Table 4. Results of Bayes factors analyses for Experiment 2.
| Outcome Measure | Prediction | Bayes Factor Prediction vs. Alternative |
|---|---|---|
| Study Time | NR = UR2 | 4.3 |
| NR > UR1 | 9.7 x 1010 | |
| NR > PR1 | 95196 | |
| NR > GR1 | 1.4 x 108 | |
| RE_UR > RE_GR | 16.6 | |
| RE_GR > RE_PR | 3.3 | |
| % Passed | NR = UR2 | 5.9 |
| NR > UR1 | 7.8 x 108 | |
| NR > PR1 | 1.7 x 106 | |
| NR > GR1 | 36421 | |
| GR1 > UR1 | 5.4 | |
| PR1 > GR1 | 24.5 |
Outcome measure denotes the outcome measure examined, prediction specifies the effect by the model. All Bayes factors express the ratio of evidence for the predicted effect against the alternative. Thus, values greater than 1 signify evidence in favor of the prediction, whereas values between 1–0 express evidence for the alternative, and a value of 1 signifies no evidence in either direction. BFs > 3 or < .33 are considered as strong evidence whereas BFs > 100 or < .01 are considered as decisive evidence [16]. RE_UR denotes the magnitude of the resit effect on study-time investment for the unconditional resit condition. Same terminology applies for the other two resit conditions.