手法を比較

	分割区画実験計画法（Split-Plot Experimental Design）	階層線形モデリング（HLM / マルチレベルモデリング）
分野	実験計画法	統計学
系統	Hypothesis test	Hypothesis test
提唱年	1935	1986
提唱者	Frank Yates	Raudenbush & Bryk (popularized); Goldstein (parallel development)
種類	Parametric mixed-model ANOVA	Parametric nested-data regression
原典	Yates, F. (1935). Complex Experiments. Supplement to the Journal of the Royal Statistical Society, 2(2), 181–247.	Raudenbush, S.W. & Bryk, A.S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods (2nd ed.). Sage. ISBN: 978-0761919049
別名	split-plot ANOVA, whole-plot sub-plot design, Bölünmüş Parsel Deseni (Split-Plot)	HLM, MLM, multilevel modeling, multilevel analysis
関連	6	4
概要	The split-plot design is a parametric experimental design that applies one factor to large whole plots and a second factor to subdivisions (sub-plots) within each whole plot. It was introduced by Frank Yates in 1935 to handle agricultural experiments where one factor — such as irrigation or tillage method — is difficult or impractical to change frequently, while a second factor can be varied more easily within the same plot.	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.