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Wu, Jiun-Yu; Kwok, Oi-man – Structural Equation Modeling: A Multidisciplinary Journal, 2012
Both ad-hoc robust sandwich standard error estimators (design-based approach) and multilevel analysis (model-based approach) are commonly used for analyzing complex survey data with nonindependent observations. Although these 2 approaches perform equally well on analyzing complex survey data with equal between- and within-level model structures…
Descriptors: Structural Equation Models, Surveys, Data Analysis, Comparative Analysis
DeMars, Christine E. – Structural Equation Modeling: A Multidisciplinary Journal, 2012
In structural equation modeling software, either limited-information (bivariate proportions) or full-information item parameter estimation routines could be used for the 2-parameter item response theory (IRT) model. Limited-information methods assume the continuous variable underlying an item response is normally distributed. For skewed and…
Descriptors: Item Response Theory, Structural Equation Models, Computation, Computer Software
Raykov, Tenko; Marcoulides, George A. – Structural Equation Modeling: A Multidisciplinary Journal, 2010
A latent variable modeling approach for examining population similarities and differences in observed variable relationship and mean indexes in incomplete data sets is discussed. The method is based on the full information maximum likelihood procedure of model fitting and parameter estimation. The procedure can be employed to test group identities…
Descriptors: Models, Comparative Analysis, Groups, Maximum Likelihood Statistics
Cheung, Mike W. L.; Chan, Wai – Structural Equation Modeling: A Multidisciplinary Journal, 2009
Structural equation modeling (SEM) is widely used as a statistical framework to test complex models in behavioral and social sciences. When the number of publications increases, there is a need to systematically synthesize them. Methodology of synthesizing findings in the context of SEM is known as meta-analytic SEM (MASEM). Although correlation…
Descriptors: Structural Equation Models, Simulation, Social Sciences, Correlation
Raykov, Tenko; Brennan, Mark; Reinhardt, Joann P.; Horowitz, Amy – Structural Equation Modeling: A Multidisciplinary Journal, 2008
A correlation structure modeling method for comparison of mediated effects is outlined. The procedure permits point and interval estimation of differences in mediator effects, and is useful with models postulating 1 or more predictor, intervening, or response variables that may also be latent constructs. The approach allows scale-free evaluation…
Descriptors: Multivariate Analysis, Comparative Analysis, Correlation, Structural Equation Models
Ximenez, Carmen – Structural Equation Modeling: A Multidisciplinary Journal, 2006
The recovery of weak factors has been extensively studied in the context of exploratory factor analysis. This article presents the results of a Monte Carlo simulation study of recovery of weak factor loadings in confirmatory factor analysis under conditions of estimation method (maximum likelihood vs. unweighted least squares), sample size,…
Descriptors: Monte Carlo Methods, Factor Analysis, Least Squares Statistics, Sample Size