Random Intercepts Models

Random Intercepts Model is a type of multilevel model that belongs to the class of mixed-effects models and is distinguished by allowing the intercept to vary randomly across groups, thereby accounting for heterogeneity in baseline levels while assuming common fixed slopes for predictors.

256

Publications

19K

Citations

1.3K

Authors

663

Institutions

Publications per year

2017–2026

144

Variants

Random Intercept Models

Authors

1.3K

  1. JN
    John Neuhaus

    University of California, San Francisco, United States

    3 publications · 656 citations

    3

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  2. OL
    Oliver Lüdtke

    Leibniz Institute for Science and Mathematics Education, Germany

    2 publications · 303 citations

    3

    h-index

  3. TA
    Terri A. deRoon‐Cassini

    Medical College of Wisconsin, United States

    2 publications · 59 citations

    2

    h-index

  4. SB
    Samir Bhatt

    Imperial College London, United Kingdom

    2 publications · 75 citations

    2

    h-index

  5. MJ
    Martin J. Donnelly

    Liverpool School of Tropical Medicine, United Kingdom

    2 publications · 75 citations

    2

    h-index

1–5 of 1.3K

Venues

  1. PLoS ONE

    6 publications · 134 citations

    7

    h-index

  2. Statistics in Medicine

    5 publications · 203 citations

    5

    h-index

  3. Frontiers in Psychology

    5 publications · 88 citations

    5

    h-index

  4. PEDIATRICS

    4 publications · 405 citations

    4

    h-index

  5. 4

    h-index

1–5