Modeling heterogeneity of the level-1 error covariance matrix in multilevel models for single-case data
Author(s) / Creator(s)
Baek, Eunkyeng
Ferron, John J. M.
Abstract / Description
Previous research applying multilevel models to single-case data has made a critical assumption that the level-1 error covariance matrix is constant across all participants. However, the level-1 error covariance matrix may differ across participants and ignoring these differences can have an impact on estimation and inferences. Despite the importance of this issue, the effects of modeling between-case variation in the level-1 error structure had not yet been systematically studied. The purpose of this simulation study was to identify the consequences of modeling and not modeling between-case variation in the level-1 error covariance matrices in single-case studies, using Bayesian estimation. The results of this study found that variance estimation was more sensitive to the method used to model the level-1 error structure than fixed effect estimation, with fixed effects only being impacted in the most extreme heterogeneity conditions. Implications for applied single-case researchers and methodologists are discussed.
Keyword(s)
single-case multilevel modeling Bayesian estimation misspecifying level-1 error structure heterogeneityPersistent Identifier
Date of first publication
2020-06-18
Journal title
Methodology
Volume
16
Issue
2
Page numbers
166–185
Publisher
PsychOpen GOLD
Publication status
publishedVersion
Review status
peerReviewed
Is version of
Citation
Baek, E., & Ferron, J. J. M. (2020). Modeling heterogeneity of the level-1 error covariance matrix in multilevel models for single-case data. Methodology, 16(2), 166-185. https://doi.org/10.5964/meth.2817
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meth.v16i2.2817.pdfAdobe PDF - 656.67KBMD5: bae775a35f5a08ce3a78b22caf6a886a
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There are no other versions of this object.
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Author(s) / Creator(s)Baek, Eunkyeng
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Author(s) / Creator(s)Ferron, John J. M.
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PsychArchives acquisition timestamp2022-04-14T11:24:39Z
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Made available on2022-04-14T11:24:39Z
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Date of first publication2020-06-18
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Abstract / DescriptionPrevious research applying multilevel models to single-case data has made a critical assumption that the level-1 error covariance matrix is constant across all participants. However, the level-1 error covariance matrix may differ across participants and ignoring these differences can have an impact on estimation and inferences. Despite the importance of this issue, the effects of modeling between-case variation in the level-1 error structure had not yet been systematically studied. The purpose of this simulation study was to identify the consequences of modeling and not modeling between-case variation in the level-1 error covariance matrices in single-case studies, using Bayesian estimation. The results of this study found that variance estimation was more sensitive to the method used to model the level-1 error structure than fixed effect estimation, with fixed effects only being impacted in the most extreme heterogeneity conditions. Implications for applied single-case researchers and methodologists are discussed.en_US
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Publication statuspublishedVersion
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Review statuspeerReviewed
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CitationBaek, E., & Ferron, J. J. M. (2020). Modeling heterogeneity of the level-1 error covariance matrix in multilevel models for single-case data. Methodology, 16(2), 166-185. https://doi.org/10.5964/meth.2817en_US
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ISSN1614-2241
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Persistent Identifierhttps://hdl.handle.net/20.500.12034/5691
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Persistent Identifierhttps://doi.org/10.23668/psycharchives.6295
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Language of contenteng
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PublisherPsychOpen GOLD
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Is version ofhttps://doi.org/10.5964/meth.2817
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Is related tohttps://doi.org/10.23668/psycharchives.2893
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Keyword(s)single-caseen_US
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Keyword(s)multilevel modelingen_US
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Keyword(s)Bayesian estimationen_US
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Keyword(s)misspecifying level-1 error structureen_US
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Keyword(s)heterogeneityen_US
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Dewey Decimal Classification number(s)150
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TitleModeling heterogeneity of the level-1 error covariance matrix in multilevel models for single-case dataen_US
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DRO typearticle
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Issue2
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Journal titleMethodology
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Page numbers166–185
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Volume16
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Visible tag(s)Version of Recorden_US