Salīdzināt metodes

	Bejeziāņu konstruktās validitātes novērtēšana	Beijesiana testēšana par mērījumu nemainīgumu
Nozare	Psihometrija	Psihometrija
Saime	Latent structure	Latent structure
Izcelsmes gads	1955 / 2012	2013
Autors	Cronbach & Meehl (validity framework); Muthén & Asparouhov (Bayesian SEM extension)	Bengt Muthen, Tihomir Asparouhov, Rens Van de Schoot
Tips	Validity assessment / Bayesian inference	Bayesian multigroup latent variable test
Pirmavots	Muthén, B. & Asparouhov, T. (2012). Bayesian structural equation modeling: A more flexible representation of substantive theory. Psychological Methods, 17(3), 313–335.	Van de Schoot, R., Kluytmans, A., Tummers, L., Lugtig, P., Hox, J., & Muthen, B. (2013). Facing off with Scylla and Charybdis: a comparison of scalar, partial, and the novel possibility of approximate measurement invariance. Frontiers in Psychology, 4, 770.
Citi nosaukumi	Bayesian validity analysis, Bayesian CFA-based validity, Bayesian structural validity, posterior construct validity	Bayesian MI, approximate measurement invariance, Bayesian multigroup CFA invariance, BSEM measurement invariance
Saistītās	6	6
Kopsavilkums	Bayesian construct validity assessment uses Bayesian confirmatory factor analysis and related Bayesian structural equation models to evaluate whether a scale or test measures the intended latent construct. It yields full posterior distributions for factor loadings, structural coefficients, and model-fit indices rather than single point estimates, enabling more nuanced and uncertainty-aware validity conclusions.	Bayesian measurement invariance testing evaluates whether a scale's factor loadings and item intercepts are equivalent across groups, using a Bayesian framework that allows parameters to deviate from strict equality by a small, probabilistically specified amount rather than imposing an exact constraint.