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النمذجة الخطية الهرمية (HLM / نمذجة المستويات المتعددة)×تحليل التباين للمقاييس المتكررة×
المجالالإحصاءالإحصاء
العائلةHypothesis testHypothesis test
سنة النشأة19861992
صاحب الطريقةRaudenbush & Bryk (popularized); Goldstein (parallel development)Girden (textbook treatment); Field (2013)
النوعParametric nested-data regressionParametric within-subjects mean comparison
المصدر التأسيسيRaudenbush, S.W. & Bryk, A.S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods (2nd ed.). Sage. ISBN: 978-0761919049Field, A. (2013). Discovering Statistics Using IBM SPSS Statistics (4th ed., Ch. 14). SAGE. ISBN: 978-1446249185
الأسماء البديلةHLM, MLM, multilevel modeling, multilevel analysiswithin-subjects ANOVA, repeated measures analysis of variance, rm-ANOVA, Tekrarlı Ölçüm ANOVA
ذات صلة44
الملخصHierarchical 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.Repeated-measures ANOVA is a parametric hypothesis test that compares three or more measurements taken from the same individuals — typically across time points or conditions — to decide whether their means differ. It extends one-way ANOVA to within-subjects designs, as treated in standard references such as Girden (1992) and Field (2013).
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ScholarGateقارن الطرق: Hierarchical Linear Modeling · Repeated-measures ANOVA. استُرجع بتاريخ 2026-06-18 من https://scholargate.app/ar/compare