Robust research needs many lines of evidence
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The convergence of experimental, correlational, and neural evidence is a successful case of triangulation.
“Such convergent findings at different levels of empirical evidence (experimental, correlational, and neural) is a successful example of triangulation that does not leave much room for further debate (Campbell & Fiske, 1959;Munafò & Smith, 2018).”
From Working memory training in typically developing children: A multilevel meta-analysis (2020) · cites this paper as Munafò & Smith, 2018
In “Repeating experiments is not enough” (page unavailable), the paper defines triangulation as using multiple approaches to one question and says agreement across methodologies makes artefacts less likely. It does not assess the citing sentence’s particular experimental, correlational, and neural findings, or say that their convergence leaves little room for further debate. (LLM, conf=0.84)
Experimental — accuracy not yet measuredThe association between child maltreatment and mental health triangulated across multiple types of quasi-experimental methods with different assumptions and potential sources of bias, strengthening causal inference.
“Notably, this association triangulated across multiple types of quasi-experimental methods with different assumptions and potential sources of bias, strengthening causal inference (57, 58).”
From Childhood Maltreatment and Mental Health Problems: A Systematic Review and Meta-Analysis of Quasi-Experimental Studies (2023) · cites this paper as [58]
The SPECIOUS ROBUSTNESS section explains that triangulation uses approaches with different assumptions, and the REPLICATION FIXATION checklist calls for comparing approaches with different sources of bias. These passages support the causal-inference rationale, but the paper does not examine whether the child-maltreatment–mental-health association was triangulated; its worked example concerns maternal smoking and birth weight. Page numbers are not available in our copy. (LLM, conf=0.88)
Experimental — accuracy not yet measuredIn general, causal inference can be presented in a triangulation framework, evaluating the overall body of evidence from several different approaches.
“In general, causal inference can be presented in a triangulation framework, evaluating the overall body of evidence from several different approaches.103 104”
From Strengthening the Reporting of Observational Studies in Epidemiology using Mendelian Randomisation (STROBE-MR): explanation and elaboration. (2021) · cites this paper as [103]
Steiger filtering removes variants from the analysis if their association with the outcome is stronger than that with the exposure.
“A further approach for identifying variants to remove from the analysis is Steiger filtering, which removes variants from the analysis if their association with the outcome is stronger than that with the exposure 102 .”
From Guidelines for performing Mendelian randomization investigations (2020) · cites this paper as [102]
The MR-Steiger method has been developed to detect such variants (for a continuous exposure and outcome) and remove them from the analysis.
“The MR-Steiger method has been developed to detect such variants (for a continuous exposure and outcome) and remove them from the analysis 102 .”
From Guidelines for performing Mendelian randomization investigations (2020) · cites this paper as [102]
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Flagged first, then by citations
Research works printed but not linked: 2
- [2]2. Munafò, M. R. et al. Nature Hum. Behav. 1, 0021 (2017).
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“That is, ‘ The practice of strengthening causal inferences by integrating results from several different approaches, where each approach has different ( and assumed to be largely unrelated ) key sources of potential bias ’ (Munafo & Davey Smith, 2018).”
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- Authors
- Munafò, M. R., & Davey Smith, G.
- Venue
- Nature
- Published
- 2018-01-23
- Volume
- 553
- Issue
- 7689
- Pages
- 399-401
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- OpenAlex
- W2786371198
- Cited by
- 666
- References
- 9
- Citations by year
- 2026: 43 · 2025: 59 · 2024: 90 · 2023: 68 · 2022: 69 · 2021: 98 · 2020: 93 · 2019: 92 · 2018: 54
- FWCI
- 64.25
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Explore Bristol Research (submittedVersion)
- Funders
- Medical Research Council (MC_UU_00011/7)
- Topics
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Social Sciences › Decision Sciences › Information Systems and Management › Interdisciplinary Research and Collaboration
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Keywords
- research robustness, triangulation, replication, evidence synthesis, research credibility, meta-analysis
- MeSH terms
- Interdisciplinary Research, Animals, Humans, Research, Research Design, Reproducibility of Results, Confounding Factors, Epidemiologic, Artifacts, Uncertainty
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