Format

Send to

Choose Destination
Behav Res Methods. 2011 Dec;43(4):1066-74. doi: 10.3758/s13428-011-0115-7.

Effects of skewness and kurtosis on normal-theory based maximum likelihood test statistic in multilevel structural equation modeling.

Author information

1
Department of Psychology, Boston College, Chestnut Hill, MA 02467, USA. ehri.ryu.1@bc.edu

Abstract

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.

PMID:
21671139
DOI:
10.3758/s13428-011-0115-7
[Indexed for MEDLINE]

Supplemental Content

Full text links

Icon for Springer
Loading ...
Support Center