REFERENCES
- Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107, 238–246. doi:10.1037/0033-2909.107.2.238
- Bentler, P. M. (2008). EQS structural equation modeling software. Encino, CA: Multivariate Software.
- Bentler, P. M., & Bonett, D. G. (1980). Significance tests and goodness of fit in the analysis of covariance structures. Psychological Bulletin, 88, 588–606. doi:10.1037/0033-2909.88.3.588
- Bollen, K. A. (1989). Structural equations with latent variables. New York, NY: Wiley.
- Brosseau-Liard, P. E., & Savalei, V. (2014). Adjusting incremental fit indices for nonnormality. Multivariate Behavioral Research, 49, 460–470. doi:10.1080/00273171.2014.933697
- Brosseau-Liard, P. E., Savalei, V., & Li, L. (2012). An investigation of the sample performance of two nonnormality corrections for RMSEA. Multivariate Behavioral Research, 47, 904–930. doi:10.1080/00273171.2012.715252
- Bryant, F. B., & Satorra, A. (2012). Principles and practice of scaled difference chi-square testing. Structural Equation Modeling, 19, 372–398. doi:10.1080/10705511.2012.687671
- Cudeck, R., & Browne, M. W. (1992). Constructing a covariance matrix that yields a specified minimizer and a specified minimum discrepancy function value. Psychometrika, 57, 357–369. doi:10.1007/BF02295424
- 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. doi:10.1037/1082-989X.1.1.16
- Fleishman, A. I. (1978). A method for simulating non-normal distributions. Psychometrika, 43, 521–532. doi:10.1007/BF02293811
- Foldnes, N., Olsson, U. H., & Foss, T. (2012). The effect of kurtosis on the power of two test statistics in covariance structure analysis. British Journal of Mathematical and Statistical Psychology, 65, 1–18. doi:10.1111/bmsp.2012.65.issue-1
- Foss, T., Jöreskog, K. G., & Olsson, U. H. (2011). Testing structural equation models: The effect of kurtosis. Computational Statistics and Data Analysis, 55, 2263–2275. doi:10.1016/j.csda.2011.01.012
- Li, L., & Bentler, P. (2006). Robust statistical tests for evaluating the hypothesis of close fit of misspecified mean and covariance structural models (UCLA Statistics Reprint #506). Los Angeles, CA: University of California.
- Muthén, L. K., & Muthén, B. O. (2012). Mplus user’s guide (7th ed.). Los Angeles, CA: Muthén & Muthén.
- Rosseel, Y. (2010). Mplus estimators: MLM and MLR [PowerPoint slides]. Retrieved from http://users.ugent.be/~yrosseel/lavaan/utrecht2010.pdf
- SAS Institute. (2015). SAS/STAT 14.1 user’s guide. Cary, NC: Author.
- Satorra, A., & Bentler, P. M. (1994). Corrections to test statistics and standard errors in covariance structure analysis. In A. Von Eye & C. C. Clogg (Eds.), Latent variables analysis: Applications for developmental research (pp. 399–419). Thousand Oaks, CA: Sage.
- Steiger, J. H. (1990). Structural model evaluation and modification: An interval estimation approach. Multivariate Behavioral Research, 25, 173–180. doi:10.1207/s15327906mbr2502_4
- Steiger, J. H., & Lind, J. C. (1980, May). Statistically based tests for the number of common factors. Paper presented at the annual meeting of the Psychometric Society, Iowa City, IA.
- Tucker, L. R., & Lewis, C. (1973). A reliability coefficient for maximum likelihood factor analysis. Psychometrika, 38, 1–10. doi:10.1007/BF02291170
- Vale, C. D., & Maurelli, V. A. (1983). Simulating multivariate nonnormal distributions. Psychometrika, 48, 465–471. doi:10.1007/BF02293687