Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Investigación Explicativa Robusta× | Investigación explicativa multivariante× | |
|---|---|---|
| Campo | Diseño de investigación | Diseño de investigación |
| Familia | Process / pipeline | Process / pipeline |
| Año de origen≠ | 1960s–1980s (robust statistics foundations); applied to explanatory research from 1990s onward | Mid-to-late 20th century (consolidated ~1960s–1980s) |
| Autor original≠ | 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 |
| Tipo | Quantitative research design | Quantitative research design |
| Fuente seminal≠ | 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 |
| Relacionados | 4 | 4 |
| Resumen≠ | 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. |
| ScholarGateConjunto de datos ↗ |
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