Сравнение методов

	Robust Model Testing Research	Конфирматорный факторный анализ (КФА)
Область	Дизайн исследования	Психометрия
Семейство	Process / pipeline	Latent structure
Год появления	1988–1998	1969
Автор метода	Albert Satorra & Peter M. Bentler; Ke-Hai Yuan	Karl Gustav Jöreskog
Тип	Quantitative model-testing research design with robust estimation	Hypothesis-testing latent variable model
Основополагающий источник	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: Applications for developmental research (pp. 399–419). Sage.	Jöreskog, K. G. (1969). A general approach to confirmatory maximum likelihood factor analysis. Psychometrika, 34(2), 183–202.
Другие названия	robust SEM, robust structural model testing, robust fit evaluation, robust model evaluation research	CFA, confirmatory FA, measurement model, restricted factor analysis
Связанные	6	4
Сводка	Robust model testing research applies structural or path models to data while explicitly accounting for violations of multivariate normality and other distributional assumptions. Rather than discarding non-normal data or forcing transformations, it uses corrected estimators — most notably the Satorra-Bentler scaled chi-square and Yuan-Bentler robust standard errors — to produce trustworthy fit indices and parameter estimates even when classical maximum likelihood assumptions are breached.	Confirmatory factor analysis tests a researcher-specified factor structure against observed data. Unlike exploratory approaches, the researcher decides in advance which indicators load on which latent factor, and the model is evaluated by how closely the implied covariance matrix reproduces the sample covariance matrix. CFA is central to scale validation, construct validity assessment, and measurement invariance testing.