Categorical Variable Representation

Categorical Variable Representation is a methodological framework within multivariate data visualization that enables the encoding and differentiation of discrete class labels in scatterplot matrices through visual attributes such as color, shape, or size to facilitate the interpretation of relationships across categorical dimensions.

135

Publications

12.5K

Citations

376

Authors

243

Institutions

Publications per year

2017–2026

25

Authors

376

  1. MF
    Michael Friendly

    York University, Canada

    7 publications · 258 citations

    7

    h-index

  2. AA
    Alan Agresti

    University of Florida, United States

    3 publications · 245 citations

    3

    h-index

  3. DL
    Donna L. Hoffman

    Vanderbilt University, United States

    2 publications · 765 citations

    2

    h-index

  4. MG
    Michael Greenacre

    Universitat Pompeu Fabra, Spain

    2 publications · 81 citations

    2

    h-index

  5. FW
    Forrest W. Young

    University of North Carolina at Chapel Hill, United States

    2 publications · 432 citations

    2

    h-index

1–5 of 376

Venues

  1. Technometrics

    8 publications · 3.6K citations

    8

    h-index

  2. Biometrics

    4 publications · 288 citations

    4

    h-index

  3. The American Statistician

    3 publications · 108 citations

    3

    h-index

  4. Computational Statistics & Data Analysis

    CSDA · 3 publications · 45 citations

    3

    h-index

  5. Journal of Computational and Graphical Statistics

    JCGS · 3 publications · 270 citations

    3

    h-index

1–5