Guest post by Quan-Hoang Vuong of Centre for Interdisciplinary Social Research, Western University Hanoi, Vietnam

A growing awareness of the lack of reproducibility has undermined society’s trust and esteem in social sciences. In some cases, well-known results have been fabricated or the underlying data have turned out to have weak technical foundations.

{credit}Quan Hoang Vuong{/credit}

Many researchers have investigated the plausibility of findings in the social sciences and humanities. A typical example is the mysterious Critical Minimum Positivity Ratio 2.9013 by Fredrickson and Losada (2005), which claimed to show that there exists such a positivity ratio and that “an individual’s degree of flourishing could be predicted by that person’s ratio of positive to negative emotions over time”. This ratio had once been a well-known, highly influential and greatly admired psychological “constant” until it was shown by Brown, Sokal and Friedman (2013) to be an unfounded, arbitrary and meaningless number.

To address the plausibility problem, I suggest that a combination of open data, open peer-review and open community dialogue, could serve as a feasible option for the social sciences.

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Guest post by Xi-Nian Zuo, Project Coordinator and Co-Founder of Consortium for Reliability and Reproducibility (CoRR), Professor of Psychology and Director of the Magnetic Resonance Imaging Research Center in the Institute of Psychology at Chinese Academy of Sciences, China.

{credit}Xi-Nian Zuo{/credit}

About a decade ago (2006), as a PhD student graduating from the School of Mathematics at Beijing Normal University, I stepped into the field of neuroimaging of the human brain by way of a short job interview offered by Dr. Yu-Feng Zang, my postdoc mentor in China. The most important thing that I learned and developed during my post doc training was how to question a study, an indication likely of my somewhat different background (mathematics versus brain sciences). Probability and statistics became my major tools in bridging new learning experiences with my existing knowledge, pushing me to further pursue research training offered by Dr. Michael Peter Milham at New York University. Ongoing work in his laboratory really interested me, particularly test-retest reliability of resting-state functional connectivity¹, the first study of test-retest reliability in the nascent field of functional connectivity. However, an obvious limitation existed to that study, and a series of test-retest reliability studies I carried out subsequently²; the small sample size. This directly motivated me to seek and build up a truly big data set for test-retest reliability in connectomics. Continue reading →