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| Structural Equation Modeling | 2 |
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| Enders, Craig K. | 2 |
| Bandalos, Deborah L. | 1 |
| Peugh, James L. | 1 |
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Peer reviewedEnders, Craig K.; Bandalos, Deborah L. – Structural Equation Modeling, 2001
Used Monte Carlo simulation to examine the performance of four missing data methods in structural equation models: (1)full information maximum likelihood (FIML); (2) listwise deletion; (3) pairwise deletion; and (4) similar response pattern imputation. Results show that FIML estimation is superior across all conditions of the design. (SLD)
Descriptors: Maximum Likelihood Statistics, Monte Carlo Methods, Simulation, Structural Equation Models
Direct linkEnders, Craig K.; Peugh, James L. – Structural Equation Modeling, 2004
Two methods, direct maximum likelihood (ML) and the expectation maximization (EM) algorithm, can be used to obtain ML parameter estimates for structural equation models with missing data (MD). Although the 2 methods frequently produce identical parameter estimates, it may be easier to satisfy missing at random assumptions using EM. However, no…
Descriptors: Inferences, Structural Equation Models, Factor Analysis, Error of Measurement
