Since Pay-What-You-Want is a relatively new field of study, the influence factors during the pricing process are not sufficiently explored. It was hypothesized that in a hypothetical Pay-What-YouWant situation, increased social interaction and social norm compliance would lead to a higher willingness to pay for a travel mug. In a laboratory experiment, 79 German students were randomly assigned to three groups which varied in the degree of social interaction. Social norm compliance was assessed with a questionnaire. A 3 × 3 between-group factorial ANOVA showed a significant main effect for social interaction (p = 0.025, η 2 = 0.08), whilst social norm compliance was slightly not significant (p = 0.067, η 2 = 0.03). Follow-up comparisons were calculated and the results discussed. The findings implicate that especially the degree of social interaction should be regarded, both by researchers and practitioners, as an important situational factor influencing the price in a Pay-What-You-Want situation.

Freezing is an adaptive defensive response to a stressful event. Recent research suggests that freezing not only occurs in response to physical threats but also in response to social threats (e.g., angry faces; Roelofs et al. in Psychol Sci 21:1575-1581, 2010). Given the practical and theoretical importance of this finding, the current study aimed to replicate and extend it. Following the original study, we measured heart rate while participants viewed emotional faces (angry, happy, neutral). Extending the original study, we included a baseline measure and performed additional, more fine-grained analyses. Our results support the hypothesis that participants show physiological signs of freezing when looking at angry faces. Importantly, we also find this effect when comparing heart rate in the angry block to baseline levels. Interestingly, the heart rate effects are explained by deceleration in the first 30 s of the 1-min angry block, but not in the second 30 s. Like Roelofs et al., we find evidence that the effects are modulated by state anxiety, but our effects are only marginal and we do not replicate the negative correlation between heart rate and state anxiety in the angry block. In general, we thus find evidence for physiological signs of freezing in response to social threat. We discuss implications and venues for future research.

No abstract

No abstract

Sometimes interesting statistical findings are produced by a small number of “lucky” data points within the tested sample. To address this issue, researchers and reviewers are encouraged to investigate outliers and influential data points. Here, we present StatBreak, an easy-to-apply method, based on a genetic algorithm, that identifies the observations that most strongly contributed to a finding (e.g., effect size, model fit, p value, Bayes factor). Within a given sample, StatBreak searches for the largest subsample in which a previously observed pattern is not present or is reduced below a specifiable threshold. Thus, it answers the following question: “Which (and how few) ‘lucky’ cases would need to be excluded from the sample for the data-based conclusion to change?” StatBreak consists of a simple R function and flags the luckiest data points for any form of statistical analysis. Here, we demonstrate the effectiveness of the method with simulated and real data across a range of study designs and analyses. Additionally, we describe StatBreak’s R function and explain how researchers and reviewers can apply the method to the data they are working with.