השוואת שיטות
סקרו את השיטות שבחרתם זו לצד זו; שורות שבהן יש הבדל מודגשות.
| מחקר הסברי חסין (Robust Explanatory Research)× | מחקר הסברי רב-משתני× | |
|---|---|---|
| תחום | תכנון מחקר | תכנון מחקר |
| משפחה | Process / pipeline | Process / pipeline |
| שנת המקור≠ | 1960s–1980s (robust statistics foundations); applied to explanatory research from 1990s onward | Mid-to-late 20th century (consolidated ~1960s–1980s) |
| הוגה השיטה≠ | 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 |
| סוג | Quantitative research design | Quantitative research design |
| מקור מכונן≠ | 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 |
| כינויים≠ | robust causal research, outlier-resistant explanatory design, robust regression-based explanatory study | multivariate explanatory design, explanatory multivariate research, multivariate causal-explanatory study, MER |
| קשורות | 4 | 4 |
| תקציר≠ | 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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