Model selection strategies and the use of association models to detect group differences  

Oleh:

Wong, Raymond Sin-Kwok

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: Sociological Methods & Research (SMR) vol. 22 no. 04 (May 1994), page 460-491.
Fulltext: WONG-460-91.pdf (2.66MB)

Ketersediaan

Perpustakaan PKPM Nomor Panggil: S28 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

Lihat Detail Induk

Isi artikel

This article discusses the use of association models to detect group differences and the choice of model selection criteria under different conditions. The performances of several commonly used model selection criteria in log-linear modeling are examined using Monte Carlo simulations. The results suggest that no single criterion can play the role of panacea in model selection. L 2/df, nor med fit index (NFI) (or 1 - L12/L02), and Akaike's information criterion (AIC) are systematically biased toward models incorporating group differences. The log-likelihood ratio test (LRT), in conjunction with the nested chi-square difference test, is useful for samples with moderate sizes. Bayesian information criterion (BIC) is most reliable among the measures tested. It consistently provides correct information about group differences in association, especially when the sample size is large. Although BIC may give equivocal results under certain conditions, the ambiguities can be mitigated by an inclusion of competing models and a cautious interpretation of any small improvement in BIC.

Opini Anda

Klik untuk menuliskan opini Anda tentang koleksi ini!

Kembali