Cited by 419Statistical Methods and Bayesian InferenceReliability and Agreement in MeasurementMeta-analysis and systematic reviews

Quantifying explained variance in multilevel models: an integrative framework for defining R-squared measures

Rights, Jason D., Sterba, Sonya K.·2019·Psychological Methods

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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Crossref · 10.1037/met0000184

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Authors
Rights, J. D., & Sterba, S. K.
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 · 2018: 4
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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