Comparar métodos

	Investigación Causal-Comparativa Multivariante	Diseño Ex Post Facto
Campo	Diseño de investigación	Diseño de investigación
Familia	Process / pipeline	Process / pipeline
Año de origen	Mid-20th century onward; multivariate extension systematized 1970s–1990s	1960s (systematic codification); concept used in social science from early 20th century
Autor original	Extension of causal-comparative tradition (cf. Chapin, 1947; Gay, Mills & Airasian)	Formalized by Fred N. Kerlinger; foundational treatment by Donald T. Campbell and Julian C. Stanley
Tipo	Quantitative non-experimental comparative design	Non-experimental quantitative research design
Fuente seminal	Fraenkel, J. R., Wallen, N. E., & Hyun, H. H. (2019). How to Design and Evaluate Research in Education (10th ed.). McGraw-Hill. ISBN: 978-1260085594	Kerlinger, F. N. (1964). Foundations of Behavioral Research. Holt, Rinehart and Winston.
Alias	multivariate causal-comparative design, MANOVA causal-comparative study, multi-outcome ex post facto research, multivariate ex post facto design	after-the-fact research, retrospective non-experimental design, causal-comparative design, EPF design
Relacionados	6	3
Resumen	Multivariate causal-comparative research is a quantitative, non-experimental design that investigates whether pre-existing group differences (defined by a naturally occurring categorical variable) are associated with differences across multiple outcome variables considered simultaneously. By extending the classic causal-comparative framework to several dependent variables at once, it reduces Type I error inflation and captures the correlated structure of outcomes that univariate comparisons would miss.	Ex post facto design is a non-experimental quantitative research approach in which the researcher investigates a phenomenon after it has already occurred, examining pre-existing differences between groups to explore potential causal or associative relationships. Because the independent variable cannot be manipulated — it happened in the past — the design relies on careful group selection, retrospective data collection, and statistical controls to approximate causal inference without experimental intervention.