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The normal distribution is a widely used probability distribution to describe samples, populations, and sampling distributions of statistics.
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In most of the inferential statistical analyses, one of the important assumptions is the assumption of multivariate normality. Multivariate normality is the assumption that each variable and all linear combinations of the variables are normally distributed (Tabachnick & Fidell, 2007, p. 78). When this assumption is violated, results derived from statistical analysis may not be reliable and valid. For example, multivariate normality of ordinal variables is a condition for the testing of measurement invariance or construct equivalence using the maximum likelihood estimation method of multiple-group confirmatory factor analysis (Byrne, 1998: Koh & Zumbo, 2008). Tests for checking multivariate normality are overly sensitive, and hence, researchers are encouraged to check for...
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References
Byrne, B. M. (1998). Structural equation modeling with Lisrel, Prelis, and Simplis: Basic concepts, applications, and programming. Mahwah, NJ: Erlbaum.
Chambers, J. M., Cleveland, W. S., Kleiner, B., & Tukey, P. A. (1983). Graphical methods for data analysis. Pacific Grove, CA: Wadsworth Brooks/Cole.
Koh, K., & Zumbo, B. (2008). Multi-group confirmatory factor analysis for testing measurement invariance in mixed item format data. Journal of Modern Applied Statistical Methods, 7(2), 471–477.
Tabachnick, B. G., & Fidell, L. S. (2007). Using multivariate statistics (5th ed.). Boston: Pearson Education.
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© 2014 Springer Science+Business Media Dordrecht
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Koh, K. (2014). Univariate Normal Distribution. In: Michalos, A.C. (eds) Encyclopedia of Quality of Life and Well-Being Research. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-0753-5_3109
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