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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