A Comparison of the Bootstrap-F, Improved General Approximation, and Brown-Forsythe Multivariate Approaches in a Mixed Repeated Measures Design  

Oleh:

Seco, Guillermo Vallejo ; Izquierdo, Marcelino Cuesta

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: Educational and Psychological Measurement vol. 66 no. 01 (Feb. 2006), page 35-62.
Topik: multisample sphericity; theoretical critical values; bootstrap resampling; sensitivity
Fulltext: 35.pdf (152.44KB)

Isi artikel

(PKPM) The authors compare the operating characteristics of the bootstrap-F approach, a direct extension of thework of Berkovits, Hancock, and Nevitt, with Huynh’s improved general approximation (IGA) and the Brown-Forsythe (BF) multivariate approach in a mixed repeated measures design when normality and multisample sphericity assumptions do not hold. The results of the simulation show that the three approaches adequately control Type I error when data are generated from normal or slightly nonnormal distributions. However, when data are generated from distributions with moderate or severe skewness, the approaches tend to produce conservative Type I error rates, except the IGA test of the main effect, which has liberal Type I error rates in some conditions.With regard to power, it was found that the bootstrap-F approach can compete with the IGA approach but not with the BF approach: The power differences favoring the bootstrap-F approach are generally small, whereas those favoring the BF approach are substantial.

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