Effects of latent variable nonnormality and model misspecification on testing structural equation modeling interactions

Journal of Experimental Education

Volumn 79, Issue 3, 2011, Pages 231-256

  Sun, Shaojing ^a   Konold, Timothy R ^b   Fan, Xitao ^c  

^a FUDAN UNIVERSITY   (China)

^b UNIVERSITY OF VIRGINIA   (United States)

^c UNIVERSITY OF MACAU   (Macao)

Author keywords

latent variable interaction; model fit; model misspecification; nonnormality; parameter estimate; structural equation model

Indexed keywords

EID: 79957831212     PISSN: 00220973     EISSN: 19400683     Source Type: Journal    
DOI: 10.1080/00220973.2010.481683     Document Type: Article

References (37)

1. Anderson, T. W. (2003). An introduction to multivariate statistical analysis (3rd ed.). Hoboken, NJ: Wiley.
2. Batista-Foguet, J. M., Coenders, G., Saris, W. E., & Bisbe, J. (2004). Simultaneous estimation of indirect and interaction effects using structural equation models. Metodoloski Zvezki, 1, 163-184.
3. Bollen, K. A. (1989). Structural equations with latent variables. New York, NY: Wiley.
4. Casella, G., & Berger, R. L. (2002). Statistical inference. Pacific Grove, CA: Wadsworth.
5. Cheng, S., & Chan, A. C. M. (2006). Relationship with others and life satisfaction in later life: Do gender and widowhood make a difference? Journal of Gerontology, 61, 46-53.
6. Chin, W. W., Marcolin, B. L., & Newsted, P. R. (1996). A partial least squares latent variable modeling approach for measuring interaction effects: Results from a Monte Carlo simulation study and voice mail emotion/adoption study. Proceedings of the Seventeenth International Conference on Information Systems, 21-41.
7. Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2003). Applied multiple regression/correlation analysis for the behavioral sciences (3rd ed.). Hillsdale, NJ: Erlbaum.
8. Curran, P. J., West, S. G., & Finch, J. F. (1996). The robustness of test statistics to nonnormality and specification error in confirmatory factor analysis. Psychological Methods, 1, 16-29. (Pubitemid 126421251)
9. Fan, X., & Fan, X. (2005). Using SAS for Monte Carlo simulation research in structural equation modeling. Structural Equation Modeling, 12, 299-333. (Pubitemid 40645248)
10. Fan, X., Felsovalyi, A., Sivo, S. A., & Keenan, S. (2003). SAS for Monte Carlo studies: A guide for quantitative researchers. Cary, NC: SAS Institute.
11. Fan, X., & Sivo, S. A. (2005). Sensitivity of fit indexes to misspecified structural or measurement model components: Rationale of two-index strategy revisited. Structural Equation Modeling, 12, 343-367. (Pubitemid 41311758)
12. Fan, X., Thompson, B., & Wang, L. (1999). Effects of sample size, estimation methods, and model specification on structural equation modeling fit indexes. Structural Equation Modeling, 6(1), 56-83.
13. Fleishman, A. I. (1978). A method for simulating non-normal distributions. Psychometrika, 43, 521-531.
14. Grewal, R., Cote, J. A., & Baumgartner, H. (2004). Multicollinearity and measurement error in structural equation models: Implications for theory testing. Marketing Science, 23, 519-529.
15. Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1-55.
16. Jöreskog, K. G. (2000). Latent variable scores and their uses. Lincolnwood, IL: Scientific Software International. Retrieved from http://www.ssicentral.com
17. Jöreskog, K. G., & Sörbom, D. (1996a). LISREL 8: User's reference guide. Lincolnwood, IL: Scientific Software.
18. Jöreskog, K. G., & Sörbom, D. (1996b). PRELIS 2: User's reference guide. Lincolnwood, IL: Scientific Software.
19. Jöreskog, K. G.,& Yang, F. (1996). Nonlinear structural equation models: The Kenny-Judd model with interaction effects. In G. A. Marcoulides & R. E. Schumaker (Eds.), Advanced structural equation modeling: Issues and techniques (pp. 57-89). Mahwah, NJ: Erlbaum.
20. Kenny, D. A., & Judd, C. M. (1984). Estimating the nonlinear and interactive effects of latent variables. Psychological Bulletin, 96, 201-210.
21. Klein, A., & Moosbrugger, H. (2000). Maximum likelihood estimation of latent interaction effects with the LMS method. Psychometrika, 65, 457-474. (Pubitemid 33222960)
22. Lei, M., & Lomax, R. G. (2005). The effect of varying degrees of nonnormality in structural equation modeling. Structural Equation Modeling, 12, 1-27. (Pubitemid 40163161)
23. Marsh, H. W., Wen, Z., & Hau, K. (2004). Structural equation models of latent interactions: Evaluation of alternative estimation strategies and indicator construction. Psychological Methods, 9, 275-300. (Pubitemid 39146306)
24. Mattson, S. (1997). How to generate non-normal data for simulation of structural equation models. Multivariate Behavioral Research, 32, 355-373. (Pubitemid 127341624)
25. Mooney, C. Z. (1997). Monte Carlo simulation. Thousand Oaks, CA: Sage.
26. Moulder, B. C., & Algina, J. (2002). Comparison of methods for estimating and testing latent variable interaction. Structural Equation Modeling, 9(1), 1-19.
27. Ping, R. A. (1998). EQS and LISREL examples using survey data. In R. E. Schumacker & G. A. Marcoulides (Eds.), Interaction and nonlinear effects in structural equation modeling (pp. 63-100). Mahwah, NJ: Erlbaum.
28. Reinartz, W. J., Echambadi, R., & Chin, W. W. (2002). Generating non-normal data for simulation of structural equation models using Mattson's method. Multivariate Behavioral Research, 37, 227-244. (Pubitemid 36630365)
29. Schumacker, R. E. (2002). Latent variable interaction modeling. Structural Equation Modeling, 9(1), 40-54.
30. Schumacker, R. E., & Marcoulides, G. A. (Eds.). (1998). Interaction and nonlinear effects in structural equation modeling. Mahwah, NJ: Erlbaum.
31. Skrondal, A. (2000). Design and analysis of Monte carol experiments: Attacking the conventional wisdom. Multivariate Behavioral Research, 35, 137-167.
32. Skrondal, A., & Rabe-Hesketh, S. (2004). Generalized latent variable modeling: Multilevel, longitudinal, and structural equation models. Boca Raton, FL: Chapman & Hall.
33. Tanaka, J. S. (1993). Multifaceted conceptions of fit in structural equation models. In K. A. Bollen, & J. S. Long (Eds.), Testing structural equation models (pp. 10-39). Newbury Park, CA: Sage.
34. Vale, C. D., & Maurelli, V. A. (1983). Simulating multivariate nonnormal distributions. Psychometrika, 48, 465-471.
35. Wall, M. M., & Amemiya, Y. (2001). Generalized appended product indicator procedure for nonlinear structural equation analysis. Journal of Educational and Behavioral Statistics, 26, 1-29. (Pubitemid 33621263)
36. Wang, L., Fan, X., & Willson, V. L. (1996). Effects of nonnormal data on parameter estimates and fit indices for a model with latent and manifest variables: An empirical study. Structural Equation Modeling, 3, 228-247.
37. Yang, F. (1997). Nonlinear structural equation models: Simulation studies of the Kenny-Judd model. Unpublished doctoral dissertation, Uppsala University, Sweden.