Estimating the prevalence of transparency and reproducibility-related research practices in psychology (2014-2017) is a research paper (2020). On theSindex it has a DataRank of 0.416. It has been cited 15 times.
Psychologists are navigating an unprecedented period of introspection about the credibility and utility of their discipline. Reform initiatives have emphasized the benefits of several transparency and reproducibility-related research practices; however, their adoption across the psychology literature is unknown. To estimate their prevalence, we manually examined a random sample of 250 psychology articles published between 2014-2017. Over half of the articles were publicly available (154/237, 65% [95% confidence interval, 59%-71%]); however, sharing of research materials (26/183, 14% [10%-19%]), study protocols (0/188, 0% [0%-1%]), raw data (4/188, 2% [1%-4%]), and analysis scripts (1/188, 1% [0%-1%]) was rare. Pre-registration was also uncommon (5/188, 3% [1%-5%]). Many articles included a funding disclosure statement (142/228, 62% [56%-69%]), but conflict of interest statements were less common (88/228, 39% [32%-45%]). Replication studies were rare (10/188, 5% [3%-8%]) and few studies were included in systematic reviews (21/183, 11% [8%-16%]) or meta-analyses (12/183, 7% [4%-10%]). Overall, the results suggest that transparency and reproducibility-related research practices were far from routine. These findings establish a baseline which can be used to assess future progress towards increasing the credibility and utility of psychology research.
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
Base Score Contribution
0.416
From this paper's citation signal
Citation Network Contribution
0
Citation network not refreshed for this result
This paper's DataRank is currently driven only by its base citation score. Citation network data was not refreshed for this result.
Learn more about DataRank methodology →DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 100% comes from its base citations and 0% from the citation network.
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.