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| Robuszt magyarázó kutatás× | Magyarázó kutatási terv× | |
|---|---|---|
| Tudományterület | Kutatástervezés | Kutatástervezés |
| Módszercsalád | Process / pipeline | Process / pipeline |
| Keletkezés éve≠ | 1960s–1980s (robust statistics foundations); applied to explanatory research from 1990s onward | 1960s–1980s (codified in behavioral and social science methodology) |
| Megalkotó≠ | Peter J. Huber (robust statistics); applied to explanatory designs via Rand Wilcox and others | Formalized by Earl Babbie and Fred Kerlinger among others |
| Típus≠ | Quantitative research design | Non-experimental quantitative research design |
| Alapmű≠ | 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 |
| Alternatív nevek≠ | robust causal research, outlier-resistant explanatory design, robust regression-based explanatory study | analytical research, causal research, explanatory study, explanatory quantitative research |
| Kapcsolódó≠ | 4 | 5 |
| Összefoglaló≠ | 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. |
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