The DOI matches its Crossref record; 93 of the 95 references it cites were found in a registry or an index.
Consequences of Failure to Meet Assumptions Underlying the Fixed Effects Analyses of Variance and Covariance
Abstract
The effects of violating the assumptions underlying the fixed-effects analyses of variance (ANOVA) and covariance (ANCOVA) on Type-I and Type-II error rates have been of great concern to researchers and statisticians. The major effects of violation of assumptions are now well known, after nearly four decades of research. Early summaries and reviews by Hey (1938), Garret and Zubin (1943), Grant (1944), and Gourlay (1955) and more recent reviews by Bradley (1963), Atiqullah (1967), Elashoff (1969) and Scheffe (1959, Ch. 10) can be extended and updated with recent research which provides closure to an area of active inquiry. (For a review of the effects of violation of the assumptions of the randomeffects ANOVA-a subject not reviewed here-the reader is directed to Scheffe, 1959, pp. 334-337 and Box & Anderson, 1962.) Asking whether ANOVA and ANCOVA assumptions are satisfied is not idle curiosity. The assumptions of most mathematical models are always false to a greater or lesser extent. The relevant question is not whether ANOVA assumptions are met exactly, but rather whether the plausible violations of the assumptions have serious consequences on the validity of probability statements based on the standard assumptions. Applied statistics in education and the social sciences experienced a largely unnecessary hegira to non-parametric statistics during the 1950s. Increasingly during the 1950s and early 1960s the fixed-effects, normal theory ANOVA was replaced by such comparable no…
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“We focus on fairly drastic non-normality because only little bias can be expected from minor violations (Bishara & Hittner, 2012; Glass et al., 1972; Hack, 1958; Puth et al., 2014).”
From Violating the normality assumption may be the lesser of two evils (2021) · cites this paper as Glass et al., 1972
In the citing paper
“For instance, when applying a t test that assumes equal variances in both groups to data that come from substantially different variances (e.g., σ12/ σ22 = 0.1), then high rates of type I errors (e.g., 23%) may be obtained in a situation where sample sizes are unbalanced (N1 = 15, N2 = 5), namely when the small sample comes from the more variable group (Glass et al., 1972; Miller, 1986).”
From Violating the normality assumption may be the lesser of two evils (2021) · cites this paper as Glass et al., 1972
In the citing paper
“Violating this assumption may result in more notable increases of type I errors (compared to what we examined here) at least when the violations are drastic.”
From Violating the normality assumption may be the lesser of two evils (2021) · cites this paper as Glass et al., 1972
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“The F-statistic of an ANOVA can be robust to violations of assumptions [46].”
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“This is because the false- positive rate is affected little despite violations of normality, even with modest sample sizes (Ghasemi & Zahediasl, 2012; Glass et al., 1972; Harwell et al., 1992; Lix et al., 1996).”
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“Over each plot, we also represented the individual performances of the students 5 Note that although our data were not normally distributed with homogeneous variance, both t -test and ANOVA are robust even when distributions of the data violate the distribution assumed by the test [36–38].”
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“Although some writers claim standard tests are robust to departures from Normality, see for instance Clinch & Keselman (1982) and Tan (1982), others have opposing views; see Bradley (1978), Glass et al (1972), Wilcox (1995) and Wilcox (1998).”
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“As the violation of this assumption has a high effect on the Type I error (Glass et al., 1972), the obtained p -values need further clarification.”
