방법 비교

	인과적 매개 분석 (자연 직접 효과 및 간접 효과)	계층적 선형 모형 (HLM / 다층 모형)	매개 분석
분야	인과추론	통계학	통계학
계열	Regression model	Hypothesis test	Hypothesis test
기원 연도	2010	1986	1986
창시자	Pearl (2001); general framework by Imai, Keele & Tingley (2010)	Raudenbush & Bryk (popularized); Goldstein (parallel development)	Baron & Kenny
유형	Counterfactual causal decomposition	Parametric nested-data regression	Indirect effects / path test
원전	Pearl, J. (2001). Direct and Indirect Effects. In Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence (UAI), 411-420.	Raudenbush, S.W. & Bryk, A.S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods (2nd ed.). Sage. ISBN: 978-0761919049	Baron, R. M. & Kenny, D. A. (1986). The moderator-mediator variable distinction in social psychological research. Journal of Personality and Social Psychology, 51(6), 1173–1182.
별칭	natural direct effect, natural indirect effect, NDE / NIE decomposition, counterfactual mediation	HLM, MLM, multilevel modeling, multilevel analysis	indirect effects analysis, path-based mediation, PROCESS macro mediation, Aracılık Analizi (Mediation / PROCESS)
관련	5	4	5
요약	Causal mediation analysis is a counterfactual framework that splits a treatment's total effect into a Natural Direct Effect (NDE) and a Natural Indirect Effect (NIE) that runs through a mediator. The modern general approach was formalised by Pearl (2001) and Imai, Keele and Tingley (2010), giving the decomposition a precise causal interpretation.	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.	Mediation analysis is a statistical procedure that tests whether the effect of an independent variable X on an outcome Y operates wholly or partly through a third variable M, called the mediator. Formalised by Baron and Kenny in 1986, it decomposes the total effect of X on Y into a direct path (c′) and an indirect path (a × b), quantifying how much of the relationship is carried by the mediating mechanism.