Table 2.
Regression model predicting the number of fake news items misclassified as true.
| B | SE | t | p | |
|---|---|---|---|---|
| Intercept | 0.038 | 0.073 | 0.524 | .601 |
| Age | -0.058 | 0.045 | -1.293 | .197 |
| Gender | 0.105 | 0.105 | 1.004 | .316 |
| Education | -0.104 | 0.090 | -1.157 | .248 |
| Positive Qualities Exaggeration | 0.068 | 0.045 | 1.512 | .131 |
| Negative Qualities Understatement | -0.135 | 0.046 | -2.951 | .003 |
| ICAR | -0.077 | 0.045 | -1.717 | .087 |
| Extraversion | 0.064 | 0.045 | 1.413 | .158 |
Note. Only the ICAR, BEFKI GC-K, and BFI scales, which were significantly associated with the respective Fake and True News Test score in the zero-order correlations were included. All variables except gender and education were standardized before inclusion in the model; gender: 0 = men, 1 = women (individuals stating non-binary gender identity are not included; standardization was implemented in the men and women only sample); education: 0 = no university degree, 1 = university (of applied sciences) degree. If education and the KSE-G scales are not included, the results of age and the ICAR reach significance (p < 0.05).