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“We proceeded with a parametric analysis of RCH score because parametric tests have been shown to be largely robust to deviations from normality (Glass et al., 1972) and datasets with a large number of observations can lead to significant deviations from normality as a result of the increased power in these datasets to…”
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“the model that would otherwise be excluded if the correct P-values were available.(38) The plot of the reduced model fitted values versus the standardized residuals for the response variable (Blue destroyed) demonstrates increasing variance in the standardized residual as the fitted values of Blue destroyed increase (…”
BackgroundMethodEffects of hypotension and hypertension on source memory and working memory
Cansino, S., Torres-Trejo, F., Estrada-Manilla, C., Mercado-Canales, A., Medina-Velázquez, D., Esquivel-Garcia, R., & Ruiz‐Velasco, S. (2021) · Aging & Mental Health · doi:10.1080/13607863.2021.1942435 · Cited by 8
“Because analysis of variance (ANOVA) is nearly unaffected when data moderately deviate from normality according to simulation studies (Glass et al., 1972), we employed ANOVA with data from the working memory task, which were the only relevant dependent variables that slightly deviated from…”
MethodVrettou, M., Yan, L., Nilsson, K. W., Wallén‐Mackenzie, Å., Nylander, I., & Comasco, E. (2021) · Scientific Reports · doi:10.1038/s41598-021-94739-8 · Cited by 8
Lenaerts, B., de Mey, Y., & Demont, M. (2021) · Journal of Agricultural Economics · doi:10.1111/1477-9552.12450 · Cited by 7
THE IMPACT OF THE USE OF GEOGEBRA ON STUDENT’S MATHEMATICAL REPRESENTATION SKILLS AND ATTITUDE
Rabi, F., Ma, F., Aziz, M., et al. (2021) · European Journal of Education Studies · doi:10.46827/ejes.v8i12.4007 · Cited by 7
“When finding the difference in mean value of pretest is significant or not, a normality test was performed to know whether population's distribution is normal or not (Hopkins et al., 1990; Lilliefors, H. W., 1967; Septian et al., 2020; Glass et al., 1972).”
BackgroundMethodKalfas, M., Smakowski, A., Hirsch, C. R., Simiao, F., & Chalder, T. (2022) · Behavior Therapy · doi:10.1016/j.beth.2022.01.004 · Cited by 7
Lu, J., Lu, Q., & Lu, L. (2022) · Behavioral Sciences · doi:10.3390/bs12030063 · Cited by 7
“Second, nonnormality, typically exhibited in Likert scales and behavioral measures, poses a serious problem for classical statistics [57,58].”
BackgroundSqualene-Rich Amaranth Oil Pickering Emulsions Stabilized by Native α-Lactalbumin Nanoparticles
Cuevas‐Gómez, A. P., González-Magallanes, B., Arroyo-Maya, I. J., Gutiérrez‐López, G. F., Cornejo‐Mazón, M., & Hernández‐Sánchez, H. (2022) · Foods · doi:10.3390/foods11141998 · Cited by 7
“Foods 2022, 11, x FOR PEER REVIEW 8 of 12 Statistically, it is difficult to work with small samples which come from populations with large standard deviations since, in these cases, the false positive rate is considerable when evaluating significant differences using One-way ANOVA [44].”
MethodInfluentialPropagation of measurement error in opinion dynamics models: The case of the Deffuant model
Carpentras, D., & Quayle, M. (2022) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2022.127993 · Cited by 8
Spatial Upsampling of Sparse Spherical Microphone Array Signals
Lübeck, T., Arend, J. M., & Pörschmann, C. (2023) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2023.3250841 · Cited by 7
Entrepreneurial competency in agriculture: is it a panacea for the problem of farmers' welfare?
Esubalew, A. A. (2023) · Journal of Agribusiness in Developing and Emerging Economies · doi:10.1108/jadee-01-2022-0006 · Cited by 11
Jenßen, L. (2023) · International Journal of Applied Positive Psychology · doi:10.1007/s41042-023-00120-7 · Cited by 12
“Because the mixed ANOVA is robust against violations of the assumption of normally distributed dependent variables for each combination of the within-and between-subjects factor, no consequences were drawn for the main analyses (Glass et al., 1972).”
Tolerance against exposure to solution of magnesium perchlorate in microinvertebrates
Kayastha, P., Rzymski, P., Gołdyn, B., Nagwani, A. K., Fiałkowska, E., Pajdak‐Stós, A., Sobkowiak, R., Robotnikowski, G., & Kaczmarek, Ł. (2023) · Zoological Journal of the Linnean Society · doi:10.1093/zoolinnean/zlad060 · Cited by 8
Vitta, J. P., Leeming, P., McLean, S., & Nicklin, C. (2023) · RELC Journal · doi:10.1177/00336882231195102 · Cited by 7
“Therefore, the distributional characteristics of the data collected via the instrument were also assessed, since normal distributions tend to result in satisfying the residual assumptions of the general linear model (Field, 2018; Glass et al., 1972).”
