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

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

Attitudinal evaluation bias: Sign, size, and significance of the influence of attitude on the persuasiveness ratings for each argument for each of the four studies.

<p>(*) Bayesian 99% credibility interval for evaluation bias does not contain the value of zero (significant). (ns) Bayesian 99% credibility interval contains the value of zero (not significant). (†) A 95% credibility interval would not contain the value of zero.</p

Pairwise comparisons between attitudinal evaluation biases within the pro-arguments.

<p>Pairwise comparisons between attitudinal evaluation biases within the pro-arguments.</p

Piecewise growth curves: M-learning.

<p>(A) Study 2a. (B) Study 2b.</p

Pairwise comparisons between attitudinal evaluation biases within the con-arguments.

<p>ns: Bayesian 99% credibility interval contains the value of zero (not significant).</p

Model comparisons (model with parameter constraints: β₁₀ and β₂₀ as well as β₁₁ and β₂₁ are held equal) with the deviance information criterion (DIC).

<p>Model comparisons (model with parameter constraints: β₁₀ and β₂₀ as well as β₁₁ and β₂₁ are held equal) with the deviance information criterion (DIC).</p

Hypothetical persuasiveness ratings if the continuous metric of arguments’ polarity is taken into account.

<p>Hypothetical persuasiveness ratings if the continuous metric of arguments’ polarity is taken into account.</p

Piecewise growth curves: MOOCs.

<p>(A) Study 1a. (B) Study 1b.</p

Die Architektur von Lernräumen

In diesem Beitrag beschäftigt sich der Autor mit physikalischen und virtuellen Lernumgebungen. Beschrieben werden die zu beeinflussenden Faktoren sowie die Wirkungsweise bei Lernräumen. Des Weiteren werden die Vorteile von digitalen Denkräumen dargestellt. (DIPF/bc