Modeling intraindividual means and variances from ecological momentary assessment data: comparing standard computational formulas to mixed-effects location-scale model estimates.
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| Title: | Modeling intraindividual means and variances from ecological momentary assessment data: comparing standard computational formulas to mixed-effects location-scale model estimates. |
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| Authors: | Wang, Wei-Lin (AUTHOR), Yang, Chih-Hsiang (AUTHOR), Nordgren, Rachel (AUTHOR), Li, Jixin (AUTHOR), Intille, Stephen (AUTHOR), Dunton, Genevieve F. (AUTHOR), Hedeker, Donald (AUTHOR) |
| Source: | Journal of Behavioral Medicine. Apr2026, Vol. 49 Issue 2, p254-274. 21p. |
| Subjects: | Statistical models, Computer simulation, Data analysis, Computer software, Research evaluation, Decision making, Multivariate analysis, Descriptive statistics, Simulation methods in education, Longitudinal method, Research bias, Statistics, Research, Health behavior, Analysis of variance, Psychological tests, Comparative studies, Data analysis software, Confidence intervals, Affect (Psychology), Regression analysis |
| Abstract: | Traditionally, intraindividual means and variances derived from ecological momentary assessment (EMA) data have been calculated using standard computational formulas (SCF), such as subject-level means and standard deviations. However, these SCF methods assume uniform precision across subjects, disregarding variation in the number of observations, missing data issues, and the non-continuous nature of data scales. This study evaluated the predictive accuracy of the coefficients of intraindividual means and variances computed via SCF against those estimated using random effects from a Mixed-Effects Location Scale (MELS) model. A five-scenario simulation study was conducted: (1) varying numbers of observations per subject, (2) varying mean-to-variance ratios, (3) varying proportions of missing data under a missing completely at random (MCAR) assumption, (4) varying proportions of missing data under a missing at random (MAR) assumption, and (5) varying categories of ordinal scale responses. Bias and coverage of the mean levels and variability coefficients were compared across methods. In addition, a real-life dataset was used to compare the difference of means and variances between SCF and MELS approaches. Results consistently showed that the MELS model approach outperformed SCF method, yielding lower bias and higher coverage of the coefficients across all scenarios. These findings support the use of MELS for more accurate and reliable estimation of intraindividual means and variances in EMA data, highlighting its advantages for subsequent predictive modeling. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Behavioral Medicine is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Psychology and Behavioral Sciences Collection |
| FullText | Text: Availability: 0 |
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| Header | DbId: pbh DbLabel: Psychology and Behavioral Sciences Collection An: 194452505 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Modeling intraindividual means and variances from ecological momentary assessment data: comparing standard computational formulas to mixed-effects location-scale model estimates. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Wang%2C+Wei-Lin%22">Wang, Wei-Lin</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yang%2C+Chih-Hsiang%22">Yang, Chih-Hsiang</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Nordgren%2C+Rachel%22">Nordgren, Rachel</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Jixin%22">Li, Jixin</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Intille%2C+Stephen%22">Intille, Stephen</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Dunton%2C+Genevieve+F%2E%22">Dunton, Genevieve F.</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Hedeker%2C+Donald%22">Hedeker, Donald</searchLink> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Behavioral+Medicine%22">Journal of Behavioral Medicine</searchLink>. Apr2026, Vol. 49 Issue 2, p254-274. 21p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Statistical+models%22">Statistical models</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+simulation%22">Computer simulation</searchLink><br /><searchLink fieldCode="DE" term="%22Data+analysis%22">Data analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+software%22">Computer software</searchLink><br /><searchLink fieldCode="DE" term="%22Research+evaluation%22">Research evaluation</searchLink><br /><searchLink fieldCode="DE" term="%22Decision+making%22">Decision making</searchLink><br /><searchLink fieldCode="DE" term="%22Multivariate+analysis%22">Multivariate analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Descriptive+statistics%22">Descriptive statistics</searchLink><br /><searchLink