Post by @vtraag@social.cwts.nl

@vtraag@social.cwts.nl

2024-02-12 09:16 UTC

Without causal thinking, we are bound to interpret observations incorrectly. One case study in particular illustrates this well. Here, we know (because we simulate) that open data improves reproducibility in research. However, when looking at research published in journals, we observe that articles with open data are less likely to be reproducible. This in contrast to the known positive effect. What happened?

Replies (2)

@vtraag@social.cwts.nl 2024-02-12 09:16

The explanation is that journals are more likely to publish research that has open data, but also that is more rigorous. If published research has no open data, it therefore tends to be more rigorous, otherwise it would not be published at all. Research that is more rigorous tends to be more reproducible, and in our case, this effect is stronger than open data. So, published research without open data is more rigorous which in turn is more reproducible.

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@Aschniedermann@fediscience.org 2024-02-19 21:57

@vtraag that is very interesting and adds important nuance to the roles of journal/editorial practices. However, I am curious what the causal analysis really adds here. Can’t you just say that the two independent variables, rigor and open data are correlated and thus less relevant (as in Multocolinearity)?

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