A latent transition model with logistic regression
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Chung, Hwan | - |
dc.contributor.author | Walls, Theodore A. | - |
dc.contributor.author | Park, Yousung | - |
dc.date.accessioned | 2021-09-09T17:10:10Z | - |
dc.date.available | 2021-09-09T17:10:10Z | - |
dc.date.created | 2021-06-10 | - |
dc.date.issued | 2007-09 | - |
dc.identifier.issn | 0033-3123 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/125726 | - |
dc.description.abstract | Latent transition models increasingly include covariates that predict prevalence of latent classes at a given time or transition rates among classes over time. In many situations, the covariate of interest may be latent. This paper describes an approach for handling both manifest and latent covariates in a latent transition model. A Bayesian approach via Markov chain Monte Carlo (MCMC) is employed in order to achieve more robust estimates. A case example illustrating the model is provided using data on academic beliefs and achievement in a low-income sample of adolescents in the United States. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | SPRINGER | - |
dc.subject | POSTERIOR DISTRIBUTIONS | - |
dc.subject | DATA AUGMENTATION | - |
dc.subject | SCHOOL | - |
dc.subject | ADJUSTMENT | - |
dc.subject | IDENTIFICATION | - |
dc.subject | PERFORMANCE | - |
dc.subject | COMPONENTS | - |
dc.subject | VARIABLES | - |
dc.subject | STANDARD | - |
dc.subject | NUMBER | - |
dc.title | A latent transition model with logistic regression | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Park, Yousung | - |
dc.identifier.doi | 10.1007/s11336-005-1382-y | - |
dc.identifier.scopusid | 2-s2.0-36448975260 | - |
dc.identifier.wosid | 000251153500008 | - |
dc.identifier.bibliographicCitation | PSYCHOMETRIKA, v.72, no.3, pp.413 - 435 | - |
dc.relation.isPartOf | PSYCHOMETRIKA | - |
dc.citation.title | PSYCHOMETRIKA | - |
dc.citation.volume | 72 | - |
dc.citation.number | 3 | - |
dc.citation.startPage | 413 | - |
dc.citation.endPage | 435 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Mathematics | - |
dc.relation.journalResearchArea | Mathematical Methods In Social Sciences | - |
dc.relation.journalResearchArea | Psychology | - |
dc.relation.journalWebOfScienceCategory | Mathematics, Interdisciplinary Applications | - |
dc.relation.journalWebOfScienceCategory | Social Sciences, Mathematical Methods | - |
dc.relation.journalWebOfScienceCategory | Psychology, Mathematical | - |
dc.subject.keywordPlus | POSTERIOR DISTRIBUTIONS | - |
dc.subject.keywordPlus | DATA AUGMENTATION | - |
dc.subject.keywordPlus | SCHOOL | - |
dc.subject.keywordPlus | ADJUSTMENT | - |
dc.subject.keywordPlus | IDENTIFICATION | - |
dc.subject.keywordPlus | PERFORMANCE | - |
dc.subject.keywordPlus | COMPONENTS | - |
dc.subject.keywordPlus | VARIABLES | - |
dc.subject.keywordPlus | STANDARD | - |
dc.subject.keywordPlus | NUMBER | - |
dc.subject.keywordAuthor | latent transition | - |
dc.subject.keywordAuthor | logistic regression | - |
dc.subject.keywordAuthor | MCMC | - |
dc.subject.keywordAuthor | academic achievement | - |
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