Jämför metoder

	Robust förklarande forskning	Multivariat förklarande forskning
Ämnesområde	Forskningsdesign	Forskningsdesign
Familj	Process / pipeline	Process / pipeline
Ursprungsår	1960s–1980s (robust statistics foundations); applied to explanatory research from 1990s onward	Mid-to-late 20th century (consolidated ~1960s–1980s)
Upphovsperson	Peter J. Huber (robust statistics); applied to explanatory designs via Rand Wilcox and others	Rooted in the multivariate statistics tradition (R.A. Fisher, Harold Hotelling) combined with explanatory research design conventions codified by Kerlinger and others
Typ	Quantitative research design	Quantitative research design
Ursprungskälla	Huber, P. J. (1981). Robust Statistics. Wiley. ISBN: 978-0471418054	Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate Data Analysis (8th ed.). Cengage Learning. ISBN: 978-1473756540
Alias	robust causal research, outlier-resistant explanatory design, robust regression-based explanatory study	multivariate explanatory design, explanatory multivariate research, multivariate causal-explanatory study, MER
Närliggande	4	4
Sammanfattning	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.	Multivariate explanatory research is a quantitative design that simultaneously examines multiple independent variables to explain variance in one or more outcomes. Rather than describing what exists or simply correlating pairs of variables, it seeks causal or structural explanations by testing theoretically grounded models with techniques such as multiple regression, MANOVA, or structural equation modeling on survey, administrative, or observational numeric data.