Salīdzināt metodes

	Robustas moderētās mediācijas analīze	Robustas mediācijas analīze
Nozare	Statistika	Statistika
Saime	Latent structure	Latent structure
Izcelsmes gads	2007–2013	2008–2014
Autors	Hayes, A. F.; building on Preacher, Rucker & Hayes (2007) for moderated mediation and robust bootstrap inference	Yuan & MacKinnon (median-regression formulation, 2014); robust bootstrap variants popularised by Hayes (2013) and Preacher & Hayes (2008)
Tips	Conditional indirect effect model with robust inference	Causal inference / indirect effects
Pirmavots	Hayes, A. F. (2022). Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Approach (3rd ed.). Guilford Press. ISBN: 978-1462549030	Yuan, Y., & MacKinnon, D. P. (2014). Robust mediation analysis based on median regression. Psychological Methods, 19(1), 1–20.
Citi nosaukumi	robust conditional process analysis, robust mediated moderation, robust moderated indirect effects, robust conditional indirect effects	robust indirect effects, outlier-resistant mediation, robust causal mediation
Saistītās	5	5
Kopsavilkums	Robust moderated mediation tests whether the indirect effect of X on Y through a mediator M varies as a function of a moderator W, while using robust estimation (percentile or bias-corrected bootstrap, heteroscedasticity-consistent standard errors, or M-estimation) to protect inference against non-normality, outliers, and heteroscedasticity in the data.	Robust mediation analysis estimates the indirect effect of an independent variable on an outcome through one or more mediators using estimators that resist the influence of outliers and non-normal error distributions. By combining robust regression (such as median or M-estimation) with percentile or bias-corrected bootstrap confidence intervals, it yields trustworthy conclusions when standard ordinary-least-squares mediation would be distorted by extreme observations.