Yu, C.-C., & Lu, C. (2023) · Sustainability · doi:10.3390/su152316442 · Cited by 9
Variance stabilization for computing and comparing grand mean waveforms in MEG and EEG
Matysiak, A., Kordecki, W., Sielużycki, C., Zacharias, N., Heil, P. F., & König, R. (2013) · Psychophysiology · doi:10.1111/psyp.12047 · Cited by 7
Statistical Modeling of Hydrogeologic Data ‐ Part I: Regression and ANOVA Models
Kufs, C. T. (1992) · Groundwater Monitoring & Remediation · doi:10.1111/j.1745-6592.1992.tb00042.x · Cited by 7
“Some authors believe that it is justified (e.g., Montgomery et al. 1987, U.S. EPA 1989), and others do not (Glass et al. 1972).”
BackgroundRowe, S. S., Witmer, S. E., Cook, E. L., & daCruz, K. (2014) · Journal of Applied School Psychology · doi:10.1080/15377903.2014.938793 · Cited by 7
Intertidal habitat utilization patterns of birds in a Northeast Pacific estuary
Frazier, M. R., Lamberson, J. O., & Nelson, W. G. (2014) · Wetlands Ecology and Management · doi:10.1007/s11273-014-9346-6 · Cited by 7
“Simulations have demonstrated that the false positive rate is not strongly affected by non-normal distributions for ANOVA analyses (e.g., Glass et al. 1972; Harwell et al. 1992), however, the P values from this analysis should still be interpreted cautiously.”
BackgroundLamprecht, S. C., Tewoldemedhin, Y. T., Hardy, M., et al. (2011) · European Journal of Plant Pathology · doi:10.1007/s10658-011-9809-z · Cited by 7
“In cases where deviations from normality were due to kurtosis and not skewness, data were accepted as reliable and the results were interpreted without transformation (Glass et al. 1972).”
BackgroundAuditory Learning Using a Portable Real-Time Vocoder: Preliminary Findings
Casserly, E. D., & Pisoni, D. B. (2015) · Journal of Speech Language and Hearing Research · doi:10.1044/2015_jslhr-h-13-0216 · Cited by 7
Five-Point Likert Items: t test versus Mann-Whitney-Wilcoxon (Addendum added October 2012)
de Winter, J. F. C., & Dodou, D. (2010) · University of Massachusetts Amherst · doi:10.7275/bj1p-ts64 · Cited by 231
Metadata
- Authors
- Glass, G. V., Peckham, P. D., & Sanders, J. R.
- Affiliations
- University of Colorado System (US)
- University of Washington (US)
- Indiana University (US)
- ORCID
- Glass, Gene V
- Venue
- Review of Educational Research
- Published
- 1972-09
- Volume
- 42
- Issue
- 3
- Pages
- 237-288
- ISSN
- 0034-6543 (ISSN-L)
- 1935-1046
- OpenAlex
- W2120106329
- Semantic Scholar
- 96cd4ca99a137bf80e8c2dc5cb20ab6782df819e
- Cited by
- 1,826
- References
- 95
- Citations by year
- 2026: 91 · 2025: 69 · 2024: 55 · 2023: 75 · 2022: 72 · 2021: 98 · 2020: 85 · 2019: 96 · 2018: 137 · 2017: 100 · 2016: 106 · 2015: 74 · 2014: 65 · 2013: 70 · 2012: 69
- FWCI
- 10.37
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Review of Educational Research (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Statistical Methods and Models
- Physical Sciences › Mathematics › Statistics and Probability › Probability and Statistical Research
- Keywords
- covariance analysis, assumption violations, type I error, type II error, nonparametric statistics, Wilcoxon test, Mann-Whitney test, Kruskal-Wallis test, Friedman test, social science research
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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