Journal
BEHAVIOR RESEARCH METHODS
Volume 43, Issue 4, Pages 1066-1074Publisher
SPRINGER
DOI: 10.3758/s13428-011-0115-7
Keywords
Maximum likelihood test statistic; Violation of multivariate normality; Multilevel structural equation modeling
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A simulation study investigated the effects of skewness and kurtosis on level-specific maximum likelihood (ML) test statistics based on normal theory in multilevel structural equation models. The levels of skewness and kurtosis at each level were manipulated in multilevel data, and the effects of skewness and kurtosis on level-specific ML test statistics were examined. When the assumption of multivariate normality was violated, the level-specific ML test statistics were inflated, resulting in Type I error rates that were higher than the nominal level for the correctly specified model. Q-Q plots of the test statistics against a theoretical chi-square distribution showed that skewness led to a thicker upper tail and kurtosis led to a longer upper tail of the observed distribution of the level-specific ML test statistic for the correctly specified model.
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