Table 4.
Logistic regression estimates (n = 164) with distractors efficiency as dependent variable (DE1r, DE2r) and item attributes as independent variables.
| DE1r | DE2r | |||||
|---|---|---|---|---|---|---|
| B | Exp(B) | Sig. | B | Exp(B) | Sig. | |
| Constant | 0.71 | 2.04 | 0.482 | 4.64 | 104.10 | 0.001 |
| P-value | -0.04 | 0.96 | 0.001 | -0.07 | 0.93 | <0.001 |
| Item PB correlation | 0.79 | 2.20 | <0.001 | 0.65 | 1.91 | 0.001 |
| Number of options (5 vs. 4) | -1.11 | 0.33 | 0.071 | -1.97 | 0.14 | 0.011 |
| NOTA | 0.50 | 1.65 | 0.281 | 0.31 | 1.36 | 0.568 |
| Inferential vs. descriptive | -0.03 | 0.98 | 0.950 | -0.52 | 0.60 | 0.288 |
| Bloom’s comprehension | -0.21 | 0.81 | 0.663 | -0.21 | 0.81 | 0.706 |
| Bloom’s application | 0.54 | 1.71 | 0.261 | 1.08 | 2.93 | 0.079 |
Nagelkerke R2 = 0.28 (DE1r) and 0.41 (DE2r). DE1r, distractor efficiency measured with the discrimination computed with the PB correlation; DE2r, distractor efficiency measured with Attali and Fraenkel’s discrimination measure; PB, point-biserial.