手法を比較

	階層的媒介分析	条件付きプロセス分析（媒介変数の調整）	階層線形モデリング（HLM / マルチレベルモデリング）
分野	統計学	因果推論	統計学
系統	Hypothesis test	Regression model	Hypothesis test
提唱年	2003	2018	1986
提唱者	Kenny, Korchmaros & Bolger	Andrew F. Hayes (PROCESS framework); Preacher, Rucker & Hayes (moderated mediation)	Raudenbush & Bryk (popularized); Goldstein (parallel development)
種類	Multilevel structural model	Regression-based conditional process model	Parametric nested-data regression
原典	Kenny, D. A., Korchmaros, J. D., & Bolger, N. (2003). Lower level mediation in multilevel models. Psychological Methods, 8(2), 115–128.	Hayes, A. F. (2018). Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Approach (2nd ed.). The Guilford Press. ISBN: 978-1462534654	Raudenbush, S.W. & Bryk, A.S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods (2nd ed.). Sage. ISBN: 978-0761919049
別名	multilevel mediation, hierarchical mediation, cross-level mediation, 1-1-1 mediation	moderated mediation, moderated mediation analysis, PROCESS model, Hayes PROCESS conditional process model	HLM, MLM, multilevel modeling, multilevel analysis
関連	8	5	4
概要	Multilevel mediation analysis is a parametric structural method that estimates indirect (mediated) effects within hierarchically nested data, such as students within schools or employees within organisations. Formalised for lower-level mediation in multilevel models by Kenny, Korchmaros and Bolger (2003), it simultaneously handles individual-level (1-1-1) and group-level (2-2-1 or 2-1-1) mediation pathways in a single coherent framework.	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.