Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Надійне пояснювальне дослідження× | Дослідження з перевіркою гіпотез× | |
|---|---|---|
| Галузь | Дизайн дослідження | Дизайн дослідження |
| Родина | Process / pipeline | Process / pipeline |
| Рік появи≠ | 1960s–1980s (robust statistics foundations); applied to explanatory research from 1990s onward | Early 20th century (Fisher 1925; Neyman–Pearson 1933) |
| Автор методу≠ | Peter J. Huber (robust statistics); applied to explanatory designs via Rand Wilcox and others | Karl Pearson, Ronald A. Fisher, Jerzy Neyman, Egon Pearson |
| Тип≠ | Quantitative research design | Quantitative confirmatory research design |
| Основоположне джерело≠ | Huber, P. J. (1981). Robust Statistics. Wiley. ISBN: 978-0471418054 | Kerlinger, F. N., & Lee, H. B. (1986). Foundations of Behavioral Research (3rd ed.). Holt, Rinehart and Winston. ISBN: 978-0030417603 |
| Інші назви≠ | robust causal research, outlier-resistant explanatory design, robust regression-based explanatory study | hypothetico-deductive research, confirmatory quantitative research, null hypothesis significance testing, NHST design |
| Пов'язані | 4 | 4 |
| Підсумок≠ | 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. | Hypothesis testing research is a quantitative design in which the investigator derives one or more explicit, falsifiable propositions from theory, translates them into a null hypothesis (H0) and an alternative hypothesis (H1), collects empirical data, and then applies an inferential statistical test to decide whether the evidence is sufficient to reject H0. The approach is the dominant paradigm for confirmatory science across the social, behavioral, health, and natural sciences. |
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