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| 강건한 조절 분석(Robust Moderation Analysis)× | 강건 구조방정식 모형× | |
|---|---|---|
| 분야 | 통계학 | 통계학 |
| 계열 | Latent structure | Latent structure |
| 기원 연도≠ | 2007 | 1994 |
| 창시자≠ | Hayes & Cai; Wilcox | Albert Satorra & Peter M. Bentler |
| 유형≠ | Robust regression-based interaction test | Latent variable / path model with robust inference |
| 원전≠ | Hayes, A. F. & Cai, L. (2007). Using heteroscedasticity-consistent standard error estimators in OLS regression: An introduction and software implementation. Behavior Research Methods, 39(4), 709–722. DOI ↗ | Satorra, A. & Bentler, P. M. (1994). Corrections to test statistics and standard errors in covariance structure analysis. In A. von Eye & C. C. Clogg (Eds.), Latent variables analysis (pp. 399–419). Sage. link ↗ |
| 별칭 | robust interaction analysis, robust moderated regression, HC-corrected moderation, outlier-resistant interaction testing | Robust SEM, SEM with robust standard errors, Satorra-Bentler SEM, non-normal SEM |
| 관련 | 5 | 5 |
| 요약≠ | Robust moderation analysis tests whether the effect of a predictor on an outcome depends on the level of a moderator variable, using estimation methods that remain valid under non-normality, heteroscedasticity, or the presence of influential outliers. It is the preferred approach when standard ordinary least squares assumptions cannot be trusted. | Robust structural equation modeling (Robust SEM) applies the full SEM framework — simultaneous estimation of measurement and structural relations among latent variables — while using corrected test statistics and sandwich standard errors that remain valid when observed data depart from multivariate normality. The Satorra-Bentler scaled chi-square is the most widely used correction. |
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