| 1. |
- Furukawa, Toshi A., et al.
(author)
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Dismantling, optimising, and personalising internet cognitive behavioural therapy for depression : a systematic review and component network meta-analysis using individual data
- 2021
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In: Lancet psychiatry. - London, United Kingdom : Elsevier. - 2215-0374 .- 2215-0366. ; 8:6, s. 500-511
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Research review (peer-reviewed)abstract
- Findings We identified 76 RCTs, including 48 trials contributing individual participant data (11 704 participants) and 28 trials with aggregate data (6474 participants). The participants' weighted mean age was 42.0 years and 12 406 (71%) of 17 521 reported were women. There was suggestive evidence that behavioural activation might be beneficial (iMD -1.83 [95% credible interval (CrI) -2.90 to -0.80]) and that relaxation might be harmful (1.20 [95% CrI 0.17 to 2.27]). Baseline severity emerged as the strongest prognostic factor for endpoint depression. Combining human and automated encouragement reduced dropouts from treatment (incremental odds ratio, 0.32 [95% CrI 0.13 to 0.93]). The risk of bias was low for the randomisation process, missing outcome data, or selection of reported results in most of the included studies, uncertain for deviation from intended interventions, and high for measurement of outcomes. There was moderate to high heterogeneity among the studies and their components. 511
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| 2. |
- Breznau, Nate, et al.
(author)
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Observing many researchers using the same data and hypothesis reveals a hidden universe of uncertainty
- 2022
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In: Proceedings of the National Academy of Sciences of the United States of America. - : National Academy of Sciences. - 0027-8424 .- 1091-6490. ; 119:44
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Journal article (peer-reviewed)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 diverging 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 teams 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.
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| 3. |
- Lakens, Daniel, et al.
(author)
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Justify your alpha
- 2018
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In: Nature Human Behaviour. - : Nature Publishing Group. - 2397-3374. ; 2:3, s. 168-171
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Journal article (peer-reviewed)abstract
- In response to recommendations to redefine statistical significance to P ≤ 0.005, we propose that researchers should transparently report and justify all choices they make when designing a study, including the alpha level.
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| 4. |
- Karimo, Aasa, et al.
(author)
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Shared Positions on Divisive Beliefs Explain Interorganizational Collaboration: Evidence from Climate Change Policy Subsystems in 11 Countries
- 2023
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In: Journal of public administration research and theory. - : OXFORD UNIV PRESS. - 1053-1858 .- 1477-9803. ; 33:3, s. 421-433
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Journal article (peer-reviewed)abstract
- Collaboration between public administration organizations and various stakeholders is often prescribed as a potential solution to the current complex problems of governance, such as climate change. According to the Advocacy Coalition Framework, shared beliefs are one of the most important drivers of collaboration. However, studies investigating the role of beliefs in collaboration show mixed results. Some argue that similarity of general normative and empirical policy beliefs elicits collaboration, while others focus on beliefs concerning policy instruments. Proposing a new divisive beliefs hypothesis, we suggest that agreeing on those beliefs over which there is substantial disagreement in the policy subsystem is what matters for collaboration. Testing our hypotheses using policy network analysis and data on climate policy subsystems in 11 countries (Australia, Brazil, the Czech Republic, Germany, Finland, Ireland, Japan, Korea, Portugal, Sweden, and Taiwan), we find belief similarity to be a stronger predictor of collaboration when the focus is divisive beliefs rather than normative and empirical policy beliefs or beliefs concerning policy instruments. This knowledge can be useful for managing collaborative governance networks because it helps to identify potential competing coalitions and to broker compromises between them.
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