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Estimated standardized scores on criminal involvement wave 3 and 4 by political ideology.  
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... mean differences in criminal behavior across the political ideology spectrum were detected within and across waves. At Wave 3, F = 14.78 (p b 0.001) and at Wave 4, F = 8.60 (p b 0.001). Results of the ANOVA are presented graphically in Fig. 1. To aid in interpretation, we converted the measures of criminal behavior to Z-scores. As is read- ily visible, individuals who classified themselves as very conservative had the lowest level of self-reported offending across both waves. Aver- age offending scores increased across consecutive ideological classifica- tions. In each ...

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... Despite this, there is strong reason to expect politically conservative regions of the United States to be less open to the use of crowdfunding than are less conservative areas. Conservatism is associated with higher levels of orderliness and politeness (Hirsh, DeYoung, Xu, & Peterson, 2010), lower levels of corporate fraud (Christensen, Dhaliwal, Boivie, & Graffin, 2015) and criminality (Wright, Beaver, Morgan, & Connolly, 2017), and greater performance in business schools (Kemmelmeier, Danielson, & Basten, 2005). ...
... Nowadays, with the rapid development of mobile devices, a huge amount of effort has been devoted to dealing with various tasks in computer vision and machine learning tasks, such as activity recognition [34,41], motion tracking [6,61,64], behavior prediction [35,36,47], and political ideology prediction [2,44,45,62]. Among them, classification based on image sets, as a promising technique, has received significant attention and has been increasingly applied for many practical applications, such as video surveillance [58], action recognition [50], and face recognition [12,19]. ...
... Although this was not representative of the federal election results on the whole, it is nonetheless consistent with how younger people tend to be more liberal. Given that political ideology would certainly be correlated with other factors, we controlled for age, gender, household income level, religion, and race (Wright, Beaver, Morgan, & Connolly, 2017). ...