The Kruskal–Wallis tests are Cochran–Mantel–Haenszel mean score tests

Rayner, J. C. W.; Livingston, Glen

doi:10.1007/s40300-020-00192-4

Cited by 10 publications

(4 citation statements)

References 4 publications

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“…When the assumptions for a normal distribution of each group with approximately equal variance in the scores are not fulfilled, the non-parametric Kruskal-Wallis test is used. The Kruskal-Wallis test is a non-parametric approach for comparing k independent samples (Sangthong 2020) and is used to test for median or mean equality (Rayner and Livingston 2020). It is essential to compare the k groups of sizes n 1 , n 2 , .…”

Section: Methodsmentioning

confidence: 99%

The Relevance of Sectoral Clustering in Corporate Debt Policy: The Case Study of Slovak Enterprises

Gajdosikova,

Valaskova,

Lazaroiu

2024

Administrative Sciences

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The processing and transformation of natural resources into completed and semi-finished products is the primary function of industry in each nation’s economy. There is no denying the significance of industry and sectoral classification of the economy, but the slow development and extension of one industry could have resulted in the advancement of other sectors that are now a part of contemporary communities. Since there are statistically significant differences between various industries, numerous authors are currently investigating the impact of the industry on the financial structure of firms, revealing the industry as a crucial determinant of corporate indebtedness. Thus, the main aim of this study is to determine the debt level of a sample of 4237 enterprises operating in the market in the period of 2018–2021 from various sectors using eight debt indicators, as well as to identify relationships between them, which may help to reveal sectors with homogeneous patterns of indebtedness (using the cluster analysis) and thus understand which sectors are the most stable and independent. The Kruskal–Wallis test is then used to determine if there are statistically significant differences between the calculated ratios related to the economic sector. Based on the results, it can be concluded that the choice of financial structure is significantly influenced by the industry. Financial performance and indebtedness indicators are quantitative statistics used to assess, monitor, and forecast company or sectoral financial health. They act as instruments for business insiders and outsiders to assess a company’s performance, particularly in comparison to competitors, and to pinpoint its strengths and weaknesses, making the outputs of this study important for all types of stakeholders.

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Section: Methodsmentioning

confidence: 99%

The Relevance of Sectoral Clustering in Corporate Debt Policy: The Case Study of Slovak Enterprises

Gajdosikova,

Valaskova,

Lazaroiu

2024

Administrative Sciences

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“…The Kruskal–Wallis test [ 32 ] was used to determine if the resulting groups differed at different levels of statistical significance (0.01, 0.001).…”

Section: Methodsmentioning

confidence: 99%

Personalized Hybrid Education Framework Based on Neuroevolution Methodologies

Yin¹

2022

Computational Intelligence and Neuroscience

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The future pedagogical systems need anthropocentric inclusive educational programs in which the goal should be adjustable according to the knowledge requirements, intelligence, and learning objective of each student. Prioritizing these needs, innovative AI methods are required to assist and ensure the making of conscious educational decisions, in terms of clear identification and categorization with high accuracy of various forms of skills and knowledge of each student. This paper proposes a neuroevolution emerging technique that combines the searchability of evolutionary computation and the learning capability of a hybrid artificial neural networks method. Specifically, the proposed growing semiorganizing neural gas (GsONG) is a practical AI methodology utilizing advanced clustering techniques to enhance the learning experience by categorizing the true abilities, skills, and needs of learners, in an inclusive differentiated learning framework. It is a neural network architecture that includes competing and cooperating neurons with an unstructured mode whereby a cooperation-competition process delimits the topological neighborhood of neurons in a grid to identify patterns for which their classes are not known. To optimize the above process, a heuristic method was used that investigates the space of an objective function by regulating the optimal topologies of neurons that form pathway segments in a semi-contemplative manner. Based on the extensive experiments and results obtained from the GsONG clustering approach, the proposed algorithm can compensate with high accuracy for difficulties in multicriteria grouping and differentiation of uncertainty structures such as in small or tiny data sets.

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mentioning

confidence: 99%