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Table 3 Summary of the logistic regression analysis for academic achievement in physics

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

Parameter

B (SE)

OR [95% CI]

B (SE)

OR [95% CI]

B (SE)

OR [95% CI]

B (SE)

OR [95% CI]

Intercept

− 0.852 (0.312)

0.43

− 1.958 (0.581)

0.14

− 1.816 (0.540)

0.16

− 1.775 (0.530)

0.17

HS GPA

1.282 (0.365)

3.60*** [1.8, 7.4]

0.513 (0.534)

1.67 [0.6, 4.8]

0.477 (0.505)

1.61 [0.6, 4.3]

− 0.450 (0.440)

1.77 [0.7, 4.6]

Knowledge of facts

–

–

0.704 (0.503)

2.02 [0.8, 5.4]

0.754 (0.496)

2.11 [0.8, 5.6]

–

–

Knowledge of meaning

–

–

1.035 (0.530)

2.82Ç‚ [1.0, 8.0]

1.232 (0.507)

3.43* [1.3, 9.3]

1.475 (0.477)

4.37** [1.7, 11.1]

Integration of knowledge

–

–

0.556 (0.521)

1.74 [0.6, 4.8]

–

–

–

–

Application of knowledge

–

–

1.259 (0.592)

3.52* [1.1, 11.2]

1.300 (0.576)

3.67* [1.2, 11.3]

1.553 (0.555)

4.73** [1.6, 14.0]

Correctly classified cases

71.2%

84.9 %

82.2 %

82.2 %

Nadelkerke’s R2

.287

.627

.615

.590

Chi-square Tests

17.29***

45.15***

43.98***

41.55***

  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. ǂp < .10
  4. *p < .05
  5. **p < .01
  6. ***p < .001