Transparent communication of research is key to foster understanding within and beyond the scientific community. Increased focus on reporting effect sizes in addition of p-value based significance statements may improve scientific communication with the general public. Across two studies (N = 446), we compared informativeness ratings for five effect sizes, Bayes Factor and commonly used significance statements. Results showed that Cohen’s U3 was rated as most informative. For example, 77% of participants found it more informative than Cohen’s d. We therefore suggest that Cohen’s U3 is used when scientific findings are communicated.

Traditional models of personality traits and of human values have frequently pointed to theoretical interconnections between these constructs, which both refer to stable psychological characteristics. Yet, decades of study have yielded no consensus about precisely how these constructs interrelate. The present research takes a significant step forward by empirically examining consequences of matching semantic content between traits and values. As an example of such semantic matching, people can vary in the extent to which they are honest and curious (traits), while varying in the extent to which they believe that it is important to be honest and curious (values). We used alternate forms of the HEXACO-trait model (Ashton et al., 2004) and Schwartz’s (1992) 10-value type model to examine this role of semantic content. As expected, we found that the HEXACO and Schwartz models assess different semantic content from each other. We also predicted and found that both the semantic content of the measures (e.g., honesty vs curiosity) and the type of measurement (i.e., as traits or values) determined relations to other variables (e.g., negative affect, internalization of moral identity). These findings show that greater theoretical precision about the relations between traits and values can be achieved by explicitly distinguishing the models’ axiological starting points and focus of assessment.

The Covid-19 pandemic has far-reaching implications for researchers. For example, many researchers cannot access their labs anymore and are hit by budget-cuts from their institutions. Luckily, there are a range of ways how high-quality research can be conducted without funding and face-to-face interactions. In the present paper, I discuss eight such possibilities, including meta-analyses, secondary data analyses, web-scrapping, scientometrics, or sharing one’s expert knowledge (e.g., writing tutorials). Most of these possibilities can be done from home, as they require only access to a computer, the internet, and time; but no state-of-the art equipment or funding to pay for participants. Thus, they are particularly relevant for researchers with limited financial resources beyond pandemics and quarantines.