Improved Goodness of Fit Procedures for Structural Equation Models

被引:1
作者
Foldnes, Njal [1 ]
Moss, Jonas [2 ]
Gronneberg, Steffen [2 ]
机构
[1] Univ Stavanger, Norwegian Reading Ctr, Stavanger, Norway
[2] BI Norwegian Business Sch, Oslo, Norway
关键词
Bootstrap; covariance structure analysis; factor model; goodness-of-fit test; non-normality; weighted sum of chi-squares; TEST STATISTICS; NONNORMAL DATA; R PACKAGE; PERFORMANCE; SKEWNESS; KURTOSIS; ML;
D O I
10.1080/10705511.2024.2372028
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
We propose new ways of robustifying goodness-of-fit tests for structural equation modeling under non-normality. These test statistics have limit distributions characterized by eigenvalues whose estimates are highly unstable and biased in known directions. To take this into account, we design model-based trend predictions to approximate the population eigenvalues. We evaluate the new procedures in a large-scale simulation study with three confirmatory factor models of varying size (10, 20, or 40 manifest variables) and six non-normal data conditions. The eigenvalues in each simulated dataset are available in a database. Some of the new procedures markedly outperform presently available methods. We demonstrate how the new tests are calculated with a new R package and provide practical recommendations.
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页码:1 / 13
页数:13
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