Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Анализ модерации (взаимодействия)× | Регрессия методом обыкновенных наименьших квадратов (ОНМК)× | |
|---|---|---|
| Область≠ | Причинно-следственный вывод | Эконометрика |
| Семейство | Regression model | Regression model |
| Год появления≠ | 2018 | 2019 |
| Автор метода≠ | Aiken & West (1991); Hayes (PROCESS, 2018) | Wooldridge (textbook treatment); classical least squares |
| Тип≠ | Linear regression with interaction term | Linear regression |
| Основополагающий источник≠ | Hayes, A. F. (2018). Introduction to Mediation, Moderation, and Conditional Process Analysis (2nd ed.). Guilford Press. ISBN: 978-1462534654 | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| Другие названия | interaction analysis, moderated regression, simple moderation, Düzenleyici Değişken Analizi (Moderation / İnteraksiyon) | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Связанные | 5 | 5 |
| Сводка≠ | Moderation analysis tests whether the effect of a predictor X on an outcome Y changes with the level of a third variable W, the moderator. It is estimated within a regression framework through an interaction term X×W, popularised by Aiken & West (1991) and Hayes's PROCESS macro (2018). | Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE). |
| ScholarGateНабор данных ↗ |
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