| Título |
Non-normal Data in Repeated Measures ANOVA: Impact on Type I Error and Power |
| Autores |
Blanca, Maria J. , Arnau, Jaume , GARCÍA DE CASTRO, FERNANDO JAVIER, Alarcon, Rafael , Bono, Roser |
| Publicación externa |
No |
| Medio |
Psicothema |
| Alcance |
Article |
| Naturaleza |
Científica |
| Cuartil JCR |
2 |
| Cuartil SJR |
1 |
| Fecha de publicacion |
01/01/2023 |
| ISI |
000925875000002 |
| DOI |
10.7334/psicothema2022.292 |
| Abstract |
Background: Repeated measures designs are commonly used in health and social sciences research. Although there are other, more advanced, statistical analyses, the F-statistic of repeated measures analysis of variance (RM-ANOVA) remains the most widely used procedure for analyzing differences in means. The impact of the violation of normality has been extensively studied for between-subjects ANOVA, but this is not the case for RM-ANOVA. Therefore, studies that extensively and systematically analyze the robustness of RM-ANOVA under the violation of normality are needed. This paper reports the results of two simulation studies aimed at analyzing the Type I error and power of RM-ANOVA when the normality assumption is violated but sphericity is fulfilled. Method: Study 1 considered 20 distributions, both known and unknown, and we manipulated the number of repeated measures (3, 4, 6, and 8) and sample size (from 10 to 300). Study 2 involved unequal distributions in each repeated measure. The distributions analyzed represent slight, moderate, and severe deviation from normality. Results: Overall, the results show that the Type I error and power of the F-statistic are not altered by the violation of normality. Conclusions: RM-ANOVA is generally robust to non-normality when the sphericity assumption is met. |
| Palabras clave |
Violation of normality; Within-subject design; Robustness; Power; ANOVA |
| Miembros de la Universidad Loyola |
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