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Direct linkHan Du; Hao Wu – Structural Equation Modeling: A Multidisciplinary Journal, 2024
Real data are unlikely to be exactly normally distributed. Ignoring non-normality will cause misleading and unreliable parameter estimates, standard error estimates, and model fit statistics. For non-normal data, researchers have proposed a distributionally-weighted least squares (DLS) estimator to combines the normal theory based generalized…
Descriptors: Least Squares Statistics, Matrices, Statistical Distributions, Bayesian Statistics
Haiyan Liu; Sarah Depaoli; Lydia Marvin – Structural Equation Modeling: A Multidisciplinary Journal, 2022
The deviance information criterion (DIC) is widely used to select the parsimonious, well-fitting model. We examined how priors impact model complexity (pD) and the DIC for Bayesian CFA. Study 1 compared the empirical distributions of pD and DIC under multivariate (i.e., inverse Wishart) and separation strategy (SS) priors. The former treats the…
Descriptors: Structural Equation Models, Bayesian Statistics, Goodness of Fit, Factor Analysis
