পদ্ধতির তুলনা করুন
নির্বাচিত পদ্ধতিগুলো পাশাপাশি পর্যালোচনা করুন; যে সারিগুলোয় পার্থক্য আছে সেগুলো চিহ্নিত করা হয়।
| Robust Explanatory Research× | বহুমাত্রিক ব্যাখ্যামূলক গবেষণা× | |
|---|---|---|
| ক্ষেত্র | গবেষণা নকশা | গবেষণা নকশা |
| পরিবার | Process / pipeline | Process / pipeline |
| উদ্ভবের বছর≠ | 1960s–1980s (robust statistics foundations); applied to explanatory research from 1990s onward | Mid-to-late 20th century (consolidated ~1960s–1980s) |
| প্রবর্তক≠ | 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 |
| ধরন | Quantitative research design | Quantitative research design |
| মৌলিক উৎস≠ | 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 |
| অপর নাম≠ | robust causal research, outlier-resistant explanatory design, robust regression-based explanatory study | multivariate explanatory design, explanatory multivariate research, multivariate causal-explanatory study, MER |
| সম্পর্কিত | 4 | 4 |
| সারসংক্ষেপ≠ | 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. |
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