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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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Cites
GPOWER: A general power analysis program
Erdfelder, E., Faul, F., & Buchner, A. (1996) · Behavior Research Methods, Instruments, & Computers · doi:10.3758/bf03203630 · Cited by 4,864
Edgington, E., Edgington, E., & Onghena, P. (2007) · doi:10.1201/9781420011814 · Cited by 2,030
Glass, G. V., Peckham, P. D., & Sanders, J. R. (1972) · Review of Educational Research · doi:10.3102/00346543042003237 · Cited by 1,826
Inference by Eye: Confidence Intervals and How to Read Pictures of Data.
Cumming, G., & Finch, S. (2005) · American Psychologist · doi:10.1037/0003-066x.60.2.170 · Cited by 1,449
Research works printed but not linked: 10
- Gough B., 2003, GNU Scientific Library Reference Manual
- 10.3102/10769986017004315 · DOI · Paper page
- 10.1037/1082-989X.13.2.110 · DOI
- 10.3102/00346543068003350 · DOI · Paper page
- Lix L. M., 1996, Review of Educational Research, 66, 579
- 10.1037/0033-2909.105.1.156 · DOI · Paper page
- 10.1145/63039.63042 · DOI · Paper page
- 10.1177/0013164490504009 · DOI
- 10.1080/02664769723576 · DOI
- 10.1080/00220979809598344 · DOI
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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
Duchowski, A. T., Krejtz, K., Krejtz, I., Biele, C., Niedzielska, A., Kiefer, P., Raubal, M., & Giannopoulos, I. (2018) · Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3173856 · Cited by 228
Weber, K. E., Gold, B., Prilop, C. N., & Kleinknecht, M. (2018) · Teaching and Teacher Education · doi:10.1016/j.tate.2018.08.008 · Cited by 165
Dzhambov, A. M., Hartig, T., Tilov, B., Atanasova, V., Makakova, D. R., & Dimitrova, D. D. (2019) · Environmental Research · doi:10.1016/j.envres.2019.108708 · Cited by 144
Wingenbach, T. S. H., Ashwin, C., & Brosnan, M. (2016) · PLOS ONE · doi:10.1371/journal.pone.0147112 · Cited by 113
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Robic, S., Sonié, S., Fonlupt, P., Henaff, M.-A., Touil, N., Coricelli, G., Mattout, J., & Schmitz, C. (2015) · Journal of Autism and Developmental Disorders · doi:10.1007/s10803-014-2311-7 · Cited by 88
Alessandri, G., Zuffianò, A., & Perinelli, E. (2017) · Frontiers in Psychology · doi:10.3389/fpsyg.2017.00223 · Cited by 88
Porras-Garcia, B., Ferrer-Garcia, M., Serrano-Troncoso, E., Carulla-Roig, M., Soto-Usera, P., Miquel-Nabau, H., Fernández-Del castillo Olivares, L., Marnet-Fiol, R., de la Montaña Santos-Carrasco, I., Borszewski, B., et al. (2021) · Journal of Clinical Medicine · doi:10.3390/jcm10040682 · Cited by 81
Chambers, S. K., Foley, E., Galt, E., Ferguson, M., & Clutton, S. (2012) · Supportive Care in Cancer · doi:10.1007/s00520-011-1195-8 · Cited by 74
Dzhambov, A. M. (2018) · Archives of Industrial Hygiene and Toxicology · doi:10.2478/aiht-2018-69-3166 · Cited by 74
Trombka, M., Demarzo, M., Campos, D., Antonio, S. B., Cicuto, K., Walcher, A. L., García-Campayo, J., Schuman-Olivier, Z., & Rocha, N. S. (2021) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2021.624876 · Cited by 69
Porras-Garcia, B., Ferrer-Garcia, M., Serrano-Troncoso, E., Carulla-Roig, M., Soto-Usera, P., Miquel-Nabau, H., Shojaeian, N., de la Montaña Santos-Carrasco, I., Borszewski, B., Díaz-Marsá, M., et al. (2020) · Journal of Clinical Medicine · doi:10.3390/jcm9103210 · Cited by 35
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“We relied upon the robustness of the t-test to departures from normality when analyzing the data of our experiments [48,55,57].”
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Schild, A., Cool, J., Barbu, M.-C., & Smith, G. D. (2021) · Wood Material Science & Engineering · doi:10.1080/17480272.2019.1652682 · Cited by 18
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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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