Σύγκριση μεθόδων

	Causal Mediation Analysis in Politics	Πολυεπίπεδη Μοντελοποίηση
Πεδίο	Political Science	Ερευνητική Στατιστική
Οικογένεια	Regression model	Process / pipeline
Έτος προέλευσης	2010	1992
Δημιουργός	Imai, Keele, Tingley & Yamamoto (potential-outcomes causal mediation)	Anthony Bryk and Stephen Raudenbush
Τύπος	Causal-inference decomposition of a treatment effect into direct and indirect (mediated) components	Method
Θεμελιώδης πηγή	Imai, K., Keele, L., & Tingley, D. (2010). A General Approach to Causal Mediation Analysis. Psychological Methods, 15(4), 309–334.	Bryk, A. S., & Raudenbush, S. W. (1992). Hierarchical Linear Models: Applications and Data Analysis Methods. SAGE Publications.
Εναλλακτικές ονομασίες	Causal mediation, Mechanism analysis, Direct and indirect effects, Potential-outcomes mediation	HLM, mixed-effects models, random effects models, MLM
Συναφείς	5	3
Σύνοψη	Causal mediation analysis decomposes the effect of a treatment — often a randomized experimental manipulation, such as a campaign message or an information treatment — into the part transmitted through a specified intermediate variable, the mediator, and the part operating through all other pathways. Formalized in the potential-outcomes framework by Imai, Keele, Tingley, and Yamamoto, it defines the average causal mediation effect (ACME) and the average direct effect, makes explicit the sequential-ignorability assumption required to identify them, and supplies a sensitivity analysis for when that assumption fails. It lets political scientists move beyond 'does the treatment work?' to 'why does it work?'	Multilevel modeling (also called hierarchical linear modeling, mixed-effects modeling) is a statistical framework for analyzing data organized in nested or clustered structures—students within schools, patients within hospitals, repeated measures within individuals. Developed by Bryk and Raudenbush (1992), it accounts for dependency among observations and partitions variance into levels (within-cluster and between-cluster), enabling valid inference and revealing context effects. Essential in education, medicine, organizational research, and any field where data have natural hierarchies.