When ANOVA Isn't Ideal: Analyzing Ordinal Data from Practical Work in Biology

Calver, Michael C.; Fletcher, Douglas

doi:10.1525/abt.2020.82.5.289

Cited by 6 publications

(5 citation statements)

References 6 publications

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“…In terms of strict definition, the data obtained from SD method has doubts about whether it is suitable for parametric analysis [17]. We additionally conducted the Mann-Whitney U test of nonparametric tests.…”

Section: Results Of Mann-whitney U Testmentioning

confidence: 99%

Gender Differences in Footwear Visual Perception

Cheng

Lee

2021

IJAE

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This study explores the differences in visual perception of footwear based on gender by conducting semantic differential evaluations and classification tasks. Cluster and factor analyses were performed to analyze the similarities and factors in footwear styles, respectively. The cluster analysis results indicated that leather shoes and sneakers were the major styles clearly distinguished by the participants. Fancy leather shoes, described as "fancy and exaggerated" by men, and as "elegant and refined" by women, was the consensus style. Women emphasized on fashion and were more likely to perceive footwear integral to their outfit, whereas men valued the functionality of footwear and treated it as a tool. The factor analysis on footwear styles extracted four male factors: evaluation, attention, feature, and protection; and four female factors for: evaluation, charm, volume, and coverage. Women were more sensitive to the volume and weight of footwear, and were also more likely to sense its coverage.

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Section: Results Of Mann-whitney U Testmentioning

confidence: 99%

Gender Differences in Footwear Visual Perception

Cheng

Lee

2021

IJAE

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“…Parametric tests assume that the scale of the data is a ratio or an interval [17]. However, ordinal data lack the quantitative properties assumed by parametric tests and cannot be interpreted quantitatively; differences between them cannot be compared as numerical data [19]. Treating ordinal data as if they were metric data can lead to systematic errors, including type I errors and type II errors [20].…”

Section: Discussionmentioning

confidence: 99%

Parametric analysis and normality assumption in phase 3 trials with small sample sizes

Kim,

Park

et al. 2024

Preprint

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Background In studies with small sample sizes, the use of nonparametric methods is generally recommended for statistical analysis. However, various studies continue to employ parametric analysis without verifying the assumption of a normal distribution. Objectives To assess the current utilization of parametric and nonparametric methods for primary outcomes, as well as the reporting of normality assumptions, in phase 3 clinical trials with small sample sizes. Methods All phase 3 trials registered on ClinicalTrials.gov until September 12, 2023, were collected. After undergoing a two-step selection process, only publications with a sample size per group of less than 30, involving two or more groups, and specifying the statistical methods used to compare the means or medians of primary outcomes between groups were selected. Statistical methods were categorized as nonparametric, parametric, and either parametric or nonparametric. The reporting of normality assumptions was also evaluated. Results A total of 317 studies were assessed in this study. Among these studies, 164 (51.7%) studies conducted parametric analysis, and 111 (35.0%) studies employed nonparametric analysis; however, 42 (13.2%) studies conducted parametric or nonparametric analysis without specifying which method was used. In addition, 63.1% of the total studies did not report normality assumptions. Specifically, within the subset of studies with parametric analysis, 70.1% of studies did not report normality assumptions. Conclusions This research demonstrated that most studies with small sample sizes employed parametric analysis without reporting normality assumptions. The findings emphasize the need for increased awareness of and compliance with statistical principles in the analysis of phase 3 clinical trials with limited sample sizes.

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“…The technique ✧✮✧✠✧★✩✰ ✬✭✮✯✧✰ ☎★✫✩ ✲✫✄✄✧✲✩✝✶ ☎✡✭✄✩✴✭✮✮☎ ✲✫✄✄✧✲✩✝ ✭★✪ ☎✲✫✄✄✧✲✩✝ ✗✧✄✧ ✲✫✪✧✪ ✭✰ 1, 2 and 3, respectively. The dependent variables concerning the technique elements were of ordinal scale without specific interval size, nor normal distribution of their scores as assessed by Shapiro-✴✮✄✱✰ ✩✧✰✩ ✁✡ ✁ ✂✝✁✄✂ ✄✳✧✄✧✒✫✄✧✶ ✩✳✧☎ ✲✫✯✮✪ ★✫✩ ✖✧ ✡✄✫cessed as continuous variables within the parametric framework (Lalla, 2017) and a rank-based non-parametric approach was required (Calver & Fletcher, 2020;McCrum-Gardner, 2008;McDonald, 2009). A Wilcoxon signed-rank test was used to compare pre-and post-measurements for each group.…”

Section: Discussionmentioning

confidence: 99%

The Impact of Blended Learning and Direct Video Feedback on Primary School Students’ Three-Step Ball Throwing Technique

Kyriakidis

Panoutsakopoulos

Paraschos

et al. 2022

International Journal of Computer Science in Sport

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The purpose of this study was to evaluate three distinct methods of teaching the three-step ball throw simulating the javelin throw technique to primary school students. The sample consisted of 131 primary school students of 5th and 6th grade (Mage = 11.4, SD = 0.47 years) randomly divided into three groups. The control group (CON) received typical instruction, the first experimental group (EXP) followed a blended learning intervention which included an interactive learning activity software and the second experimental group (EXPVF) followed the same blended learning method with an additional direct video feedback system. A pre/post-test design was implemented to evaluate students’ technique, using as criteria five selected technique elements of the three-step ball throw. Wilcoxon signed-rank test analysis showed that all three groups performed significantly better after the intervention in all five criteria. However, Kruskal-Wallis H test analysis with post-hoc test revealed that the results for EXPVF group were significantly better than the other two groups in all elements, while the EXP group showed significantly better results in three of the five elements compared with the CON group. In conclusion, students appeared to benefit more in their three-step ball throw technique through blended learning and direct video feedback.

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When ANOVA Isn't Ideal: Analyzing Ordinal Data from Practical Work in Biology

Cited by 6 publications

References 6 publications

Gender Differences in Footwear Visual Perception

Gender Differences in Footwear Visual Perception

Parametric analysis and normality assumption in phase 3 trials with small sample sizes

The Impact of Blended Learning and Direct Video Feedback on Primary School Students’ Three-Step Ball Throwing Technique

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