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Quantifying explained variance in multilevel models: an integrative framework for defining R-squared measures
Abstract
that computes all measures in our framework to help researchers in considering effect size and conveying practical significance. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
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“Given the issues quantifying explained variance in multilevel models (86, 87)”
From Predicting Symptoms of Depression and Anxiety Using Smartphone and Wearable Data (2021) · cites this paper as [86]
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Metadata
- Authors
- Rights, J. D., & Sterba, S. K.
- ORCID
- Rights, Jason D.
- Venue
- Psychological Methods
- Published
- 2019-06
- Volume
- 24
- Issue
- 3
- Pages
- 309-338
- ISSN
- 1082-989X (ISSN-L)
- 1939-1463
- OpenAlex
- W2823644790
- Cited by
- 413
- References
- 26
- Citations by year
- 2026: 54 · 2025: 62 · 2024: 62 · 2023: 74 · 2022: 55 · 2021: 44 · 2020: 40 · 2019: 18
- FWCI
- 22.20
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Psychological Methods (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Reliability and Agreement in Measurement
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Keywords
- explained variance, multilevel models, effect size, practical significance, variance partitioning
- MeSH terms
- Data Interpretation, Statistical, Educational Measurement, Humans, Psychology, Students, Models, Statistical, Multilevel Analysis, Biostatistics
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