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Non-Parametric Tests of Significance
Published in M. Venkataswamy Reddy, Statistical Methods in Psychiatry Research and SPSS, 2019
The Friedman test of significance is used when the researcher has the hypothesis that the k-related sample has come from different populations with respect to mean ranks. The test is applicable when the measurement of the variable are expressed as ranks.
Inferential statistics
Published in Louis Cohen, Lawrence Manion, Keith Morrison, Research Methods in Education, 2017
Louis Cohen, Lawrence Manion, Keith Morrison
For more than two related samples (e.g. the same group voting for three or more items, or the same grouping voting at three points in time), the Friedman test is applied. For example, in Tables 41.26 to 41.28 there are three variables (‘The course encouraged and stimulated your motivation and willingness to learn’; ‘The course encouraged you to take responsibility for your own learning’; and ‘The teaching and learning tasks and activities consolidate learning through application’), all of which are voted on by the same group. The frequencies are given. Is there a statistically significant difference between the groups in their voting?
Adolescents in Treatment:
Published in Shulamith Lala Ashenberg Straussner, Christine Huff Fewell, Impact of Substance Abuse on Children and Families, 2012
Gillian Leichtling, Roy M. Gabriel, Chandra K. Lewis, Kelly Jean Vander Ley
Within group differences for each study group were assessed from baseline to 6 months, 6 months to 12 months, and from baseline to 12 months. For ordinal dependent variables, the Friedman test was utilized with the Wilcoxon test for follow-up comparisons. For interval-level dependent variables repeated measures general linear modeling (GLM) was used along with paired sample t-test for follow-up comparisons. All analyses were conducted using the Statistical Package for the Social Sciences, Version 11.5 (SPSS, 11.5).
Effects of a remotely supervised motor rehabilitation program for individuals with Rett syndrome at home
Published in Disability and Rehabilitation, 2022
Alberto Romano, Gabriella Di Rosa, Adriana Tisano, Rosa Angela Fabio, Meir Lotan
Friedman's test was run to compare the RESMES average total scores and RESMES subscale scores at the three evaluation points. The Friedman test is the non-parametric alternative to the one-way ANOVA with repeated measures. It is used for data that has violated the assumptions necessary to run the one-way ANOVA with repeated measures (e.g., the dependent variable measured is ordinal). Post hoc analysis with Wilcoxon’s signed-rank tests was conducted for pairwise comparisons. The threshold for significance for the comparisons above has been assumed as α = 0.05. No correction for multiple comparisons was applied [51]. The effects size for the above reported repeated measures comparison was conducted with Kendall's W coefficient, while for the pairwise comparisons, the matched-pairs rank-biserial correlation was used [52,53]. The effect size was considered small if between 0.140 and 0.310, medium if between 0.310 and 0.610, and large if above 0.610 [54].
Functional electrical stimulation compared with ankle-foot orthosis in subacute post stroke patients with foot drop: A pilot study
Published in Assistive Technology, 2021
Naama Karniel, Eitan Raveh, Isabella Schwartz, Sigal Portnoy
Statistical analyses were performed using IBM SPSS v.25. Descriptive statistics were used to describe the study population and measured parameters. Data were tested for normality using the Shapiro–Wilk normality test. As the distribution of the variables was not normal for most variables, and due to the small sample size, nonparametric statistics were used. Descriptive statistics of the demographic data were presented as median and interquartile range (IQR). Friedman test was used to assess differences between the three evaluations in each group. If statistical significance was found, post hoc analysis using Wilcoxon tests were performed. The Mann–Whitney U test was used to test between-group difference at each time point. The effect size for Mann–Whitney U test, r, was calculated and interpreted so that a large effect is .5, a medium effect is .3, and a small effect is 0.1 (Fritz, Morris, & Richler, 2012). Statistical significance was denoted for p < 0.05. It should be noted that if the Bonferroni correction is considered for these analysis, according to its calculation for the number of spatiotemporal and SI parameters (n = 21), p < 0.0024 is the adjusted p-value for statistical significance.
Partial splenic embolization is superior to intravenous somatostatin for decreasing portal pressure in cirrhotic patients: a dynamic self-controlled cohort study
Published in Scandinavian Journal of Gastroenterology, 2020
Jiangtao Liu, Xuyang Sun, Suvranu Ganguli, Eric Paul Wehrenberg-Klee, Irun Bhan, Yiming Zhao, Li Zhao, Ke Meng, Rui Sun, Haotian Yu, Gang Sun
Our primary hypothesis for somatostatin is that it decreases WHVP and the resulting HVPG at any time points during injection compared with the baseline level. As all indices were measured repeatedly at different time points, we used the one-way repeated measures ANOVA method to identify any differences. A box plot test and Shapiro–Wilk test were used to evaluate all variables for normal distribution. Assumption of sphericity (whether the variance of the differences between groups is equal) was checked using the Mauchly’s test of sphericity. If the above assumptions were not met, the non-parametric Friedman test would have been applied. If the one-way repeated ANOVA implied that the values were statistically different at different time points, then multiple pairwise t-tests (post-hoc test) between time points would be performed. p-Values were adjusted using the Bonferroni multiple testing correction method. The distribution of percent decrease in HVPG was compared between all time points that had a significant difference compared with baseline levels by the Wilcoxon rank-sum test. A threshold of p < .05 was used to determine statistical significance. FreeR software (R Foundation for Statistical Computing, Vienna, Austria; Version 3.6.1) was used for all analyses.