Blocking Factor Analysis

Blocking Factor Analysis is a statistical method that partitions variability in experimental data by isolating the effects of nuisance variables through structured grouping, thereby enhancing treatment effect estimation precision within randomized block designs.

34

Publications

2.7K

Citations

103

Authors

49

Institutions

Publications per year

2017–2026

0

Authors

103

  1. AG
    Alan Grafen

    University of Oxford, United Kingdom

    2 publications · 246 citations

    2

    h-index

  2. SA
    Sidney Addelman

    University at Buffalo, State University of New York, United States

    2 publications · 856 citations

    2

    h-index

  3. TG
    Tetsuya Gatanaga

    University of California, Irvine, United States

    2 publications · 185 citations

    2

    h-index

  4. RL
    Rigdon Lentz

    University of California, Irvine, United States

    2 publications · 185 citations

    2

    h-index

  5. JM
    John M. Tomich

    Kansas State University, United States

    2 publications · 185 citations

    2

    h-index

1–5 of 103

Venues

  1. Technometrics

    3 publications · 945 citations

    3

    h-index

  2. PubMed

    Repository · 2 publications · 42 citations

    2

    h-index

  3. Ecology

    2 publications · 246 citations

    2

    h-index

  4. Headache The Journal of Head and Face Pain

    1 publications · 341 citations

    1

    h-index

  5. Contemporary Clinical Trials

    1 publications · 18 citations

    1

    h-index

1–5