Сравнение методов

	Robust Model Testing Research	Исследование с проверкой многомерных моделей
Область	Дизайн исследования	Дизайн исследования
Семейство	Process / pipeline	Process / pipeline
Год появления	1988–1998	1970s–1980s (multivariate model testing as a distinct approach)
Автор метода	Albert Satorra & Peter M. Bentler; Ke-Hai Yuan	Karl Jöreskog (SEM/LISREL framework); Barbara Tabachnick & Linda Fidell (multivariate methods synthesis)
Тип	Quantitative model-testing research design with robust estimation	Quantitative confirmatory research design
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.	Tabachnick, B. G., & Fidell, L. S. (2019). Using Multivariate Statistics (7th ed.). Pearson. ISBN: 978-0134790541
Другие названия	robust SEM, robust structural model testing, robust fit evaluation, robust model evaluation research	multivariate model testing, multivariate structural testing, multivariate confirmatory modeling, MVMT research
Связанные	6	5
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.	Multivariate model testing research is a confirmatory quantitative design in which a theoretically derived model involving multiple variables and their interrelationships is formally tested against empirical data. Rather than exploring patterns inductively, the researcher specifies a model a priori — capturing hypothesized directional paths, latent constructs, or covariance structures — and then evaluates how well this model reproduces the observed data using techniques such as structural equation modeling, confirmatory factor analysis, or multivariate path analysis.