Salīdzināt metodes

	Pētījumi par Bayes modeles testēšanu	Apstiprinošā faktoru analīze (AFA)
Nozare	Pētījuma dizains	Psihometrija
Saime	Process / pipeline	Latent structure
Izcelsmes gads	1935 (Jeffreys); widely adopted in social and behavioral sciences from the 1990s onward	1969
Autors	Harold Jeffreys; formalized for applied sciences by Robert Kass and Adrian Raftery	Karl Gustav Jöreskog
Tips	Quantitative inferential research design	Hypothesis-testing latent variable model
Pirmavots	Kass, R. E., & Raftery, A. E. (1995). Bayes factors. Journal of the American Statistical Association, 90(430), 773–795.	Jöreskog, K. G. (1969). A general approach to confirmatory maximum likelihood factor analysis. Psychometrika, 34(2), 183–202.
Citi nosaukumi	Bayesian hypothesis testing, Bayesian model comparison, Bayes factor analysis, BMT	CFA, confirmatory FA, measurement model, restricted factor analysis
Saistītās	4	4
Kopsavilkums	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.	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.