Confronta i metodi

	Ricerca esplicativa robusta	Ricerca esplicativa
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	1960s–1980s (codified in behavioral and social science methodology)
Ideatore	Peter J. Huber (robust statistics); applied to explanatory designs via Rand Wilcox and others	Formalized by Earl Babbie and Fred Kerlinger among others
Tipo	Quantitative research design	Non-experimental quantitative research design
Fonte seminale	Huber, P. J. (1981). Robust Statistics. Wiley. ISBN: 978-0471418054	Kerlinger, F. N. (1986). Foundations of Behavioral Research (3rd ed.). Holt, Rinehart and Winston. ISBN: 978-0030417559
Alias	robust causal research, outlier-resistant explanatory design, robust regression-based explanatory study	analytical research, causal research, explanatory study, explanatory quantitative research
Correlati	4	5
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.	Explanatory research is a non-experimental quantitative research design that goes beyond describing a phenomenon to identifying why it occurs — examining the relationships or mechanisms that account for observed patterns. Rooted in positivist social science methodology, it uses theory-driven hypotheses and statistical analysis to test whether specific variables explain variation in an outcome, without necessarily manipulating those variables.