A Review of Kruskal-Wallis Test Applications in Scientific Research

Samsul Arifin

Abstract


The Kruskal-Wallis test is a widely applied non-parametric statistical method for comparing multiple independent groups. Despite its frequent usage in various disciplines, limited bibliometric studies have been conducted to examine research trends, influential authors, and collaborations related to this test. This study aims to analyze the bibliometric landscape of Kruskal-Wallis test research using Scopus as the primary database and Scopus-AI as a secondary source for validation. The dataset consists of journal articles published in English between 2021 and 2025, retrieved using the search query TITLE-ABS-KEY (kruskal AND wallis AND test) with additional filters for document type and language. The bibliometric analysis was conducted using VOSviewer, focusing on co-authorship networks, keyword co-occurrence, citation analysis, bibliographic coupling, and co-citation patterns. The results reveal key research clusters, emerging trends, and highly influential publications. The keyword analysis highlights the interdisciplinary applications of the Kruskal-Wallis test, particularly in biomedical research, machine learning, and social sciences. Citation network analysis identifies high-impact authors and journals, while co-authorship mapping illustrates significant global research collaborations. By integrating Scopus-AI as a second opinion, this study strengthens the validity of findings and uncovers additional insights that may not be evident from conventional bibliometric searches. The results provide a comprehensive overview of research developments related to the Kruskal-Wallis test, offering valuable guidance for future studies and interdisciplinary applications.

Keywords


Bibliometric Analysis, Kruskal-Wallis Test, VOSviewer, Research Trends, Scopus, Scopus-AI

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References


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DOI: http://dx.doi.org/10.33021/jafrm.v4i2.6301

DOI (PDF): http://dx.doi.org/10.33021/jafrm.v4i2.6301.g2413

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