PsyCuraDat: Development of user-oriented curation criteria for psychological reseearch data

Blask, Katarina; Latz, Marc; Müller, Marie-Luise; Kraffert, Stephanie; Arnold, Valentin

doi:10.23668/psycharchives.4477

Cited by 1 publication

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, data sets should be documented with informative meta-data, including comprehensive codebooks and detailed information on the data collection (e.g., recruitment, sampling procedure, or relevant technical details such as sampling rates in psychophysiological research). Some emerging standards in this regard have recently been published (e.g., Gollwitzer et al, 2021) or are currently under development (e.g., psych-DS: https://psych-ds.github.io/, or PsyCuraDat: Blask et al, 2020).…”

Section: Step 9: Planning For Informative Studiesmentioning

confidence: 99%

Ten steps toward a better personality science – how quality may be rewarded more in research evaluation

et al. 2022

View full text Add to dashboard Cite

This target article is part of a theme bundle including open peer commentaries (https://doi.org/10.5964/ps.9227) and a rejoinder by the authors (https://doi.org/10.5964/ps.7961). We point out ten steps that we think will go a long way in improving personality science. The first five steps focus on fostering consensus regarding (1) research goals, (2) terminology, (3) measurement practices, (4) data handling, and (5) the current state of theory and evidence. The other five steps focus on improving the credibility of empirical research, through (6) formal modelling, (7) mandatory pre-registration for confirmatory claims, (8) replication as a routine practice, (9) planning for informative studies (e.g., in terms of statistical power), and (10) making data, analysis scripts, and materials openly available. The current, quantity-based incentive structure in academia clearly stands in the way of implementing many of these practices, resulting in a research literature with sometimes questionable utility and/or integrity. As a solution, we propose a more quality-based reward scheme that explicitly weights published research by its Good Science merits. Scientists need to be increasingly rewarded for doing good work, not just lots of work.

show abstract

Section: Step 9: Planning For Informative Studiesmentioning

confidence: 99%