A new method for re-analyzing evaluation bias: Piecewise growth curve modeling reveals an asymmetry in the evaluation of pro and con arguments

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Abstract

© 2016 Jirschitzka et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.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 proargument side. For the con-arguments, in contrast, the evaluation bias did not feature such a systematic picture.

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Jirschitzka, J., Kimmerle, J., & Cress, U. (2016). A new method for re-analyzing evaluation bias: Piecewise growth curve modeling reveals an asymmetry in the evaluation of pro and con arguments. PLoS ONE, 11(2). https://doi.org/10.1371/journal.pone.0148283

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