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Ανάλυση Αιτιακής Διαμεσολάβησης (Φυσικά Άμεσες και Έμμεσες Επιπτώσεις)×Ανάλυση Δορυφορικής Διαδικασίας (Συντονισμένη Διαμεσολάβηση)×Ιεραρχική Γραμμική Μοντελοποίηση (HLM / Πολυεπίπεδη Μοντελοποίηση)×
ΠεδίοΑιτιακή ΣυμπερασματολογίαΑιτιακή ΣυμπερασματολογίαΣτατιστική
ΟικογένειαRegression modelRegression modelHypothesis test
Έτος προέλευσης201020181986
ΔημιουργόςPearl (2001); general framework by Imai, Keele & Tingley (2010)Andrew F. Hayes (PROCESS framework); Preacher, Rucker & Hayes (moderated mediation)Raudenbush & Bryk (popularized); Goldstein (parallel development)
ΤύποςCounterfactual causal decompositionRegression-based conditional process modelParametric nested-data regression
Θεμελιώδης πηγήPearl, J. (2001). Direct and Indirect Effects. In Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence (UAI), 411-420. link ↗Hayes, A. F. (2018). Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Approach (2nd ed.). The Guilford Press. ISBN: 978-1462534654Raudenbush, S.W. & Bryk, A.S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods (2nd ed.). Sage. ISBN: 978-0761919049
Εναλλακτικές ονομασίεςnatural direct effect, natural indirect effect, NDE / NIE decomposition, counterfactual mediationmoderated mediation, moderated mediation analysis, PROCESS model, Hayes PROCESS conditional process modelHLM, MLM, multilevel modeling, multilevel analysis
Συναφείς554
ΣύνοψηCausal mediation analysis is a counterfactual framework that splits a treatment's total effect into a Natural Direct Effect (NDE) and a Natural Indirect Effect (NIE) that runs through a mediator. The modern general approach was formalised by Pearl (2001) and Imai, Keele and Tingley (2010), giving the decomposition a precise causal interpretation.Conditional process analysis is Andrew F. Hayes's regression-based PROCESS framework (2018) that combines mediation and moderation in a single model, testing how an indirect effect changes across levels of a moderator. It quantifies conditional indirect and conditional direct effects and tests them with bootstrap confidence intervals.Hierarchical Linear Modeling (HLM), also known as Multilevel Modeling (MLM), is a parametric statistical method for analyzing nested or clustered data — for example students within classrooms, patients within hospitals, or employees within organizations. Formalized by Raudenbush and Bryk in their 2002 seminal text (building on work from the mid-1980s), HLM simultaneously estimates individual-level and group-level effects while correctly partitioning variance across levels.
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ScholarGateΣύγκριση μεθόδων: Causal Mediation Analysis · Conditional Process Analysis · Hierarchical Linear Modeling. Ανακτήθηκε στις 2026-06-18 από https://scholargate.app/el/compare