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

Sun, Shaojing

Softcover

ISBN 10: 363917366X ISBN 13: 9783639173666

Publisher: VDM Verlag, 2009

Synopsis

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.

About this title

About the Author

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.

Edition details

• Publisher: VDM Verlag
• Publication date: 2009
• Language: English
• ISBN 10: 363917366X
• ISBN 13: 9783639173666
• Binding: Paperback
• Number of pages: 124