Mean and Mean-and-Variance Corrections With Big Data. Issue 2 (4th March 2018)

• Record Type: Journal Article
• Authors: Yuan, Ke-Hai; Jiang, Ge; Yang, Miao
• Is Part Of: Structural equation modeling. Volume 25:Issue 2(2018)
• Page Start: 214
• Page End: 229
• Publication Date: 2018-03-04
• Subjects: adjusted statistic -- big data -- chi-square distribution -- relative multivariate kurtosis -- rescaled statistic; Multivariate analysis -- Periodicals; Social sciences -- Statistical methods -- Periodicals; 519.535
• DOI: 10.1080/10705511.2017.1379012
• Languages: English
• ISSNs: 1070-5511

Abstract

Abstract : Mean and mean-and-variance corrections are the 2 major principles to develop test statistics with violation of conditions. In structural equation modeling (SEM), mean-rescaled and mean-and-variance-adjusted test statistics have been recommended under different contexts. However, recent studies indicated that their Type I error rates vary from 0% to 100% as the number of variables p increases. Can we still trust the 2 principles and what alternative rules can be used to develop test statistics for SEM with "big data"? This article addresses the issues by a large-scale Monte Carlo study. Results indicate that empirical means and standard deviations of each statistic can differ from their expected values many times in standardized units when p is large. Thus, the problems in Type I error control with the 2 statistics are because they do not possess the properties to which they are entitled, not because of the wrongdoing of the mean and mean-and-variance corrections. However, the 2 principles need to be implemented using small sample methodology instead of asymptotics. Results also indicate that distributions other than chi-square might better describe the behavior of test statistics in SEM with big data.