Generalizability Models

Generalizability Models is a class of statistical frameworks that extend generalizability theory by quantifying the dependability of measurements across diverse facets of observation, distinguishing itself through the explicit modeling of variance components associated with admissible observational universes to assess the robustness of inferences under varying conditions.

340

Publications

30.6K

Citations

1.4K

Authors

494

Institutions

Publications per year

2017–2026

122

Authors

1.4K

  1. GA
    George A. Marcoulides

    California State University, Fullerton, United States

    8 publications · 252 citations

    8

    h-index

  2. RL
    Robert L. Brennan

    University of Iowa, United States

    7 publications · 613 citations

    7

    h-index

  3. BL
    Brian Lakey

    Wayne State University, United States

    5 publications · 298 citations

    5

    h-index

  4. GL
    Guemin Lee

    Yonsei University, South Korea

    4 publications · 96 citations

    4

    h-index

  5. RL
    Robert L. Linn

    University of Illinois Urbana-Champaign, United States

    4 publications · 449 citations

    4

    h-index

1–5 of 1.4K

Venues

  1. Journal of Educational Measurement

    9 publications · 333 citations

    9

    h-index

  2. Academic Medicine

    8 publications · 235 citations

    8

    h-index

  3. arXiv (Cornell University)

    Repository · 8 publications · 208 citations

    8

    h-index

  4. Teaching and Learning in Medicine

    7 publications · 204 citations

    7

    h-index

  5. PubMed

    Repository · 7 publications · 308 citations

    7

    h-index

1–5