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A Test for Cluster Bias: Detecting Violations of Measurement Invariance across Clusters in Multilevel Data
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Jak, Suzanne; Oort, Frans J.; Dolan, Conor V. – Structural Equation Modeling: A Multidisciplinary Journal, 2013
We present a test for cluster bias, which can be used to detect violations of measurement invariance across clusters in 2-level data. We show how measurement invariance assumptions across clusters imply measurement invariance across levels in a 2-level factor model. Cluster bias is investigated by testing whether the within-level factor loadings…
Descriptors: Statistical Bias, Measurement, Structural Equation Models, Hierarchical Linear Modeling
ARMA-Based SEM When the Number of Time Points T Exceeds the Number of Cases N: Raw Data Maximum Likelihood.
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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
On the Nature of SEM Estimates of ARMA Parameters.
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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
A Cautionary Note on the Use of Information Fit Indexes in Covariance Structure Modeling with Means
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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