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A New Method for Re-Analyzing Evaluation Bias: Piecewise Growth Curve Modeling Reveals an Asymmetry in the Evaluation of Pro and Con Arguments

机译:重新评估评估偏差的新方法:分段增长曲线建模揭示了Pro和Con参数评估中的不对称性

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摘要

In four studies we tested a new methodological approach to the investigation of evaluation bias. The usage of piecewise growth curve modeling allowed for investigation into the impact of people’s attitudes on their persuasiveness ratings of pro- and con-arguments, measured over the whole range of the arguments’ polarity from an extreme con to an extreme pro position. Moreover, this method provided the opportunity to test specific hypotheses about the course of the evaluation bias within certain polarity ranges. We conducted two field studies with users of an existing online information portal (Studies 1a and 2a) as participants, and two Internet laboratory studies with mostly student participants (Studies 1b and 2b). In each of these studies we presented pro- and con-arguments, either for the topic of MOOCs (massive open online courses, Studies 1a and 1b) or for the topic of M-learning (mobile learning, Studies 2a and 2b). Our results indicate that using piecewise growth curve models is more appropriate than simpler approaches. An important finding of our studies was an asymmetry of the evaluation bias toward pro- or con-arguments: the evaluation bias appeared over the whole polarity range of pro-arguments and increased with more and more extreme polarity. This clear-cut result pattern appeared only on the pro-argument side. For the con-arguments, in contrast, the evaluation bias did not feature such a systematic picture.
机译:在四项研究中,我们测试了一种新的方法论方法来调查评估偏差。通过使用分段增长曲线模型,可以调查人们的态度对赞成和反对的说服力等级的影响,这些态度是在从极端骗局到极端赞成的整个论点极性范围内衡量的。而且,该方法提供了机会来测试关于特定极性范围内评估偏差过程的特定假设。我们以现有在线信息门户网站的用户(研究1a和2a)作为参与者进行了两项实地研究,并且以大多数学生为参与者(研究1b和2b)进行了两次互联网实验室研究。在每项研究中,我们都针对MOOC主题(大规模开放式在线课程,研究1a和1b)或M学习(移动学习,研究2a和2b)提出了赞成和反对的观点。我们的结果表明,使用分段增长曲线模型比简单方法更合适。我们研究的一个重要发现是,对前参数或参数的评估偏差不对称:评估偏差出现在前参数的整个极性范围内,并随着越来越多的极端极性而增加。这种清晰的结果模式仅出现在赞成观点的一方。相比之下,对于争论而言,评估偏差并未体现出如此系统的图景。

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