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Identification of Emotion Parameters in Music to Modulate Human Affective States
Published in Wellington Pinheiro dos Santos, Juliana Carneiro Gomes, Valter Augusto de Freitas Barbosa, Swarm Intelligence Trends and Applications, 2023
Maíra A. Santana, Ingrid B. Nunes, Andressa L.Q. Ribeiro, Flávio S. Fonseca, Arianne S. Torcate, Amanda Suarez, Vanessa Marques, Nathália Córdula, Juliana C. Gomes, Giselle M.M. Moreno, Wellington P. Santos
Kendall’s coefficient of agreement can be understood as a measure of agreement among m judges who rank a set of n entities (Field, 2005). This metric quantitatively assesses the coherence of the collective classification (S) provided by the (Franceschini and Maisano, 2021) models. The main intention is that Kendall’s W presents results ranging from 0 (when there is no agreement) to 1 (when there is complete agreement).
Identifying the Enablers and Inhibitors of Organizational Learning in the Context of IT Governance: An Exploratory Delphi Study
Published in Information Systems Management, 2022
Koen De Maere, Steven De Haes, Michael von Kutzchenbach, Tim Huygh
In order to obtain the degree of consensus among the experts, Kendall’s coefficient of concordance (i.e. Kendall’s W) was calculated.8 Due to its ability to provide a unique and easily understandable solution, Kendall’s W is preferred over other methods for assessing agreement among panelists in a Delphi study (Schmidt, 1997). Kendall’s W can range from 0 to 1, with ‘1ʹ indicating that the participants apply exactly the same standard in judging the importance of the issues under investigation (i.e. perfect consensus) (Von der Gracht, 2012). Commonly accepted rules of thumb related to the values of Kendall’s W have been identified. More specifically, Kendall’s W > .7 indicates strong consensus, .5 ≤ Kendall’s W ≤ .7 indicates moderate consensus, and Kendall’s W < .5 indicates weak consensus (Schmidt, 1997).
Influencing factors of synchronization in manufacturing systems
Published in International Journal of Production Research, 2018
Stanislav Chankov, Marc-Thorsten Hütt, Julia Bendul
In order to further improve our understanding of the relation between workload level and synchronisation emergence, we perform a repeated-measures comparison analysis for four selected workload levels (30, 50, 70 and 90%) across the six basic networks and all CoV values. Thus, there are 90 measurements per workload level (six networks and 15 CoV values). Synchronisation measurements are not normally distributed. Thus, we can conduct the Friedman’s ANOVA test, which is the non-parametric version of repeated-measure ANOVA. It is applied for testing differences between several conditions (in our case, different workload levels) and the same participants (in our case, same network-CoV experiments) (Field 2013). We use Kendall’s Coefficient of Concordance (W) as a measure of the test’s effect size (Kinnear and Gray 2010). Kendall’s W can take a value between 0 and 1 where values between 0 and 0.3 indicate a low, between 0.3 and 0.5 a medium, and larger than 0.5 a strong effect.
Minimizing Low Back Cumulative Loading during Design of Manual Material Handling Tasks: An Optimization Approach
Published in IISE Transactions on Occupational Ergonomics and Human Factors, 2021
Sivan Almosnino, Jessica Cappelletto
For each algorithm optimization routine, we describe our results using a percentage agreement of the box mass that were allocated into the specific storage locations, for each of the four relative frequency distributions and four LBCL integration methods. That is, the percent agreement expresses the degree to which each integration method provided a unique solution, given the tested conditions. We further assessed agreement across integration methods using Kendall’s coefficient of concordance (W), with alpha preset at 0.05. Kendall’s W is a non-parametric statistic that evaluates concurrence between raters, with 1.0 indicating perfect agreement, and 0 indicating no agreement between raters. Here, the four integration methods were treated as an independent rater.