On the Use of Mixed Markov Models for Intensive Longitudinal Data.
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| Title: | On the Use of Mixed Markov Models for Intensive Longitudinal Data. |
|---|---|
| Authors: | de Haan-Rietdijk, S.1, Kuppens, P.2, Bergeman, C. S.3, Sheeber, L. B.4, Allen, N. B.5, Hamaker, E. L.1,2 |
| Source: | Multivariate Behavioral Research. Nov/Dec2017, Vol. 52 Issue 6, p747-767. 21p. |
| Subject Terms: | Markov processes, Time series analysis, Latent class analysis (Statistics) |
| Abstract: | Markov modeling presents an attractive analytical framework for researchers who are interested in state-switching processes occurring within a person, dyad, family, group, or other system over time. Markov modeling is flexible and can be used with various types of data to study observed or latent state-switching processes, and can include subject-specific random effects to account for heterogeneity. We focus on the application of mixed Markov models to intensive longitudinal data sets in psychology, which are becoming ever more common and provide a rich description of each subject’s process. We examine how specifications of a Markov model change when continuous random effect distributions are included, and how mixed Markov models can be used in the intensive longitudinal research context. Advantages of Bayesian estimation are discussed and the approach is illustrated by two empirical applications. [ABSTRACT FROM PUBLISHER] |
| Copyright of Multivariate Behavioral Research is the property of Taylor & Francis Ltd 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 |
| FullText | Text: Availability: 0 |
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| Header | DbId: ehh DbLabel: Education Research Complete An: 126328181 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1080/00273171.2017.1370364 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 21 StartPage: 747 Subjects: – SubjectFull: Markov processes Type: general – SubjectFull: Time series analysis Type: general – SubjectFull: Latent class analysis (Statistics) Type: general Titles: – TitleFull: On the Use of Mixed Markov Models for Intensive Longitudinal Data. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: de Haan-Rietdijk, S. – PersonEntity: Name: NameFull: Kuppens, P. – PersonEntity: Name: NameFull: Bergeman, C. S. – PersonEntity: Name: NameFull: Sheeber, L. B. – PersonEntity: Name: NameFull: Allen, N. B. – PersonEntity: Name: NameFull: Hamaker, E. L. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 11 Text: Nov/Dec2017 Type: published Y: 2017 Identifiers: – Type: issn-print Value: 00273171 Numbering: – Type: volume Value: 52 – Type: issue Value: 6 Titles: – TitleFull: Multivariate Behavioral Research Type: main |
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