Bayesian multilevel compositional data analysis: Introduction, evaluation, and application. Article Swipe
Flora Le
,
Ty Stanford
,
Dorothea Dumuid
,
Joshua F. Wiley
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1037/met0000750
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1037/met0000750
Multilevel compositional data are data that are repeatedly measured or clustered within groups, and are nonnegative and sum to a constant value. These data arise in various settings, such as intensive, longitudinal studies using ecological momentary assessments and wearable devices. Examples include 24-hr sleep-wake behaviors, sleep architecture, and macronutrients. This article presents an innovative method for analyzing multilevel compositional data using Bayesian inference. We describe the theoretical details of the data and the models, and outline the steps necessary to implement this method. We introduce the R package multilevelcoda to facilitate the application of this method and illustrate using a real data example. An extensive parameter recovery simulation study verified the robust performance of the method. Across all conditions investigated in the simulation study, the fitted models had minimal convergence issues (convergence rate > 99%) and achieved excellent quality parameter estimates and inference, with an average bias of 0.00 (range = -0.09 to 0.05) and coverage of 0.95 (range = 0.93 to 0.97). We conclude the article with recommendations on the use of the Bayesian multilevel compositional data analysis. We hope to promote wider application of this method to gain novel and robust answers to scientific questions. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
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- Landing Page
- https://doi.org/10.1037/met0000750
- OA Status
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- Cited By
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https://openalex.org/W4409455677Canonical identifier for this work in OpenAlex
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https://doi.org/10.1037/met0000750Digital Object Identifier
- Title
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Bayesian multilevel compositional data analysis: Introduction, evaluation, and application.Work title
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articleOpenAlex work type
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enPrimary language
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2025Year of publication
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2025-04-14Full publication date if available
- Authors
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Flora Le, Ty Stanford, Dorothea Dumuid, Joshua F. WileyList of authors in order
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https://doi.org/10.1037/met0000750Publisher landing page
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://doi.org/10.1037/met0000750Direct OA link when available
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Bayesian probability, Multilevel model, Statistics, Statistical analysis, Econometrics, Computer science, MathematicsTop concepts (fields/topics) attached by OpenAlex
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8Total citation count in OpenAlex
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2025: 8Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.implement | 80 |
| abstract_inverted_index.introduce | 84 |
| abstract_inverted_index.momentary | 35 |
| abstract_inverted_index.necessary | 78 |
| abstract_inverted_index.parameter | 105, 139 |
| abstract_inverted_index.settings, | 27 |
| abstract_inverted_index.Multilevel | 0 |
| abstract_inverted_index.behaviors, | 44 |
| abstract_inverted_index.conditions | 118 |
| abstract_inverted_index.ecological | 34 |
| abstract_inverted_index.facilitate | 90 |
| abstract_inverted_index.illustrate | 97 |
| abstract_inverted_index.inference, | 142 |
| abstract_inverted_index.inference. | 62 |
| abstract_inverted_index.innovative | 53 |
| abstract_inverted_index.intensive, | 30 |
| abstract_inverted_index.multilevel | 57, 175 |
| abstract_inverted_index.questions. | 196 |
| abstract_inverted_index.repeatedly | 7 |
| abstract_inverted_index.reserved). | 205 |
| abstract_inverted_index.scientific | 195 |
| abstract_inverted_index.simulation | 107, 122 |
| abstract_inverted_index.sleep-wake | 43 |
| abstract_inverted_index.application | 92, 184 |
| abstract_inverted_index.assessments | 36 |
| abstract_inverted_index.convergence | 129 |
| abstract_inverted_index.nonnegative | 15 |
| abstract_inverted_index.performance | 112 |
| abstract_inverted_index.theoretical | 66 |
| abstract_inverted_index.(convergence | 131 |
| abstract_inverted_index.investigated | 119 |
| abstract_inverted_index.longitudinal | 31 |
| abstract_inverted_index.architecture, | 46 |
| abstract_inverted_index.compositional | 1, 58, 176 |
| abstract_inverted_index.multilevelcoda | 88 |
| abstract_inverted_index.macronutrients. | 48 |
| abstract_inverted_index.recommendations | 168 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 98 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/13 |
| sustainable_development_goals[0].score | 0.47999998927116394 |
| sustainable_development_goals[0].display_name | Climate action |
| citation_normalized_percentile.value | 0.9962179 |
| citation_normalized_percentile.is_in_top_1_percent | True |
| citation_normalized_percentile.is_in_top_10_percent | True |