R2 = 0.38
Strategic knowledge	0.86	0.16	[− 0.04, 0.36]	0.121
Hypothesis generation	1.14***	0.27	[0.12, 0.43]	< 0.001
Evidence generation	1.60**	0.27	[0.09, 0.44]	0.003
Evidence evaluation	1.65**	0.24	[0.08, 0.40]	0.003

Model 1 is a multiple regression containing diagnostic activities variables. Model 2 is a hierarchical regression consisting of knowledge variables in Model 2a and knowledge and diagnostic activities in Model 2b. b represents unstandardized regression weights. ß represents standardized regression weights. CI = confidence interval. *p < 0.05, **p < 0.01, ***p < 0.001