Effect of Potential Confounding Factors on Fit Indices and Parameter Estimates for True and Misspecified SEM Models  

Oleh:

Lin, Wang ; Xitao, Fan

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: Educational and Psychological Measurement vol. 58 no. 5 (1998), page 701-735.
Topik: parameters; mathematical models; psychology; statistical models
Fulltext: 701.pdf (2.94MB)

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: EE30.2 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

The present monte carlo study assessed the effects of three potential confounding factors on structural equation modleing (SEM) fit indices and parameter estimates : data non normality, estimation method, and sampel size. The major findings were that : a. relatively mild data non normaltiy has little effect on SEM fit indices and parameter estimates b. under misspecifiec models, estimation method (maximum likelihood [ML] vs. generalized least squares [GLSI] has considerable influence on SEM incremental fit indices and c. some fit indices are more succeptible to the influence of sample size. Previous findings in the literature that SEM fit indices were consistent under different estimation methods may need to be revisited, because the finding was primarily based on monte carlo simulations involving true SEM models. Because, SEM researchers rarely are certain whether they have correctly specified their models, it is critical that simulation studies are conducted in the presence of model misspecification as in the present study.

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