Understanding Variation in Longitudinal Data Using Latent Growth Mixture Modeling.
Saved in:
| Title: | Understanding Variation in Longitudinal Data Using Latent Growth Mixture Modeling. |
|---|---|
| Authors: | Mara, Constance A1,2 (AUTHOR) constance.mara@cchmc.org, Carle, Adam C2,3,4 (AUTHOR) |
| Source: | Journal of Pediatric Psychology. Mar2021, Vol. 46 Issue 2, p179-188. 10p. 1 Chart, 3 Graphs. |
| Subject Terms: | *Child psychology, *Longitudinal method, Child patients, Mixtures |
| Abstract: | |
| Copyright of Journal of Pediatric Psychology is the property of Oxford University Press / USA 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: | Education Research Complete |
|
Full text is not displayed to guests.
Login for full access.
|
|
| FullText | Links: – Type: pdflink Text: Availability: 1 |
|---|---|
| Header | DbId: ehh DbLabel: Education Research Complete An: 148954242 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Understanding Variation in Longitudinal Data Using Latent Growth Mixture Modeling. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Mara%2C+Constance+A%22">Mara, Constance A</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> constance.mara@cchmc.org</i><br /><searchLink fieldCode="AR" term="%22Carle%2C+Adam+C%22">Carle, Adam C</searchLink><relatesTo>2,3,4</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Pediatric+Psychology%22">Journal of Pediatric Psychology</searchLink>. Mar2021, Vol. 46 Issue 2, p179-188. 10p. 1 Chart, 3 Graphs. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Child+psychology%22">Child psychology</searchLink><br />*<searchLink fieldCode="DE" term="%22Longitudinal+method%22">Longitudinal method</searchLink><br /><searchLink fieldCode="DE" term="%22Child+patients%22">Child patients</searchLink><br /><searchLink fieldCode="DE" term="%22Mixtures%22">Mixtures</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: <bold>Objective: </bold>This article guides researchers through the process of specifying, troubleshooting, evaluating, and interpreting latent growth mixture models.<bold>Methods: </bold>Latent growth mixture models are conducted with small example dataset of N = 117 pediatric patients using Mplus software.<bold>Results: </bold>The example and data show how to select a solution, here a 3-class solution. We also present information on two methods for incorporating covariates into these models.<bold>Conclusions: </bold>Many studies in pediatric psychology seek to understand how an outcome changes over time. Mixed models or latent growth models estimate a single average trajectory estimate and an overall estimate of the individual variability, but this may mask other patterns of change shared by some participants. Unexplored variation in longitudinal data means that researchers can miss critical information about the trajectories of subgroups of individuals that could have important clinical implications about how one assess, treats, and manages subsets of individuals. Latent growth mixture modeling is a method for uncovering subgroups (or "classes") of individuals with shared trajectories that differ from the average trajectory. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Pediatric Psychology is the property of Oxford University Press / USA 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.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=ehh&AN=148954242 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1093/jpepsy/jsab010 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 10 StartPage: 179 Subjects: – SubjectFull: Child psychology Type: general – SubjectFull: Longitudinal method Type: general – SubjectFull: Child patients Type: general – SubjectFull: Mixtures Type: general Titles: – TitleFull: Understanding Variation in Longitudinal Data Using Latent Growth Mixture Modeling. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Mara, Constance A – PersonEntity: Name: NameFull: Carle, Adam C IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 03 Text: Mar2021 Type: published Y: 2021 Identifiers: – Type: issn-print Value: 01468693 Numbering: – Type: volume Value: 46 – Type: issue Value: 2 Titles: – TitleFull: Journal of Pediatric Psychology Type: main |
| ResultId | 1 |
