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Yuan Fang; Lijuan Wang – Grantee Submission, 2024
Dynamic structural equation modeling (DSEM) is a useful technique for analyzing intensive longitudinal data. A challenge of applying DSEM is the missing data problem. The impact of missing data on DSEM, especially on widely applied DSEM such as the two-level vector autoregressive (VAR) cross-lagged models, however, is understudied. To fill the…
Descriptors: Structural Equation Models, Research Problems, Longitudinal Studies, Simulation
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Teague R. Henry; Zachary F. Fisher; Kenneth A. Bollen – Structural Equation Modeling: A Multidisciplinary Journal, 2024
Model-Implied Instrumental Variable Two-Stage Least Squares (MIIV-2SLS) is a limited information, equation-by-equation, noniterative estimator for latent variable models. Associated with this estimator are equation-specific tests of model misspecification. One issue with equation-specific tests is that they lack specificity, in that they indicate…
Descriptors: Bayesian Statistics, Least Squares Statistics, Structural Equation Models, Equations (Mathematics)
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Dongho Shin – Grantee Submission, 2024
We consider Bayesian estimation of a hierarchical linear model (HLM) from small sample sizes. The continuous response Y and covariates C are partially observed and assumed missing at random. With C having linear effects, the HLM may be efficiently estimated by available methods. When C includes cluster-level covariates having interactive or other…
Descriptors: Bayesian Statistics, Computation, Hierarchical Linear Modeling, Data Analysis
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Ben Kelcey; Fangxing Bai; Amota Ataneka; Yanli Xie; Kyle Cox – Society for Research on Educational Effectiveness, 2024
We develop a structural after measurement (SAM) method for structural equation models (SEMs) that accommodates missing data. The results show that the proposed SAM missing data estimator outperforms conventional full information (FI) estimators in terms of convergence, bias, and root-mean-square-error in small-to-moderate samples or large samples…
Descriptors: Structural Equation Models, Research Problems, Error of Measurement, Maximum Likelihood Statistics