ArticleBehavior research methods2016
How to quantify the evidence for the absence of a correlation.
Article in Behavior research methods, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers.
What it found
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Who cites it
45 citing papers in PubMed, 135 citations in OpenAlex.
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- Exploring Associations between C-Reactive Protein and Self-Reported Interoception in Major Depressive Disorder: A Bayesian Analysis.Brain sciences · 2023Article
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- Building a decoder of perceptual decisions from microsaccades and pupil size.Frontiers in psychology · 2022Article
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- Brain stimulation and brain lesions converge on common causal circuits in neuropsychiatric disease.Nature human behaviour · 2021Article
- Bayesian Analysis Reporting Guidelines.Nature human behaviour · 2021Review
- Neutral and threatening distracter word stimuli are unnecessarily stored in working memory but do not differ in their degree of working memory storage.Biological psychology · 2021Article
- Neural correlates underpinning synchronized movement of double scull rowers.Scientific reports · 2021Observational
- Cyton2: A Model of Immune Cell Population Dynamics That Includes Familial Instructional Inheritance.Frontiers in bioinformatics · 2021Article
- Does Location Uncertainty Modulate Unconscious Processing Under Continuous Flash Suppression?Advances in cognitive psychology · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We present a suite of Bayes factor hypothesis tests that allow researchers to grade the decisiveness of the evidence that the data provide for the presence versus the absence of a correlation between two variables. For concreteness, we apply our methods to the recent work of Donnellan et al. (in press) who conducted nine replication studies with over 3,000 participants and failed to replicate the phenomenon that lonely people compensate for a lack of social warmth by taking warmer baths or showers. We show how the Bayes factor hypothesis test can quantify evidence in favor of the null hypothesis, and how the prior specification for the correlation coefficient can be used to define a broad range of tests that address complementary questions. Specifically, we show how the prior specification can be adjusted to create a two-sided test, a one-sided test, a sensitivity analysis, and a replication test.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.