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تحلیل تعدیل‌شده میانجی‌گری قوی×مدل‌سازی معادلات ساختاری (SEM)×
حوزهآمارآمار پژوهش
خانوادهLatent structureProcess / pipeline
سال پیدایش2007–20131921
پدیدآورHayes, A. F.; building on Preacher, Rucker & Hayes (2007) for moderated mediation and robust bootstrap inferenceSewall Wright
نوعConditional indirect effect model with robust inferenceMethod
منبع بنیادینHayes, A. F. (2022). Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Approach (3rd ed.). Guilford Press. ISBN: 978-1462549030Jöreskog, K. G., & Sörbom, D. (1973). LISREL: A general computer program for estimating a linear structural equation system. Research Bulletin 73-5. University of Stockholm. link ↗
نام‌های دیگرrobust conditional process analysis, robust mediated moderation, robust moderated indirect effects, robust conditional indirect effectsSEM, path analysis, latent variable modeling, causal modeling
مرتبط53
خلاصهRobust 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.Structural equation modeling (SEM) is a comprehensive statistical framework combining path analysis (Sewall Wright, 1921) and confirmatory factor analysis to test complex causal models linking observed and latent variables. Formalized by Jöreskog (1973) with LISREL software, SEM enables simultaneous estimation of measurement relationships (how variables measure latent constructs) and structural relationships (how constructs influence outcomes), making it powerful for theory testing in psychology, epidemiology, organizational research, and health sciences where complex mediation, moderation, and latent processes require integrated analysis.
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ScholarGateمقایسهٔ روش‌ها: Robust Moderated Mediation · Structural Equation Modeling. بازیابی‌شده در 2026-06-17 از https://scholargate.app/fa/compare