Bayesian Item Response Models

Bayesian Item Response Models is a statistical modeling framework within latent trait theory that employs Bayesian inference to estimate item and ability parameters, distinguishing itself through the incorporation of prior distributions to represent uncertainty and facilitate more robust parameter estimation in the presence of limited data.

182

Publications

11.9K

Citations

332

Authors

191

Institutions

Publications per year

2017–2026

54

Authors

332

  1. JF
    Jean‐Paul Fox

    University of Twente, Netherlands

    8 publications · 392 citations

    8

    h-index

  2. HW
    Howard Wainer

    Educational Testing Service, United States

    5 publications · 404 citations

    6

    h-index

  3. RJ
    Robert J. Mislevy

    Educational Testing Service, United States

    5 publications · 714 citations

    5

    h-index

  4. CA
    Cees A. W. Glas

    University of Twente, Netherlands

    5 publications · 282 citations

    5

    h-index

  5. SS
    Sandip Sinharay

    Educational Testing Service, United States

    5 publications · 148 citations

    5

    h-index

1–5 of 332

Venues

  1. Applied Psychological Measurement

    29 publications · 1.5K citations

    19

    h-index

  2. Educational and Psychological Measurement

    16 publications · 468 citations

    12

    h-index

  3. Journal of Educational and Behavioral Statistics

    12 publications · 804 citations

    12

    h-index

  4. Psychometrika

    11 publications · 728 citations

    11

    h-index

  5. Journal of Educational Measurement

    10 publications · 539 citations

    10

    h-index

1–5