Skip to main content

Table 2 Summary of the logistic regression analysis for academic achievement in biology

From: Assessing prior knowledge types as predictors of academic achievement in the introductory phase of biology and physics study programmes using logistic regression

 

Model A

Model B

Model C

Model D

Model E

Parameter

B (SE)

OR [95% CI]

B (SE)

OR [95% CI]

B (SE)

OR [95% CI]

B (SE)

OR [95% CI]

B (SE)

OR [95% CI]

Intercept

0.769 (0.197)

2.16

0.869 (0.219)

2.38

0.842 (0.218)

2.32

0.855 (0.216)

2.35

0.867 (0.215)

2.38

HS GPA

0.203 (0.193)

1.23 [0.8, 1.8]

–

–

–

–

–

–

–

–

Knowledge of facts

–

–

0.304 (0.261)

1.36 [0.8, 2.3]

0.292 (0.257)

1.34 [0.8, 2.2]

0.278 (0.256)

1.32 [0.8, 2.2]

–

–

Knowledge of meaning

–

–

0.780 (0.297)

2.18** [1.2, 3.9]

0.752 (0.282)

2.12** [1.2, 3.7]

0.706 (0.264)

2.03** [1.2, 3.4]

0.839 (0.235)

2.32*** [1.5, 3.7]

Integration of knowledge

–

–

− 0.083 (0.270)

0.921 [0.5, 1.6]

–

–

–

–

–

–

Application of knowledge

–

–

− 0.090 (0.230)

0.914 [0.6, 1.4]

− 0.108 (0.222)

0.90 [0.6, 1.4]

–

–

–

–

Correctly classified cases

68.3%

70.0 %

70.8 %

70.8 %

68.3 %

Nadelkerke’s R2

.013

.188

.187

.185

.172

Chi-square tests

1.11 n.s.

17.28**

17.18**

16.95***

15.74***

  1. Chi-square tests test the deviance of the fitted model against the null model. Not significant predictors were excluded in the following model. Dependent variable = academic achievement (1 = passed all subject-specific courses)
  2. OR odds ratio
  3. **p < .01
  4. ***p < .01