Сравнение методов

	Робастный анализ модерируемой медиации	Робастный анализ путей
Область	Статистика	Статистика
Семейство	Latent structure	Latent structure
Год появления	2007–2013	1998
Автор метода	Hayes, A. F.; building on Preacher, Rucker & Hayes (2007) for moderated mediation and robust bootstrap inference	Yuan & Bentler (robust SEM/path framework); Huber (M-estimation foundation)
Тип	Conditional indirect effect model with robust inference	Causal path modeling with robust estimation
Основополагающий источник	Hayes, A. F. (2022). Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Approach (3rd ed.). Guilford Press. ISBN: 978-1462549030	Yuan, K.-H. & Bentler, P. M. (1998). Robust mean and covariance structure analysis. British Journal of Mathematical and Statistical Psychology, 51(1), 63–88.
Другие названия	robust conditional process analysis, robust mediated moderation, robust moderated indirect effects, robust conditional indirect effects	robust PA, path analysis with robust standard errors, robust causal path modeling, robust structural path modeling
Связанные	5	6
Сводка	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 path analysis applies robust estimation — such as sandwich standard errors or M-estimation — to path models that specify directed causal relationships among observed variables. It preserves valid inference about path coefficients and indirect effects when data violate normality, contain outliers, or exhibit heteroscedasticity that would distort conventional standard errors.