- Estimating the Weight Matrix in Distributionally Weighted Least Squares Estimation: An Empirical Bayesian SolutionEstimating the Weight Matrix in Distributionally Weighted Least Squares Estimation: An Empirical Bayesian SolutionReal 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 …
- Comparing DIC and WAIC for multilevel models with missing dataIn Bayesian statistics, the most widely used criteria of Bayesian model assessment and comparison are Deviance Information Criterion (DIC) and Watanabe–Akaike Information Criterion (WAIC). We use a multilevel mediation model as an illustrative example to compare different types of DIC and WAIC. More specifically, we aim to compare the performance of conditional and marginal DICs …
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- Extended Unbiased Distribution Free Estimator With Mean StructuresTo handle the nonnormal data issue, Browne proposed an unbiased distribution free (DF) estimator (ΓˆUDF) and an asymptotically distribution free estimator (ΓˆADF) of the covariance matrix of sample variances/covariances Γ to calculate robust test statistics and robust standard errors. However, ΓˆUDF is ignored in methodological and substantive research, and has not been extended to models …
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- “40-Year Old Unbiased Distribution Free Estimator Reliably Improves SEM Statistics for Nonnormal Data” is accepted by Structural Equation ModelingAbstract: In structural equation modeling, researchers conduct goodness-of-fit tests to evaluate whether the specified model fits the data well. With nonnormal data, the standard goodness-of-fit test statistic T does not follow a chi-square distribution. Comparing T to χ df 2 can fail to control Type I error rates and lead to misleading model selection conclusions. …
- “Compatibility in Imputation Specification” is accepted by Behavior Research MethodsMissing data such as data missing at random (MAR) are unavoidable in real data and have the potential to undermine the validity of research results. Multiple imputation is one of the most widely used MAR-based methods in education and behavioral science applications. Arbitrarily specifying imputation models can lead to incompatibility and cause biased estimation. Building …
- “Distributionally Weighted Least Squares in Growth Curve Modelin” is accepted by Structural Equation ModelingGrowth curve modeling is commonly used in psychological, educational, and social science research. The mainstream estimators for growth curve modeling are based on normal theory, but real data are unlikely to be exactly normally distributed. To improve estimation and inference with non-normal data, various estimators have been proposed. Among these estimators, the asymptotically distribution free …
- Congratulations to Stefany MenaStefany Mena is awarded the National Science Foundation Graduate Research Fellowship (NSF GRFP) in 2020. The NSF GRFP is a three-year fellowship awarded to doctoral students in STEM fields.
- “A Bayesian Latent Variable Selection Model for Nonignorable Missingness” is accepted by Multivariate Behavioral ResearchMissing data are exceedingly common across a variety of disciplines, such as educational, social, and behavioral science areas. Missing not at random (MNAR) mechanism where missingness is related to unobserved data is widespread in real data and has detrimental consequence. However, the existing MNAR-based methods have potential problems such as leaving the data incomplete and …
- “Distributionally-Weighted Least Squares in Structural Equation Modeling” is accepted by Psychological MethodsIn real data analysis with structural equation modeling, data are unlikely to be exactly normally distributed. If we ignore the non-normality reality, the parameter estimates, standard error estimates, and model fit statistics from normal theory based methods such as maximum likelihood (ML) and normal theory based generalized least squares estimation (GLS) are unreliable. On the …