Nominal Association Tests

Nominal Association Tests is a statistical hypothesis testing framework used in categorical data analysis to assess the independence or association between two or more nominal variables by comparing observed contingency table frequencies to expected frequencies under the null hypothesis of no association.

28

Publications

1.5K

Citations

527

Authors

252

Institutions

Publications per year

2017–2026

3

Authors

527

  1. JL
    James L. Kennedy

    Centre for Addiction and Mental Health, Canada

    3 publications · 112 citations

    3

    h-index

  2. CL
    Christoph Lange

    Harvard University, United States

    2 publications · 265 citations

    2

    h-index

  3. NS
    Neonila Szeszenia‐Dąbrowska

    Nofer Institute of Occupational Medicine, Poland

    2 publications · 129 citations

    2

    h-index

  4. JH
    Johannes Hebebrand

    University of Duisburg-Essen, Germany

    2 publications · 52 citations

    2

    h-index

  5. AH
    Anke Hinney

    University of Duisburg-Essen, Germany

    2 publications · 52 citations

    2

    h-index

1–5 of 527

Venues

  1. Genes Brain & Behavior

    2 publications · 44 citations

    2

    h-index

  2. The American Journal of Human Genetics

    AJHG · 2 publications · 265 citations

    2

    h-index

  3. PLoS ONE

    2 publications · 109 citations

    2

    h-index

  4. Psychological Bulletin

    1 publications · 81 citations

    1

    h-index

  5. PLoS Genetics

    1 publications · 41 citations

    1

    h-index

1–5