So sánh phương pháp

	Nghiên cứu kiểm định mô hình mạnh mẽ	Nghiên cứu kiểm định mô hình đa biến
Lĩnh vực	Thiết kế nghiên cứu	Thiết kế nghiên cứu
Họ	Process / pipeline	Process / pipeline
Năm ra đời	1988–1998	1970s–1980s (multivariate model testing as a distinct approach)
Người khởi xướng	Albert Satorra & Peter M. Bentler; Ke-Hai Yuan	Karl Jöreskog (SEM/LISREL framework); Barbara Tabachnick & Linda Fidell (multivariate methods synthesis)
Loại	Quantitative model-testing research design with robust estimation	Quantitative confirmatory research design
Công trình gốc	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
Tên gọi khác	robust SEM, robust structural model testing, robust fit evaluation, robust model evaluation research	multivariate model testing, multivariate structural testing, multivariate confirmatory modeling, MVMT research
Liên quan	6	5
Tóm tắt	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.