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
1.7K
Choice Reviews Online · 1990 · 35.7K citations
Interaction terms in logit and probit models
Chunrong Ai, Edward C. Norton · Economics Letters · 2003 · 5.8K citations
Applied Logistic Regression Analysis
Technometrics · 1996 · 3.6K citations
1–5 of 1.7K
3.7K
12
h-index
10
h-index
10
h-index
8
h-index
8
h-index
1–5 of 3.7K
1.4K
Educational Testing Service
Princeton, United States
23 h-index
54 publications
2.4K citations
Harvard University
Cambridge, United States
19 h-index
42 publications
3.4K citations

University of Massachusetts Amherst
Amherst Center, United States
19 h-index
40 publications
9.4K citations
University of Washington
Seattle, United States
17 h-index
32 publications
3.7K citations
KU Leuven
Leuven, Belgium
16 h-index
31 publications
2.4K citations
University of Illinois Urbana-Champaign
Urbana, United States
16 h-index
29 publications
1.4K citations
Stanford University
Stanford, United States
15 h-index
18 publications
3K citations
University of California, Los Angeles
Los Angeles, United States
15 h-index
30 publications
1.6K citations
1–8 of 1.4K
54 publications · 9.4K citations
37
h-index
71 publications · 7.3K citations
37
h-index
53 publications · 4.3K citations
33
h-index
44 publications · 4K citations
30
h-index
41 publications · 1.6K citations
23
h-index
1–5