Table 3.
Regression model predicting the number of true news items misclassified as fake.
| B | SE | t | p | |
|---|---|---|---|---|
| Intercept | 0.016 | 0.071 | 0.220 | .826 |
| Age | -0.241 | 0.044 | -5.485 | <.001 |
| Gender | 0.151 | 0.099 | 1.537 | .125 |
| Education | -0.085 | 0.087 | -0.981 | .327 |
| Positive Qualities Exaggeration | -0.000 | 0.043 | -0.004 | .997 |
| Negative Qualities Understatement | 0.029 | 0.044 | 0.655 | .513 |
| BEFKI GC-K | -0.136 | 0.044 | -3.108 | .002 |
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, significances (i.e., whether they are < 0.05 or ≥ 0.05) of results do not change.