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oai:riuma.uma.es:10630/281602026-02-03T11:16:06Zcom_10630_2254col_10630_37953

Blanca-Mena, María José Arnau, Jaume García-Castro, F. Javier Alarcón-Postigo, Rafael Bono Cabré, Roser 2023-11-28T12:57:01Z 2023-11-28T12:57:01Z 2022-09-25 Blanca, M. J., Arnau, J., García-Castro, F. J., Alarcón, R., & Bono, R. (2023). Non-normal data in repeated measures ANOVA: Impact on Type I error and power. Psicothema, 35(1), 21-29. https://doi.org/10.7334/psicothema2022.292 https://hdl.handle.net/10630/28160 10.7334/psicothema2022.292 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. eng open access Psicología - Investigación Non-normal Data in Repeated Measures ANOVA: Impact on Type I Error and Power. journal article