Сравнение на методи

	Изследвания за робустна проверка на модели	Изследвания за тестване на байесови модели
Област	Дизайн на изследването	Дизайн на изследването
Семейство	Process / pipeline	Process / pipeline
Година на възникване	1988–1998	1935 (Jeffreys); widely adopted in social and behavioral sciences from the 1990s onward
Създател	Albert Satorra & Peter M. Bentler; Ke-Hai Yuan	Harold Jeffreys; formalized for applied sciences by Robert Kass and Adrian Raftery
Тип	Quantitative model-testing research design with robust estimation	Quantitative inferential research design
Основополагащ източник	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.	Kass, R. E., & Raftery, A. E. (1995). Bayes factors. Journal of the American Statistical Association, 90(430), 773–795.
Други названия	robust SEM, robust structural model testing, robust fit evaluation, robust model evaluation research	Bayesian hypothesis testing, Bayesian model comparison, Bayes factor analysis, BMT
Свързани	6	4
Резюме	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.	Bayesian model testing research is a quantitative design in which competing theoretical models or hypotheses are evaluated by comparing their marginal likelihoods given observed data. The central tool is the Bayes factor — a ratio that quantifies how much more likely the data are under one model than under another. Unlike null-hypothesis significance testing, Bayesian model testing yields direct evidence for or against specific hypotheses, incorporates prior knowledge, and can support a null hypothesis rather than merely failing to reject it.