On Examining the Underlying Normal Variable Assumption in Latent Variable Models With Categorical Indicators

被引:6
|
作者
Raykov, Tenko [1 ]
Marcoulides, George A. [2 ]
机构
[1] Michigan State Univ, E Lansing, MI 48824 USA
[2] Univ Calif Santa Barbara, Santa Barbara, CA 93106 USA
关键词
categorical indicator; latent variable modeling; underlying latent normal variable assumption; FALSE DISCOVERY RATE;
D O I
10.1080/10705511.2014.937846
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
A multiple testing approach is outlined that can be used to examine the assumption of underlying normal variables in latent variable models with categorical indicators. The method is based on an application of the increasingly popular Benjamini-Hochberg multiple testing procedure, and is readily applicable with widely circulated software. The discussed method is especially useful for ascertaining this assumption that is very often made in research based on structural equation modeling using models containing discrete outcomes. The described approach is illustrated with numerical data.
引用
收藏
页码:581 / 587
页数:7
相关论文
共 50 条