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مدل‌سازی خطی سلسله‌مراتبی (HLM / مدل‌سازی چندسطحی)×تحلیل واریانس یک‌طرفه×
حوزهآمارآمار
خانوادهHypothesis testHypothesis test
سال پیدایش19861925
پدیدآورRaudenbush & Bryk (popularized); Goldstein (parallel development)Ronald A. Fisher
نوعParametric nested-data regressionParametric mean comparison
منبع بنیادینRaudenbush, S.W. & Bryk, A.S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods (2nd ed.). Sage. ISBN: 978-0761919049Fisher, R. A. (1925). Statistical Methods for Research Workers. Edinburgh: Oliver and Boyd. link ↗
نام‌های دیگرHLM, MLM, multilevel modeling, multilevel analysisone-factor ANOVA, single-factor ANOVA, analysis of variance, tek yönlü ANOVA
مرتبط44
خلاصهHierarchical 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.One-way ANOVA is a parametric hypothesis test that compares the means of three or more independent groups on a single continuous outcome to decide whether at least one group mean differs. It rests on the variance-partitioning framework introduced by Ronald A. Fisher in 1925.
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ScholarGateمقایسهٔ روش‌ها: Hierarchical Linear Modeling · One-way ANOVA. بازیابی‌شده در 2026-06-18 از https://scholargate.app/fa/compare