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Testing Latent Variable Interaction Effect: dealing with data nonnormality and model misspecification - Tapa blanda

Sun, Shaojing

 
9783639173666: Testing Latent Variable Interaction Effect: dealing with data nonnormality and model misspecification
Tapa blanda
ISBN 10: 363917366X ISBN 13: 9783639173666
Editorial: VDM Verlag, 2009

Sinopsis

The book discusses the effects of data nonnormality, model misspecification, sample size, and effect size on testing latent variable interactions through an inspection of the Jöreskog and Yang's (1996) model. Mattson's (1997) method was used to generate nonnormal latent variables in this Monte Carlo study. One covariance parameter was deleted for investigating the influence of misspecified models. The simulation involved a balanced experimental design, with 3 × 2 × 3 × 3 = 54 combinations. Data analysis focused on bias of estimating parameters, standard errors, model fit indexes. Variance partition was conducted to further examine the unique and combined influence of the factors (i.e., data nonnormality, model specification, sample size, effect size). Results indicated that data nonnormality and model misspecification had large effects on fit indexes (e.g., SRMR, RMSEA). Also, severe nonnormality led to a large bias of estimating the interaction effect. Implications of and recommendations for testing latent variable interactions are discussed.

"Sinopsis" puede pertenecer a otra edición de este libro.

Reseña del editor

The book discusses the effects of data nonnormality, model misspecification, sample size, and effect size on testing latent variable interactions through an inspection of the Jöreskog and Yang's (1996) model. Mattson's (1997) method was used to generate nonnormal latent variables in this Monte Carlo study. One covariance parameter was deleted for investigating the influence of misspecified models. The simulation involved a balanced experimental design, with 3 × 2 × 3 × 3 = 54 combinations. Data analysis focused on bias of estimating parameters, standard errors, model fit indexes. Variance partition was conducted to further examine the unique and combined influence of the factors (i.e., data nonnormality, model specification, sample size, effect size). Results indicated that data nonnormality and model misspecification had large effects on fit indexes (e.g., SRMR, RMSEA). Also, severe nonnormality led to a large bias of estimating the interaction effect. Implications of and recommendations for testing latent variable interactions are discussed.

Biografía del autor

Shaojing Sun, Currently an associate professor of the School of Journalism at Fudan University in China. He obtained his first Ph.D. in communication from Kent State University, and second Ph.D. in research methodology from University of Virginia in the U.S. He taught at University of Maryland and Weber State University in the U.S. before.

"Sobre este título" puede pertenecer a otra edición de este libro.

Editorial
VDM Verlag
Año de publicación
2009
Idioma
Inglés
ISBN 10 
363917366X
ISBN 13 
9783639173666
Encuadernación
Tapa blanda
Número de páginas
124
Contacto del fabricante
no disponible
Persona responsable
no disponible