Comparar métodos

	Ecological Inference	Modelado multinivel
Campo	Political Science	Estadística para la investigación
Familia	Regression model	Process / pipeline
Año de origen	1997	1992
Autor original	Leo Goodman (ecological regression); Gary King (statistical EI solution)	Anthony Bryk and Stephen Raudenbush
Tipo	Aggregate-data model inferring individual-level rates from grouped totals	Method
Fuente seminal	King, G. (1997). A Solution to the Ecological Inference Problem: Reconstructing Individual Behavior from Aggregate Data. Princeton: Princeton University Press. ISBN: 9780691012414	Bryk, A. S., & Raudenbush, S. W. (1992). Hierarchical Linear Models: Applications and Data Analysis Methods. SAGE Publications.
Alias	EI, Ecological regression, King's ecological inference, Aggregate-to-individual inference	HLM, mixed-effects models, random effects models, MLM
Relacionados	5	3
Resumen	Ecological inference is the problem of learning about individual behavior — such as how Black and white voters cast their ballots — when only aggregate data are available, like precinct-level turnout and racial composition. Because individual-level data are missing, the within-group rates are not directly observed; ecological inference recovers them by combining the deterministic accounting constraints that each precinct must satisfy with a statistical model of how the unobserved rates vary across precincts. Gary King's 1997 solution unified the deterministic method of bounds with Leo Goodman's classic ecological regression, sharply reducing the long-standing risk of the ecological fallacy.	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.