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Method-of-Moment Corrected Maximum Likelihood (Ml) Structural-after-Measurement (SAM) Estimator for n-Level Structural Equation Models
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Fangxing Bai; Ben Kelcey – Society for Research on Educational Effectiveness, 2024
Purpose and Background: Despite the flexibility of multilevel structural equation modeling (MLSEM), a practical limitation many researchers encounter is how to effectively estimate model parameters with typical sample sizes when there are many levels of (potentially disparate) nesting. We develop a method-of-moment corrected maximum likelihood…
Descriptors: Maximum Likelihood Statistics, Structural Equation Models, Sample Size, Faculty Development
Structural after Measurement Estimation with Missing Data in Structural Equation Models
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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