The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Is It Really Robust?: Reinvestigating the Robustness of ANOVA Against Violations of the Normal Distribution Assumption
Abstract
Empirical evidence to the robustness of the analysis of variance (ANOVA) concerning violation of the normality assumption is presented by means of Monte Carlo methods. High-quality samples underlying normally, rectangularly, and exponentially distributed basic populations are created by drawing samples which consist of random numbers from respective generators, checking their goodness of fit, and allowing only the best 10% to take part in the investigation. A one-way fixed-effect design with three groups of 25 values each is chosen. Effect-sizes are implemented in the samples and varied over a broad range. Comparing the outcomes of the ANOVA calculations for the different types of distributions, gives reason to regard the ANOVA as robust. Both, the empirical type I error α and the empirical type II error β remain constant under violation. Moreover, regression analysis identifies the factor “type of distribution” as not significant in explanation of the ANOVA results.
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Cited by
Non-normal data: Is ANOVA still a valid option?
Blanca, M., Alarcón, R., Arnau, J., Bono, R., & Bendayan, R. (2017) · Psicothema · doi:10.7334/psicothema2016.383 · Cited by 1,624
Kraus, J., Scholz, D., Stiegemeier, D., & Baumann, M. (2020) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1177/0018720819853686 · Cited by 276
“MF– = no malfunction; LT = low transparency; MF+ = with malfunction; HT = high transparency. violation (Schmider et al., 2010) and thus are reported with corrected statistics. results”
Bachstetter, A. D., Van Eldik, L. J., Schmitt, F. A., Neltner, J. H., Ighodaro, E. T., Webster, S. J., Patel, E., Abner, E. L., Kryscio, R. J., & Nelson, P. T. (2015) · Acta Neuropathologica Communications · doi:10.1186/s40478-015-0209-z · Cited by 258
Sun, S., Goldberg, S. B., Lin, D., Qiao, S., & Operario, D. (2021) · Globalization and Health · doi:10.1186/s12992-021-00663-x · Cited by 221
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Metadata
- Authors
- Schmider, E., Ziegler, M., Danay, E., Beyer, L., & Bühner, M.
- Venue
- Methodology
- Publisher
- Hogrefe Publishing Group
- Published
- 2010-01
- Volume
- 6
- Issue
- 4
- Pages
- 147-151
- ISSN
- 1614-1881 (ISSN-L)
- 1614-2241
- OpenAlex
- W2104806923
- Cited by
- 1,290
- References
- 4
- FWCI
- 11.58
- Type
- journal article
- Language
- en
- Open access
- closed
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- Schmider, E., Ziegler, M., Danay, E., Beyer, L., & Bühner, M. (2010). Is It Really Robust?: Reinvestigating the Robustness of ANOVA Against Violations of the Normal Distribution Assumption. Methodology. https://doi.org/10.1027/1614-2241/a000016
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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