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SummaryIn a recent study, te Nijenhuis et al. (2017) used a version of Jensen's method of correlated vectors to study the nature of ethnic group differences on Raven's Progressive Matrices test. In this comment, the author points out that this method has been shown to be psychometrically inappropriate in studying group differences in performance on dichotomous (correctly or incorrectly scored) items. Specifically, the method uses item statistics like the item-total correlation that necessarily differ across groups differing in ability and employs a linear model to test inherent non-linear relations. Wicherts (2017) showed that this method can provide correlations far exceeding r=0.44 in cases where the group differences cannot possibly be on g because the items measure different traits across the groups. The psychometric problems with their method cast serious doubts on te Nijenhuis et al.'s conclusions concerning the role of g in the studied group difference in cognitive test performance.

SummaryIn their response to my criticism of their recent article in Journal of Biosocial Science (te Nijenhuis et al., 2017), te Nijenhuis and van den Hoek (2018) raise four points none of which concerns my main point that the method of correlated vectors (MCV) applied to item-level data represents a flawed method. Here, I discuss te Nijenhuis and van den Hoek's four points. First, I argue that my previous application of MCV to item-level data showed that the method can yield nonsensical results. Second, I note that meta-analytic corrections for sampling error, imperfect measures, restriction of range and unreliability of the vectors are futile and cannot help fix the method. Third, I note that even with perfect data, the method can yield negative correlations. Fourth, I highlight the irrelevance of te Nijenhuis and van den Hoek (2018)'s point that my comment had not been published in a peerreviewed journal by referring to my articles in 2009 and 2017 on MCV in peer-reviewed journals.

We present a meta-analytic review of the literature on sex differences in the trust game (174 effect sizes) and the related gift-exchange game (35 effect sizes). Based on parental investment theory and social role theory we expected men to be more trusting and women to be more trustworthy. Indeed, men were more trusting in the trust game (g = 0.22), yet we found no significant sex difference in trust in the gift-exchange game (g = 0.15). Regarding trustworthiness, we found no significant sex difference in the trust game (g = −0.04), and we found men, not women, to be more trustworthy in the gift-exchange game (g = 0.33). These results suggest that men send more money than women do when their money is going to be multiplied, thereby creating an efficiency gain. This so-called "male multiplier effect" may be explained by a stronger psychological tendency in men to acquire resources.

Looking at Open Science and Open Data from a broad perspective. This is the idea behind "Scientific data sharing: an interdisciplinary workshop", an initiative designed to foster dialogue between scholars from different scientific domains which was organized by the Istituto Italiano di Antropologia in Anagni, Italy, 2-4 September 2013.We here report summaries of the presentations and discussions at the meeting. They deal with four sets of issues: (i) setting a common framework, a general discussion of open data principles, values and opportunities; (ii) insights into scientific practices, a view of the way in which the open data movement is developing in a variety of scientific domains (biology, psychology, epidemiology and archaeology); (iii) a case study of human genomics, which was a trail-blazer in data sharing, and which encapsulates the tension that can occur between large-scale data sharing and one of the boundaries of openness, the protection of individual data; (iv) open science and the public, based on a round table discussion about the public communication of science and the societal implications of open science. There were three proposals for the planning of further interdisciplinary initiatives on open science. Firstly, there is a need to integrate top-down initiatives by governments, institutions and journals with bottom-up approaches from the scientific community. Secondly, more should be done to popularize the societal benefits of open science, not only in providing the evidence needed by citizens to draw their own conclusions on scientific issues that are of concern to them, but also explaining the direct benefits of data sharing in areas such as the control of infectious disease. Finally, introducing arguments from social sciences and humanities in the educational dissemination of open data may help students become more profoundly engaged with Open Science and look at science from a broader perspective.