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Event-Related Potential
Published in Kaushik Majumdar, A Brief Survey of Quantitative EEG, 2017
Two crucial assumptions for the ANOVA are (1) each data set is distributed normally and (2) variances of all the data sets are equal to each other. Equality of variances can be tested through Bartlett’s test. Skewness and kurtosis measures are the most elementary ways to test for normality of the data. There are many other tests for normality, for example, the Lilliefors test.
Bridge deck surface distress evaluation using S-UAS acquired high-spatial resolution aerial imagery
Published in Annals of GIS, 2023
Su Zhang, Susan M. Bogus, Shirley V. Baros, Paul R. H. Neville, Hays A. Barrett, Tyler Eshelman
The Mann Whitney U Test was performed to compare the measurements in an unpaired group way. Although this test does not require normally distributed data, it requires data from each population must be an independent random sample, and the population must have equal variances. For non-normally distributed data, the Levene’s Test and Bartlett’s Test are usually used to examine variance equability. For the Levene’s Test and Bartlett’s Test, the null hypothesis is that the population variances are equal. For both length and width, the p-value is greater than 0.05 (Table 5), and therefore, the null hypothesis should be accepted, and subsequently indicating that the population variances for both length and width measurements are equal at a 5% significance level. Ultimately, the Mann Whitney U Test is appropriate for both length and width. For both length and width measurements, the p-value is greater than 0.05 (0.9699 and 0.7052, respectively), and therefore, the null hypothesis should be accepted, and therefore indicating for ground-based measurements and orthophoto-based measurements there is no significant difference in the distribution pattern at a 5% significance (Table 6).
Understanding improvisation in construction through antecedents, behaviours and consequences
Published in Construction Management and Economics, 2019
Farook R. Hamzeh, Farah Faek, Hasnaa AlHussein
Nine hypotheses involving antecedents, behaviours and consequences of improvisation are formulated. Then, several statistical analyses are conducted to test the hypotheses. An analysis of variance (ANOVA) is used whenever the authors want to show a significant difference between different data sets collected form the survey, when normality and equal variance exist. If they are significant, post-hoc multi-comparisons are performed. On the other hand, Kruskal–Wallis rank sum test is performed as a non-parametric in case normality of data is not achieved. Then, pair-wise comparisons are made using Wilcoxon rank sum test with holm as the p-value adjustment method to identify the groups that are significantly different. On one hand, the authors perform t-tests whenever they want to investigate a significant difference between two sets of the collected data, while assuming normality. However, non-parametric Mann-Whitney test is performed in case the normality assumption doesn’t apply. The function “var.test” is utilized to check for equal variances, and Bartlett test is employed to study equal variances across more than two data sets. Also, the Shapiro test is used to test whether the data is normal. Table 2 presents the null hypotheses of the tests which are explained in the following paragraphs.
Factors influencing safety performance in the construction industry of Saudi Arabia: an exploratory factor analysis
Published in International Journal of Occupational Safety and Ergonomics, 2022
Bartlett’s test of sphericity indicates whether there is a relationship among the variables [19]. It states whether the correlation matrix is significantly diverse from the identity matrix, and thus that the correlation among variables is generally significantly dissimilar from 0 [18]. For Bartlett’s test to be highly significant, the significance values must be ≤0.001 (p < 0.001) [18]. In the present research, the significance for Bartlett’s test was 0.000, indicating the acceptability of the data for factor analysis (Table 5).