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Structural Equation Modeling | 3 |
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Dolan, Conor V. | 3 |
Hamaker, Ellen L. | 2 |
Molenaar, Peter C. M. | 2 |
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Hamaker, Ellen L.; Dolan, Conor V.; Molenaar, Peter C. M. – Structural Equation Modeling, 2003
Demonstrated, through simulation, that stationary autoregressive moving average (ARMA) models may be fitted readily when T>N, using normal theory raw maximum likelihood structural equation modeling. Also provides some illustrations based on real data. (SLD)
Descriptors: Maximum Likelihood Statistics, Simulation, Structural Equation Models

Hamaker, Ellen L.; Dolan, Conor V.; Molenaar, Peter C. M. – Structural Equation Modeling, 2002
Reexamined the nature of structural equation modeling (SEM) estimates of autoregressive moving average (ARMA) models, replicated the simulation experiments of P. Molenaar, and examined the behavior of the log-likelihood ratio test. Simulation studies indicate that estimates of ARMA parameters observed with SEM software are identical to those…
Descriptors: Maximum Likelihood Statistics, Regression (Statistics), Simulation, Structural Equation Models
Wicherts, Jelte M.; Dolan, Conor V. – Structural Equation Modeling, 2004
Information fit indexes such as Akaike Information Criterion, Consistent Akaike Information Criterion, Bayesian Information Criterion, and the expected cross validation index can be valuable in assessing the relative fit of structural equation models that differ regarding restrictiveness. In cases in which models without mean restrictions (i.e.,…
Descriptors: Goodness of Fit, Structural Equation Models, Factor Structure, Indexes