Logistic Regression Approaches

Logistic Regression Approaches is a class of differential item functioning detection methods that model the probability of an item response as a logistic function of a matching variable for latent ability, group membership, and their interaction, and identify DIF by testing whether the regression coefficients for group and interaction terms differ significantly from zero.

1.7K

Publications

165.1K

Citations

3.7K

Authors

1.4K

Institutions

Publications per year

2017–2026

356

Authors

3.7K

  1. DW
    David W. Hosmer

    University of Massachusetts Amherst, United States

    13 publications · 3.8K citations

    12

    h-index

  2. EW
    Ewout W. Steyerberg

    Erasmus MC, Netherlands

    10 publications · 2.4K citations

    10

    h-index

  3. SL
    Stanley Lemeshow

    The Ohio State University, United States

    10 publications · 2.7K citations

    10

    h-index

  4. SJ
    Shelby J. Haberman

    Educational Testing Service, United States

    8 publications · 251 citations

    8

    h-index

  5. AD
    Alfred DeMaris

    Bowling Green State University, United States

    8 publications · 1.1K citations

    8

    h-index

1–5 of 3.7K

Venues

  1. Journal of the American Statistical Association

    54 publications · 9.4K citations

    37

    h-index

  2. Statistics in Medicine

    71 publications · 7.3K citations

    37

    h-index

  3. Applied Psychological Measurement

    53 publications · 4.3K citations

    33

    h-index

  4. Biometrika

    44 publications · 4K citations

    30

    h-index

  5. Educational and Psychological Measurement

    41 publications · 1.6K citations

    23

    h-index

1–5