Mixed Model Variance Decomposition
Mixed Model Variance Decomposition is a statistical method that decomposes error variance in hierarchical or longitudinal data by distinguishing between fixed and random effects to quantify the proportion of variability attributable to each level within a mixed-effects modeling framework.
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Publications
1.4K
Citations
11
Authors
10
Institutions
Publications per year
2017–2026
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Friedrich Schiller University Jena
Jena, Germany
2 h-index
4 publications
768 citations

UNSW Sydney
Sydney, Australia
2 h-index
2 publications
384 citations
University of California, Berkeley
Berkeley, United States
2 h-index
2 publications
514 citations
Harvard University
Cambridge, United States
2 h-index
2 publications
514 citations

University of Edinburgh
Edinburgh, United Kingdom
2 h-index
2 publications
384 citations

Vanderbilt University
Nashville, United States
1 h-index
2 publications
62 citations
Cancer Research And Biostatistics
Seattle, United States
1 h-index
1 publications
422 citations

University of North Carolina at Greensboro
Greensboro, United States
1 h-index
1 publications
20 citations
1–8 of 10
2 publications · 514 citations
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2 publications · 33 citations
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Repository · 1 publications · 25 citations
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1 publications · 391 citations
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Repository · 1 publications · 37 citations
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