Comparar métodos

	Investigación jerárquica cuasi-experimental	Investigación Longitudinal Causal-Comparativa
Campo	Diseño de investigación	Diseño de investigación
Familia	Process / pipeline	Process / pipeline
Año de origen	1960s (causal-comparative); 1980s–2002 (hierarchical/multilevel extension)	1970s–1980s (as an established combined design in educational and social research)
Autor original	Kerlinger (causal-comparative logic); Raudenbush & Bryk (hierarchical extension)	Synthesized from causal-comparative tradition (Kerlinger, 1973) and longitudinal design frameworks (Goldstein, 1979)
Tipo	Non-experimental quantitative research design	Non-experimental quantitative research design
Fuente seminal	Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods (2nd ed.). Sage. ISBN: 978-0761919049	Fraenkel, J. R., Wallen, N. E., & Hyun, H. H. (2009). How to Design and Evaluate Research in Education (7th ed.). McGraw-Hill. ISBN: 978-0073525532
Alias	multilevel causal-comparative design, nested causal-comparative research, HLM causal-comparative study, hierarchical ex post facto comparison	longitudinal ex post facto design, longitudinal causal-comparative design, repeated-measures causal-comparative research, prospective causal-comparative study
Relacionados	4	4
Resumen	Hierarchical causal-comparative research is a non-experimental quantitative design that compares pre-existing groups on an outcome variable while explicitly modeling the nested structure of the data. Participants are clustered within higher-level units — students within classrooms, employees within organizations — and the design uses multilevel analytical techniques to distinguish group differences at each level. The cause-and-effect inference is strengthened by accounting for variance attributable to the hierarchy rather than misattributing it to individual-level group membership.	Longitudinal causal-comparative research is a non-experimental quantitative design that compares pre-existing groups on one or more dependent variables across multiple measurement points over time. Unlike true experiments, the researcher does not manipulate the independent variable; instead, naturally occurring group differences (e.g., gender, socioeconomic status, diagnostic category) are examined to explore their relationship to outcomes as they evolve longitudinally.