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Background <p>Structured datasets, such as time-series or survey-based tables, often lack intuitive visualizations that reveal rankings, interrelationships, or leadership dominance among subjects. Traditional parametric statistics fail to capture such relational patterns, especially in ordinal or categorical data. This study proposes a novel nonparametric framework to visualize leadership styles through network diagrams.</p> Methods <p>We introduced a three-tier “basket model”—cells (small baskets), columns (medium baskets), and the full table (large basket)—to transform rectangular data into weighted co-word matrices. Using publicly available 2023 HCAHPS survey data from 52 U.S. states and territories, we applied a follower-leader clustering algorithm (FLCA) implemented in R. Leadership was classified into three types: absolute, relative, and no advantage. Network visualizations were generated using Sankey-style diagrams to highlight dominance and inter-cluster relationships.</p> Results <p>The weighted approach successfully identified Nebraska as the top leader in the upper 20 states and District of Columbia as a cluster leader among the bottom 20 after data inversion. The network diagrams effectively differentiated between absolute dominance (single strong cluster), relative dominance (sub-cluster formations), and no dominance (multiple independent clusters). Compared to traditional bar charts and choropleths, the method provided deeper insights into inter-state performance dynamics.</p> Conclusion <p>This study offers an innovative method for visualizing rankings and leadership patterns in rectangular datasets. By combining a multi-level basket model with co-word network analysis and open-source R scripts, users can quickly generate interpretable, cluster-based dominance diagrams. The approach is scalable, customizable, and applicable to a variety of fields, including healthcare, education, and public policy. Future work may extend this model to dynamic visual tools and broader interdisciplinary applications.</p>

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Visualizing leadership classifications in rectangular data using a basket model and co-word network analysis: a case study of U.S. HCAHPS survey results

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月20日

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  • Tsair-Wei Chien,
  • Willy Chou

摘要

Background

Structured datasets, such as time-series or survey-based tables, often lack intuitive visualizations that reveal rankings, interrelationships, or leadership dominance among subjects. Traditional parametric statistics fail to capture such relational patterns, especially in ordinal or categorical data. This study proposes a novel nonparametric framework to visualize leadership styles through network diagrams.

Methods

We introduced a three-tier “basket model”—cells (small baskets), columns (medium baskets), and the full table (large basket)—to transform rectangular data into weighted co-word matrices. Using publicly available 2023 HCAHPS survey data from 52 U.S. states and territories, we applied a follower-leader clustering algorithm (FLCA) implemented in R. Leadership was classified into three types: absolute, relative, and no advantage. Network visualizations were generated using Sankey-style diagrams to highlight dominance and inter-cluster relationships.

Results

The weighted approach successfully identified Nebraska as the top leader in the upper 20 states and District of Columbia as a cluster leader among the bottom 20 after data inversion. The network diagrams effectively differentiated between absolute dominance (single strong cluster), relative dominance (sub-cluster formations), and no dominance (multiple independent clusters). Compared to traditional bar charts and choropleths, the method provided deeper insights into inter-state performance dynamics.

Conclusion

This study offers an innovative method for visualizing rankings and leadership patterns in rectangular datasets. By combining a multi-level basket model with co-word network analysis and open-source R scripts, users can quickly generate interpretable, cluster-based dominance diagrams. The approach is scalable, customizable, and applicable to a variety of fields, including healthcare, education, and public policy. Future work may extend this model to dynamic visual tools and broader interdisciplinary applications.