Extremism Leads to Ostracism

2020 ◽  
Vol 51 (3) ◽  
pp. 149-156
Author(s):  
Andrew H. Hales ◽  
Kipling D. Williams

Abstract. Ostracism has been shown to increase openness to extreme ideologies and groups. We investigated the consequences of this openness-to-extremity from the perspective of potential ostracizers. Does openness-to-extremity increase one’s prospects of being ostracized by others who are not affiliated with the extreme group? Participants rated willingness to ostracize 40 targets who belong to activist groups that vary in the type of goals/cause they support (prosocial vs. antisocial), and the extremity of their actions (moderate vs. extreme). Mixed-effects modeling showed that people are more willing to ostracize targets whose group engages in extreme actions. This effect was unexpectedly stronger for groups pursuing prosocial causes. It appears openness-to-extremity entails interpersonal cost, and could increase reliance on the extreme group for social connection.

2019 ◽  
Vol 13 ◽  
pp. 408-414 ◽  
Author(s):  
Edinéia A.S. Galvanin ◽  
Raquel Menezes ◽  
Murilo H.X. Pereira ◽  
Sandra M.A.S. Neves

2015 ◽  
Vol 5 (1) ◽  
pp. 135-152 ◽  
Author(s):  
Jan Vanhove

I discuss three common practices that obfuscate or invalidate the statistical analysis of randomized controlled interventions in applied linguistics. These are (a) checking whether randomization produced groups that are balanced on a number of possibly relevant covariates, (b) using repeated measures ANOVA to analyze pretest-posttest designs, and (c) using traditional significance tests to analyze interventions in which whole groups were assigned to the conditions (cluster randomization). The first practice is labeled superfluous, and taking full advantage of important covariates regardless of balance is recommended. The second is needlessly complicated, and analysis of covariance is recommended as a more powerful alternative. The third produces dramatic inferential errors, which are largely, though not entirely, avoided when mixed-effects modeling is used. This discussion is geared towards applied linguists who need to design, analyze, or assess intervention studies or other randomized controlled trials. Statistical formalism is kept to a minimum throughout.


Technometrics ◽  
2010 ◽  
Vol 52 (3) ◽  
pp. 265-277 ◽  
Author(s):  
Peihua Qiu ◽  
Changliang Zou ◽  
Zhaojun Wang

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