The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
Reversing Arrows in Mediation Models Does Not Distinguish Plausible Models
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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“However, several recent publications, such as Thoemmes (2015) and Lemmer and Gollwitzer (2017), showed that it is not possible to determine merely on a statistical basis which of the different models we could define within the same set of three variables (a so-called equivalence class) is the most plausible.”
From Mindfulness, Interpretation Bias, and Levels of Anxiety and Depression: Two Mediation Studies (2019) · cites this paper as Thoemmes (2015)
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Cites
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What mediation analysis can (not) do
Fiedler, K., Schott, M., & Meiser, T. (2011) · Journal of Experimental Social Psychology · doi:10.1016/j.jesp.2011.05.007 · Cited by 609
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Research works printed but not linked: 2
- Chen B., Psychometrika
- Thoemmes, F., Liao, W. & Yamasaki, M. (2014). The Markov equivalence class of the mediation model . Paper presented at the annual Modern Modeling Methods Conference, Storrs, CT.
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Thinking Clearly About Correlations and Causation: Graphical Causal Models for Observational Data
Rohrer, J. M. (2018) · Advances in Methods and Practices in Psychological Science · doi:10.1177/2515245917745629 · Cited by 1,002
Unwarranted inferences from statistical mediation tests – An analysis of articles published in 2015
Fiedler, K., Harris, C., & Schott, M. (2017) · Journal of Experimental Social Psychology · doi:10.1016/j.jesp.2017.11.008 · Cited by 419
Evidence that psychopathology symptom networks have limited replicability.
Forbes, M. K., Wright, A. G. C., Markon, K. E., & Krueger, R. F. (2017) · Journal of Abnormal Psychology · doi:10.1037/abn0000276 · Cited by 385
The Effects of a Female Role Model on Academic Performance and Persistence of Women in STEM Courses
Herrmann, S. D., Adelman, R. M., Bodford, J. E., Graudejus, O., Okun, M. A., & Kwan, V. S. Y. (2016) · Basic and Applied Social Psychology · doi:10.1080/01973533.2016.1209757 · Cited by 321
Meaningful Mediation Analysis: Plausible Causal Inference and Informative Communication
Pieters, R. G. M. (2017) · Journal of Consumer Research · doi:10.1093/jcr/ucx081 · Cited by 289
Metadata
- 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
- 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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