ORCID Profile
0000-0002-0024-5046
Current Organisation
Technische Universität Dresden
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Publisher: Wiley
Date: 16-03-2021
DOI: 10.1111/POPS.12746
Abstract: Belief in conspiracies is not restricted to the fringe dwellers of society. International research suggests that such beliefs are quite common and that conspiracy theories may serve three basic psychological motives (i.e., epistemic, existential, and relational) for in iduals. Yet, little is known about conspiracy theory awareness or belief in Australasia. We report the first large systematic investigation of system‐justifying motives using two nationally representative s les of Australians ( n = 1011) and New Zealanders ( n = 754). Our findings show that almost all are aware of local and international conspiracies, the majority endorse one or more, and that all three psychological motives consistently relate to conspiracy belief, but not to awareness. In a series of hierarchical multiple regressions, we find that relational (i.e., increased anomie and disillusionment with the government) and existential motives (i.e., less trust in others and increased religiosity) are uniquely and relatively more important than epistemic needs (i.e., decreased analytic thinking) as predictors of increased local and international conspiracy belief. Findings are discussed in terms of the importance of understanding conspiracy theories as an ideological belief system that may function to serve underlying psychological motives.
Publisher: Zenodo
Date: 2018
Publisher: Center for Open Science
Date: 24-03-2021
Abstract: This study explores how researchers’ analytical choices affect the reliability of scientific findings. Most discussions of reliability problems in science focus on systematic biases. We broaden the lens to include conscious and unconscious decisions that researchers make during data analysis and that may lead to erging results. We coordinated 161 researchers in 73 research teams and observed their research decisions as they used the same data to independently test the same prominent social science hypothesis: that greater immigration reduces support for social policies among the public. In this typical case of research based on secondary data, we find that research teams reported widely erging numerical findings and substantive conclusions despite identical start conditions. Researchers’ expertise, prior beliefs, and expectations barely predicted the wide variation in research outcomes. More than 90% of the total variance in numerical results remained unexplained even after accounting for research decisions identified via qualitative coding of each team’s workflow. This reveals a universe of uncertainty that is hidden when considering a single study in isolation. The idiosyncratic nature of how researchers’ results and conclusions varied is a new explanation for why many scientific hypotheses remain contested. It calls for greater humility and clarity in reporting scientific findings.
Publisher: Center for Open Science
Date: 26-01-2019
Abstract: In an era of mass migration, social scientists, populist parties and social movements raise concerns over the future of immigration-destination societies. What impacts does this have on policy and social solidarity? Comparative cross-national research, relying mostly on secondary data, has findings in different directions. There is a threat of selective model reporting and lack of replicability. The heterogeneity of countries obscures attempts to clearly define data-generating models. P-hacking and HARKing lurk among standard research practices in this area.This project employs crowdsourcing to address these issues. It draws on replication, deliberation, meta-analysis and harnessing the power of many minds at once. The Crowdsourced Replication Initiative carries two main goals, (a) to better investigate the linkage between immigration and social policy preferences across countries, and (b) to develop crowdsourcing as a social science method. The Executive Report provides short reviews of the area of social policy preferences and immigration, and the methods and impetus behind crowdsourcing plus a description of the entire project. Three main areas of findings will appear in three papers, that are registered as PAPs or in process.
Publisher: Proceedings of the National Academy of Sciences
Date: 28-10-2022
Abstract: This study explores how researchers’ analytical choices affect the reliability of scientific findings. Most discussions of reliability problems in science focus on systematic biases. We broaden the lens to emphasize the idiosyncrasy of conscious and unconscious decisions that researchers make during data analysis. We coordinated 161 researchers in 73 research teams and observed their research decisions as they used the same data to independently test the same prominent social science hypothesis: that greater immigration reduces support for social policies among the public. In this typical case of social science research, research teams reported both widely erging numerical findings and substantive conclusions despite identical start conditions. Researchers’ expertise, prior beliefs, and expectations barely predict the wide variation in research outcomes. More than 95% of the total variance in numerical results remains unexplained even after qualitative coding of all identifiable decisions in each team’s workflow. This reveals a universe of uncertainty that remains hidden when considering a single study in isolation. The idiosyncratic nature of how researchers’ results and conclusions varied is a previously underappreciated explanation for why many scientific hypotheses remain contested. These results call for greater epistemic humility and clarity in reporting scientific findings.
Publisher: Public Library of Science (PLoS)
Date: 10-11-2020
DOI: 10.1371/JOURNAL.PCBI.1008316
Abstract: Computational science has been greatly improved by the use of containers for packaging software and data dependencies. In a scholarly context, the main drivers for using these containers are transparency and support of reproducibility in turn, a workflow’s reproducibility can be greatly affected by the choices that are made with respect to building containers. In many cases, the build process for the container’s image is created from instructions provided in a Dockerfile format. In support of this approach, we present a set of rules to help researchers write understandable Dockerfiles for typical data science workflows. By following the rules in this article, researchers can create containers suitable for sharing with fellow scientists, for including in scholarly communication such as education or scientific papers, and for effective and sustainable personal workflows.
No related grants have been discovered for Daniel Nüst.