The effect of preregistration on trust in empirical research findings: results of a registered report

The crisis of confidence has undermined the trust that researchers place in the findings of their peers. In order to increase trust in research, initiatives such as preregistration have been suggested, which aim to prevent various questionable research practices. As it stands, however, no empirical evidence exists that preregistration does increase perceptions of trust. The picture may be complicated by a researcher's familiarity with the author of the study, regardless of the preregistration status of the research. This registered report presents an empirical assessment of the extent to which preregistration increases the trust of 209 active academics in the reported outcomes, and how familiarity with another researcher influences that trust. Contrary to our expectations, we report ambiguous Bayes factors and conclude that we do not have strong evidence towards answering our research questions. Our findings are presented along with evidence that our manipulations were ineffective for many participants, leading to the exclusion of 68% of complete datasets, and an underpowered design as a consequence. We discuss other limitations and confounds which may explain why the findings of the study deviate from a previously conducted pilot study. We reflect on the benefits of using the registered report submission format in light of our results. The OSF page for this registered report and its pilot can be found here: http://dx.doi.org/10.17605/OSF.IO/B3K75.

Bayesian rank-based hypothesis testing for the rank sum test, the signed rank test, and Spearman's ρ

Bayesian inference for rank-order problems is frustrated by the absence of an explicit likelihood function. This hurdle can be overcome by assuming a latent normal representation that is consistent with the ordinal information in the data: the observed ranks are conceptualized as an impoverished reflection of an underlying continuous scale, and inference concerns the parameters that govern the latent representation. We apply this generic data-augmentation method to obtain Bayes factors for three popular rank-based tests: the rank sum test, the signed rank test, and Spearman's ρs.

Many Analysts, One Data Set: Making Transparent How Variations in Analytic Choices Affect Results

Twenty-nine teams involving 61 analysts used the same data set to address the same research question: whether soccer referees are more likely to give red cards to dark-skin-toned players than to light-skin-toned players. Analytic approaches varied widely across the teams, and the estimated effect sizes ranged from 0.89 to 2.93 ( Mdn = 1.31) in odds-ratio units. Twenty teams (69%) found a statistically significant positive effect, and 9 teams (31%) did not observe a significant relationship. Overall, the 29 different analyses used 21 unique combinations of covariates. Neither analysts’ prior beliefs about the effect of interest nor their level of expertise readily explained the variation in the outcomes of the analyses. Peer ratings of the quality of the analyses also did not account for the variability. These findings suggest that significant variation in the results of analyses of complex data may be difficult to avoid, even by experts with honest intentions. Crowdsourcing data analysis, a strategy in which numerous research teams are recruited to simultaneously investigate the same research question, makes transparent how defensible, yet subjective, analytic choices influence research results.

Registered Replication Report

According to the facial feedback hypothesis, people’s affective responses can be influenced by their own facial expression (e.g., smiling, pouting), even when their expression did not result from their emotional experiences. For example, Strack, Martin, and Stepper (1988) instructed participants to rate the funniness of cartoons using a pen that they held in their mouth. In line with the facial feedback hypothesis, when participants held the pen with their teeth (inducing a “smile”), they rated the cartoons as funnier than when they held the pen with their lips (inducing a “pout”). This seminal study of the facial feedback hypothesis has not been replicated directly. This Registered Replication Report describes the results of 17 independent direct replications of Study 1 from Strack et al. (1988), all of which followed the same vetted protocol. A meta-analysis of these studies examined the difference in funniness ratings between the “smile” and “pout” conditions. The original Strack et al. (1988) study reported a rating difference of 0.82 units on a 10-point Likert scale. Our meta-analysis revealed a rating difference of 0.03 units with a 95% confidence interval ranging from −0.11 to 0.16.

Testing the counter model for perceptual identification: effects of repetition priming and word frequency

The counter model for perceptual identification (Ratcliff & McKoon, 1997) differs from alternative views of word recognition in two important ways. First, it assumes that prior study of a word does not result in increased sensitivity but, rather, in bias. Second, the effects of word frequency and prior study are explained by different mechanisms. In the present experiment, study status and word frequency of target and foil were varied independently. Using a forced-choice task, we replicated the bias effect. However, we also found several interactions between frequency and prior study that are in direct conflict with the counter model. Most important, prior study of both alternatives resulted in an attenuation of the frequency effect and an increase in performance for low-frequency targets, but not for high-frequency targets. These findings suggest that the effects of frequency and prior study are not mediated by completely independent mechanisms.

A criterion-shift model for enhanced discriminability in perceptual identification: a note on the counter model

The original version of the counter model for perceptual identification (Ratcliff & McKoon, 1997) assumed that word frequency and prior study act solely to bias the identification process (i.e., subjects have a tendency to prefer high-frequency and studied low-frequency words, irrespective of the presented word). In a recent study, using a two-alternative forced-choice paradigm, we showed an enhanced discriminability effect for high-frequency and studied low-frequency words (Wagenmakers, Zeelenberg, & Raaijmakers, 2000). These results have led to a fundamental modification of the counter model: Prior study and high frequency not only result in bias, but presumably also result in a higher rate of feature extraction (i.e., better perception). We demonstrate that a criterion-shift model, assuming limited perceptual information extracted from the flash as well as a reduced distance to an identification threshold for high-frequency and studied low-frequency words, can also account for enhanced discriminability.