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Reichardt, Charles S. – Multivariate Behavioral Research, 2011
Maxwell, Cole, and Mitchell (2011) demonstrated that simple structural equation models, when used with cross-sectional data, generally produce biased estimates of meditated effects. I extend those results by showing how simple structural equation models can produce biased estimates of meditated effects when used even with longitudinal data. Even…
Descriptors: Structural Equation Models, Statistical Data, Longitudinal Studies, Error of Measurement
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McDonald, Roderick P. – Multivariate Behavioral Research, 2004
Additive and non-additive models for an individual trend curve are examined, and five methods for fitting these to a set of individuals are described. It is suggested that classical fitting methods are more informative than latent curve methods, and commonly preferable. A limited study of the effect of time-structure is reported, and results on…
Descriptors: Models, Statistical Data, Item Response Theory, Multivariate Analysis
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Nevitt, Jonathan; Hancock, Gregory R. – Multivariate Behavioral Research, 2004
Through Monte Carlo simulation, small sample methods for evaluating overall data-model fit in structural equation modeling were explored. Type I error behavior and power were examined using maximum likelihood (ML), Satorra-Bentler scaled and adjusted (SB; Satorra & Bentler, 1988, 1994), residual-based (Browne, 1984), and asymptotically…
Descriptors: Statistical Data, Sample Size, Monte Carlo Methods, Structural Equation Models