Multilevel Generalizability Analysis

Multilevel Generalizability Analysis is a methodological framework in D-Study Design that extends generalizability theory by simultaneously evaluating measurement consistency across multiple nested or crossed facets, thereby enabling the assessment of score dependability at differing levels of a hierarchical data structure.

9

Publications

288

Citations

26

Authors

17

Institutions

Publications per year

2017–2026

2

Authors

26

  1. CM
    Christine M. Lee

    University of Washington, United States

    1 publications · 63 citations

    1

    h-index

  2. DC
    David C. Atkins

    University of Washington, United States

    1 publications · 63 citations

    1

    h-index

  3. HS
    Hua Shu

    Beijing Normal University, China

    1 publications · 18 citations

    1

    h-index

  4. HG
    Harvey Goldstein

    University of Bristol, United Kingdom

    1 publications · 17 citations

    1

    h-index

  5. ME
    Mary E. Larimer

    University of Washington, United States

    1 publications · 63 citations

    1

    h-index

1–5 of 26

Venues

  1. Psychological Assessment

    1 publications · 63 citations

    1

    h-index

  2. School Effectiveness and School Improvement

    1 publications · 59 citations

    1

    h-index

  3. Social Problems

    1 publications · 24 citations

    1

    h-index

  4. Studies In Educational Evaluation

    1 publications · 21 citations

    1

    h-index

  5. Assessing Writing

    1 publications · 50 citations

    1

    h-index

1–5