Journal
STRUCTURAL EQUATION MODELING-A MULTIDISCIPLINARY JOURNAL
Volume 7, Issue 3, Pages 356-410Publisher
ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
DOI: 10.1207/S15328007SEM0703_2
Keywords
-
Ask authors/readers for more resources
Questions of whether hypothesized structure models are appropriate representations of the pattern of association among a group of variables can be addressed using a wide variety of statistical procedures. These procedures include covariance structure analysis techniques and correlation structure analysis techniques, in which covariance structure procedures are based on distribution theory for covariances, and correlation structure procedures are based on distribution theory for correlations. The present article provides an overview of standard and modified normal theory and asymptotically distribution-free covariance and correlation structure analysis techniques and also details Monte Carlo simulation results on the Type I and Type II error control as a function of structure model type, number of variables in the model, sample size, and distributional nonnormality. The present Monte Carlo simulation demonstrates clearly that the robustness and nonrobustness of structure analysis techniques vary as a function of the structure of the model and the data conditions. Implications of these results for users of structure analysis techniques are considered in the context of current software availability.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available