A Comparison of the Bootstrap-F, Improved General Approximation, and Brown-Forsythe Multivariate Approaches in a Mixed Repeated Measures Design
| Autor: | F. Javier Herrero Diez, Marcelino Cuesta Izquierdo, Guillermo Vallejo Seco, M. Paula Fernández García |
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| Rok vydání: | 2006 |
| Předmět: |
Multivariate statistics
Applied Mathematics media_common.quotation_subject 05 social sciences 050401 social sciences methods 050301 education Repeated measures design Brown–Forsythe test Education 0504 sociology Sample size determination Skewness Statistics Developmental and Educational Psychology Main effect 0503 education Applied Psychology Normality Mathematics media_common Type I and type II errors |
| Zdroj: | Educational and Psychological Measurement. 66:35-62 |
| ISSN: | 1552-3888 0013-1644 |
| Popis: | The authors compare the operating characteristics of the bootstrap- F approach, a direct extension of the work 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. |
| Databáze: | OpenAIRE |
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