Confronta i metodi

	Ricerca esplicativa robusta	Ricerca Causal-Comparativa
Campo	Disegno della ricerca	Disegno della ricerca
Famiglia	Process / pipeline	Process / pipeline
Anno di origine	1960s–1980s (robust statistics foundations); applied to explanatory research from 1990s onward	1964
Ideatore	Peter J. Huber (robust statistics); applied to explanatory designs via Rand Wilcox and others	Fred N. Kerlinger
Tipo	Quantitative research design	Non-experimental quantitative research design
Fonte seminale	Huber, P. J. (1981). Robust Statistics. Wiley. ISBN: 978-0471418054	Kerlinger, F. N. (1964). Foundations of Behavioral Research. Holt, Rinehart and Winston.
Alias	robust causal research, outlier-resistant explanatory design, robust regression-based explanatory study	ex post facto research, causal-comparative design, retrospective causal study, CCR
Correlati	4	3
Sintesi	Robust explanatory research combines the explanatory goal of identifying why and how variables causally influence one another with robust statistical methods that remain valid when data violate classical assumptions — particularly normality, homoscedasticity, and the absence of influential outliers. Rather than discarding outliers or forcing data to conform to ordinary least squares assumptions, this design applies estimators and inferential procedures that down-weight or resist the distorting influence of extreme observations while preserving the explanatory aim of the study.	Causal-comparative research is a non-experimental quantitative design in which the researcher compares two or more groups that already differ on an independent variable — one that was not manipulated — to investigate possible causes or consequences of that difference. Because group membership is pre-existing rather than randomly assigned, the design can suggest causal relationships but cannot establish them with the certainty of a true experiment. It is widely used in education, psychology, and social sciences when experimental manipulation is impractical or unethical.