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تحليل العمليات الشرطية (الوساطة المعتدلة)×النمذجة الخطية الهرمية (HLM / نمذجة المستويات المتعددة)×
المجالالاستدلال السببيالإحصاء
العائلةRegression modelHypothesis test
سنة النشأة20181986
صاحب الطريقةAndrew F. Hayes (PROCESS framework); Preacher, Rucker & Hayes (moderated mediation)Raudenbush & Bryk (popularized); Goldstein (parallel development)
النوعRegression-based conditional process modelParametric nested-data regression
المصدر التأسيسيHayes, 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
الأسماء البديلةmoderated mediation, moderated mediation analysis, PROCESS model, Hayes PROCESS conditional process modelHLM, MLM, multilevel modeling, multilevel analysis
ذات صلة54
الملخصConditional 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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ScholarGateقارن الطرق: Conditional Process Analysis · Hierarchical Linear Modeling. استُرجع بتاريخ 2026-06-18 من https://scholargate.app/ar/compare