Analytic strategy
REGRESSION
Participants of the intervention condition (contrasting to the control) were expected to provide less correct ratings on fake news (H1), and they would intend to share fake news to a smaller extent (H2), even after controlling for relevant individual differences (H1a, H2a). We also expected that fake news accuracy ratings would be negatively related to analytic thinking (H3), and need for cognition (H6); but positively to bullshit receptivity (H4), digital literacy (H5), socioeconomic status (parental education and minority status, H7), conspiracy mentality (H8), and conservative party voters (H9). Finally, we expected that treatment (contrasting to control) would lead to lower levels of bullshit receptivity for the immediate and the longer-run results (one-month follow-up, H10).Based on the preregistration, for the two main hypotheses, we use OLS regression analyses to measure the effect of the condition on fake news accuracy (H1) and sharing intention (H2). Subsequently, we control for relevant (correlated) individual differences and assess the effect of the treatment above and beyond these individual differences. We run these analyses for the immediate and the longer-run results (one-month follow-up). OLS regression analyses and correlations are used to assess the relationship between the above-mentioned individual differences (H3-H8) and fake news accuracy ratings as well as sharing intentions. With an OLS regression analysis, we assess the effect of the treatment on bullshit receptivity scores after controlling for pre-intervention bullshit scores (H10). We run these analyses for the immediate and the longer-run results (one-month follow-up).