Table 3.
Predictors of increased number of false items believed to be true
Poisson regression model predicting count of false items rated as true at end | |||
---|---|---|---|
Coefficient | Standard error | p value | |
Warning condition | |||
Warning-After | 0.128 | 0.108 | 0.238 |
Warning-During | 0.087 | 0.110 | 0.428 |
Order condition | |||
Memory items first | 0.086 | 0.087 | 0.322 |
Personal variables | |||
Male gender (relative to female) | 0.031 | 0.090 | 0.732 |
Ideology (higher is more conservative) | − 0.010 | 0.027 | 0.718 |
Interest in politics | − 0.034 | 0.044 | 0.434 |
Conspiratorial disposition** | 0.108 | 0.035 | 0.002 |
Online news variables | |||
Social media usage | 0.015 | 0.043 | 0.717 |
Trust in social media | − 0.015 | 0.051 | 0.765 |
Trust in online news | − 0.021 | 0.054 | 0.702 |
True news stories rated as true*** | 0.074 | 0.022 | 0.001 |
Constant | − 0.500 | 0.312 | 0.109 |
Items with *** were statistically significant at p < 0.001, ** are significant at p < 0.01, and others were not significant (p > 0.05). The reference categories were Warning Before for Warning condition, Bias awareness questions first for Order condition, and female for Gender (with other genders excluded due to sample size)