Power analysis in covariance structure modeling using GFI and AGFI

被引:350
作者
MacCallum, RC
Hong, S
机构
[1] Department of Psychology, Ohio State University, 1885 Neil Avenue, Columbus
关键词
D O I
10.1207/s15327906mbr3202_5
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
Extending earlier work by MacCallum, Browne, and Sugawara (1996), procedures are shown for conducting power analysis of tests of overall fit of covariance structure models when null and alternative levels of model fit are specified in terms of values of the GFI or AGFI fit indexes. Results show that for GFI-based power analyses, holding null and alternative values of GFI fixed, power decreases as degrees of freedom increase, which is a counter-intuitive and undesirable phenomenon indicating lower power for detecting false null hypotheses about simpler models. For AGFI-based analyses, power increases as degrees of freedom increase. However, for both indexes it is shown that it is problematic to establish consistently appropriate values for null and alternative hypotheses about model fit. Because of these problems, it is recommended that the RMSEA index is preferable as a basis for power analysis and model evaluation.
引用
收藏
页码:193 / 210
页数:18
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