Cited by 208Advanced Causal Inference TechniquesBayesian Modeling and Causal InferenceStatistical Methods and Bayesian Inference

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Reversing Arrows in Mediation Models Does Not Distinguish Plausible Models

Thoemmes, Felix·2015·Basic and Applied Social Psychology

DOI 10.1080/01973533.2015.1049351

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Abstract

Reversing arrows in the classic tri-variate X-M-Y mediation models as a test to check whether one mediation model is superior to another is inadmissible. Presenting evidence that one tri-variate mediation model yields a significant indirect effect, whereas one with some reversed arrows does not, is not proof or even evidence that one model should be preferred. In fact, the significance of the indirect or any other effect can never be used to infer whether one model should be preferred over another, if the models are in the same so-called equivalence class. The practice of running several mediation models with reversed arrows to decide which model to prefer should be abandoned. The only way to choose among equivalent models is through assumptions that are either fulfilled by design features or invoked based on theory. Similar arguments about reversing arrows in mediation models have been made before, but this current work is the first to derive this result analytically for the complete (Markovian) equivalence class of the tri-variate mediation model.

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Authors: Thoemmes, F.
Affiliations: Cornell University (US)
Corresponding author: Thoemmes, Felix
ORCID: Thoemmes, Felix
Venue: Basic and Applied Social Psychology
Publisher: Informa UK Limited
Published: 2015-07-04
Volume: 37
Issue: 4
Pages: 226-234
ISSN: 0197-3533 (ISSN-L)
1532-4834
DOI: 10.1080/01973533.2015.1049351
OpenAlex: W2155450807
Cited by: 208
References: 11
Citations by year: 2026: 14 · 2025: 10 · 2024: 11 · 2023: 20 · 2022: 23 · 2021: 19 · 2020: 26 · 2019: 27 · 2018: 20 · 2017: 23 · 2016: 13 · 2015: 1
FWCI: 16.91
Type: journal article
Language: en
Open access: closed
Locations: Basic and Applied Social Psychology (publishedVersion)
Topics: Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
Keywords: mediation model, model comparison, equivalence classes, indirect effects, Markov equivalence, causal inference, model selection

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Reversing Arrows in Mediation Models Does Not Distinguish Plausible Models

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