Dynamic Multilevel Models
Dynamic Multilevel Models is a Bayesian multilevel modeling framework that extends static hierarchical structures by incorporating time-varying parameters and latent processes to capture temporal dynamics within and across levels of nested data.
72
Publications
5.3K
Citations
199
Authors
124
Institutions
Publications per year
2017–2026
29
Variants
Dynamic Multilevel Modeling
Parents
72
Ellen L. Hamaker, Tihomir Asparouhov, Annette Brose et al. · Multivariate Behavioral Research · 2018 · 540 citations · Full text
Ellen L. Hamaker, Marieke Wichers · Current Directions in Psychological Science · 2017 · 495 citations
Mårten Schultzberg, Bengt Muthén · Structural Equation Modeling A Multidisciplinary Journal · 2017 · 294 citations
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124

Delft University of Technology
Delft, The Netherlands
4 h-index
13 publications
533 citations
WHU – Otto Beisheim School of Management
Vallendar, Germany
3 h-index
3 publications
171 citations
University of Münster
Münster, Germany
3 h-index
3 publications
171 citations
Columbia University
New York, United States
3 h-index
3 publications
182 citations
Chinese University of Hong Kong
Hong Kong, Hong Kong
2 h-index
4 publications
164 citations

Federal Reserve Bank of New York
New York, United States
2 h-index
4 publications
326 citations
University of California, Davis
Davis, United States
2 h-index
3 publications
202 citations
University of Southern California
Los Angeles, United States
2 h-index
3 publications
214 citations
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3 publications · 101 citations
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3 publications · 142 citations
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2 publications · 85 citations
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ORM · 2 publications · 60 citations
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SEM · 2 publications · 315 citations
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