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- W4313703120 abstract "Mediation analysis empirically investigates the process underlying the effect of an experimental manipulation on a dependent variable of interest. In the simplest mediation setting, the experimental treatment can affect the dependent variable through the mediator (indirect effect) and/or directly (direct effect). However, what appears to be an indirect effect in standard mediation analysis may reflect a data-generating process without mediation, including the possibility of a reversed causal ordering of measured variables, regardless of the statistical properties of the estimate. To overcome this indeterminacy where possible, the authors develop the insight that a statistically reliable total effect, combined with strong evidence for conditional independence of the treatment and the outcome given the mediator, is unequivocal evidence for mediation as the underlying causal model into an operational procedure. This is particularly helpful when theory is insufficient to definitely causally order measured variables, or when the dependent variable is measured before what is believed to be the mediator. The procedure combines Bayes factors as principled measures of the degree of support for conditional independence, with latent variable modeling to account for measurement error and discretization in a fully Bayesian framework. The authors reanalyze a set of published mediation studies to illustrate how their approach facilitates stronger conclusions." @default.
- W4313703120 created "2023-01-08" @default.
- W4313703120 creator A5016501553 @default.
- W4313703120 creator A5058915949 @default.
- W4313703120 date "2023-05-24" @default.
- W4313703120 modified "2023-09-27" @default.
- W4313703120 title "Measuring Evidence for Mediation in the Presence of Measurement Error" @default.
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- W4313703120 doi "https://doi.org/10.1177/00222437231151873" @default.
- W4313703120 hasPublicationYear "2023" @default.
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