fieldCode="DE" term="%22Simulation+methods+in+education%22">Simulation methods in education</searchLink><br /><searchLink fieldCode="DE" term="%22Longitudinal+method%22">Longitudinal method</searchLink><br /><searchLink fieldCode="DE" term="%22Research+bias%22">Research bias</searchLink><br /><searchLink fieldCode="DE" term="%22Statistics%22">Statistics</searchLink><br /><searchLink fieldCode="DE" term="%22Research%22">Research</searchLink><br /><searchLink fieldCode="DE" term="%22Health+behavior%22">Health behavior</searchLink><br /><searchLink fieldCode="DE" term="%22Analysis+of+variance%22">Analysis of variance</searchLink><br /><searchLink fieldCode="DE" term="%22Psychological+tests%22">Psychological tests</searchLink><br /><searchLink fieldCode="DE" term="%22Comparative+studies%22">Comparative studies</searchLink><br /><searchLink fieldCode="DE" term="%22Data+analysis+software%22">Data analysis software</searchLink><br /><searchLink fieldCode="DE" term="%22Confidence+intervals%22">Confidence intervals</searchLink><br /><searchLink fieldCode="DE" term="%22Affect+%28Psychology%29%22">Affect (Psychology)</searchLink><br /><searchLink fieldCode="DE" term="%22Regression+analysis%22">Regression analysis</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Traditionally, intraindividual means and variances derived from ecological momentary assessment (EMA) data have been calculated using standard computational formulas (SCF), such as subject-level means and standard deviations. However, these SCF methods assume uniform precision across subjects, disregarding variation in the number of observations, missing data issues, and the non-continuous nature of data scales. This study evaluated the predictive accuracy of the coefficients of intraindividual means and variances computed via SCF against those estimated using random effects from a Mixed-Effects Location Scale (MELS) model. A five-scenario simulation study was conducted: (1) varying numbers of observations per subject, (2) varying mean-to-variance ratios, (3) varying proportions of missing data under a missing completely at random (MCAR) assumption, (4) varying proportions of missing data under a missing at random (MAR) assumption, and (5) varying categories of ordinal scale responses. Bias and coverage of the mean levels and variability coefficients were compared across methods. In addition, a real-life dataset was used to compare the difference of means and variances between SCF and MELS approaches. Results consistently showed that the MELS model approach outperformed SCF method, yielding lower bias and higher coverage of the coefficients across all scenarios. These findings support the use of MELS for more accurate and reliable estimation of intraindividual means and variances in EMA data, highlighting its advantages for subsequent predictive modeling. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Behavioral Medicine is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s10865-025-00628-0 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 21 StartPage: 254 Subjects: – SubjectFull: Statistical models Type: general – SubjectFull: Computer simulation Type: general – SubjectFull: Data analysis Type: general – SubjectFull: Computer software Type: general – SubjectFull: Research evaluation Type: general – SubjectFull: Decision making Type: general – SubjectFull: Multivariate analysis Type: general – SubjectFull: Descriptive statistics Type: general – SubjectFull: Simulation methods in education Type: general – SubjectFull: Longitudinal method Type: general – SubjectFull: Research bias Type: general – SubjectFull: Statistics Type: general – SubjectFull: Research Type: general – SubjectFull: Health behavior Type: general – SubjectFull: Analysis of variance Type: general – SubjectFull: Psychological tests Type: general – SubjectFull: Comparative studies Type: general – SubjectFull: Data analysis software Type: general – SubjectFull: Confidence intervals Type: general – SubjectFull: Affect (Psychology) Type: general – SubjectFull: Regression analysis Type: general Titles: – TitleFull: Modeling intraindividual means and variances from ecological momentary assessment data: comparing standard computational formulas to mixed-effects location-scale model estimates. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Wang, Wei-Lin – PersonEntity: Name: NameFull: Yang, Chih-Hsiang – PersonEntity: Name: NameFull: Nordgren, Rachel – PersonEntity: Name: NameFull: Li, Jixin – PersonEntity: Name: NameFull: Intille, Stephen – PersonEntity: Name: NameFull: Dunton, Genevieve F. – PersonEntity: Name: NameFull: Hedeker, Donald IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 04 Text: Apr2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 01607715 Numbering: – Type: volume Value: 49 – Type: issue Value: 2 Titles: – TitleFull: Journal of Behavioral Medicine Type: main |
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