Multilevel Modeling is a statistical technique that belongs to the class of regression models and is distinguished by its explicit accommodation of hierarchical or nested data structures through the inclusion of random effects at multiple levels.
3K
Publications
448.6K
Citations
5.1K
Authors
1.7K
Institutions
Publications per year
2017–2026
614
Variants
Mixed-effects ModelingMultilevel Analysis
Parents
Publications
3K
1–5 of 3K
Authors
5.1K
- HG
44
h-index
- DH
23
h-index
- WV
20
h-index
- SW
20
h-index
- GL
19
h-index
1–5 of 5.1K
Institutions
1.7K
University of Bristol
Bristol, United Kingdom
41 h-index
118 publications
9.8K citations
KU Leuven
Leuven, Belgium
35 h-index
177 publications
11.7K citations
University of California, Los Angeles
Los Angeles, United States
34 h-index
83 publications
12.2K citations
University of North Carolina at Chapel Hill
Chapel Hill, United States
31 h-index
74 publications
29.2K citations
University of London
London, United Kingdom
30 h-index
52 publications
6.2K citations
University of Michigan
Ann Arbor, United States
30 h-index
60 publications
7.7K citations
Texas A&M University
College Station, United States
28 h-index
67 publications
9.5K citations
Utrecht University
Utrecht, The Netherlands
27 h-index
63 publications
17.8K citations
1–8 of 1.7K
Venues
- Psychological Methods
59 publications · 21.8K citations
43
h-index
- Structural Equation Modeling A Multidisciplinary Journal
SEM · 84 publications · 7.1K citations
42
h-index
- Journal of the American Statistical Association
64 publications · 33.3K citations
41
h-index
- Multivariate Behavioral Research
MBR · 83 publications · 10.4K citations
38
h-index
- Biometrics
57 publications · 9.7K citations
38
h-index
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