Mixture Item Response Models

Mixture Item Response Model is a latent trait modeling framework within Bayesian item response theory that extends standard item response models by incorporating discrete subpopulations or latent classes, thereby allowing for heterogeneity in item response behavior through class-specific item parameters or trait distributions.

98

Publications

4.5K

Citations

191

Authors

123

Institutions

Publications per year

2017–2026

32

Authors

191

  1. AS
    Allan S. Cohen

    University of Georgia, United States

    12 publications · 821 citations

    12

    h-index

  2. SC
    Sun‐Joo Cho

    Vanderbilt University, United States

    10 publications · 450 citations

    10

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  3. SK
    Seock‐Ho Kim

    University of Georgia, United States

    7 publications · 369 citations

    7

    h-index

  4. MV
    Matthias von Davier

    Educational Testing Service, United States

    4 publications · 184 citations

    4

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  5. PD
    Paul De Boeck

    KU Leuven, Belgium

    4 publications · 160 citations

    4

    h-index

1–5 of 191

Venues

  1. Applied Psychological Measurement

    12 publications · 667 citations

    12

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  2. Educational and Psychological Measurement

    11 publications · 267 citations

    11

    h-index

  3. Journal of Educational Measurement

    5 publications · 368 citations

    5

    h-index

  4. 5

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  5. 5

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1–5