Bayesian Item Calibration

Bayesian Item Calibration is a statistical method for estimating item parameters in item response theory that incorporates prior distributions to update parameter estimates through Bayesian inference.

16

Publications

768

Citations

27

Authors

14

Institutions

Publications per year

2017–2026

3

Authors

27

  1. WJ
    Wim J. van der Linden

    University of Twente, Netherlands

    4 publications · 350 citations

    4

    h-index

  2. MJ
    Matthew Johnson

    University of Liverpool, United Kingdom

    4 publications · 115 citations

    4

    h-index

  3. SS
    Sandip Sinharay

    Educational Testing Service, United States

    4 publications · 115 citations

    4

    h-index

  4. DM
    David M. Williamson

    University of Cambridge, United Kingdom

    2 publications · 67 citations

    2

    h-index

  5. DX
    Dehui Xing

    University of Massachusetts Amherst, United States

    1 publications · 51 citations

    1

    h-index

1–5 of 27

Venues

  1. Applied Psychological Measurement

    7 publications · 273 citations

    7

    h-index

  2. 2

    h-index

  3. Psychometrika

    2 publications · 210 citations

    2

    h-index

  4. ETS Research Report Series

    2 publications · 36 citations

    2

    h-index

  5. Educational Measurement Issues and Practice

    EM · 1 publications · 45 citations

    1

    h-index

1–5