Research on conspiracy theories has proliferated since 2016, in part due to the US election of President Trump, the COVID-19 pandemic, and increasingly threatening environmental conditions. In the rush to publication given these concerning social consequences, researchers have increasingly treated as definitive a 2016 paper by Michael Wood (Political Psychology, 37(5), 695–705, 2016) that concludes that the phrase “conspiracy theory” has no negative effect upon people’s willingness to endorse a claim. We revisit Wood’s findings and its (re)uptake in the recent (...) literature. Is the label “conspiracy theory” a pejorative? If so, does it sway or affect people’s belief in specific claims of conspiracy (i.e. particular conspiracy theories), or is the effect one that concerns claims of conspiracy more generally (i.e. all conspiracy theories)? Through an examination of the conceptual and methodological scope of Wood’s work and the results of our similar quasi-experimental design, we argue that it is premature to suggest the label “conspiracy theory” has no impact on the believability of a claim, or that it has no rhetorical power. (shrink)

Students during the distance education were experiencing solitude and depression in their studies due to no social interaction which led to psychological suffering. In this article, college students' attitudes toward statistics learning were investigated, and its predictors by statistical modeling. Secondary data was extracted from a current study from the literature, summarized using descriptive statistics, and presented in tabular form. As for modeling the predictors of students' attitudes in learning statistics, it was done through multiple linear regression via the ordinary (...) least square (OLS) approach. The finding of statistical calculations revealed that, on average, students possess a "neutral attitude" towards learning statistics online. This suggests that students do not show active involvement and positive engagement in the classroom environment due to some challenges they have encountered during online learning. The regression model I revealed that students who are using mobile phones, fewer study hours in statistics, and intimate relationships with teachers are the significant factors of positive attitude in learning. On the other hand, regression model II showed that low internet signal and low mental health are significant determinants of positive behavior in learning statistics online. Conclusively, students who have suitable gadgets for online learning and with good vibes with their teachers have good performance in class even if they study less, however, effective. Moreover, students with less distraction on social media and with mental stress, tend to deviate their attention in studying their lessons in statistics which positively increases their learning attitude and well-being. (shrink)